With so many companies grappling with it, technical debt has become a critical subject for almost every developer, architect, and anyone else involved in software development. A report published by OutSystems revealed that 69% of IT leaders found that technical debt fundamentally limits their ability to innovate. Additionally, 61% of tech leaders reported that it negatively affects their organization’s performance, and 64% believe that technical debt will continue to have a substantial impact in the future. The report further explained that, in some cases, organizations may benefit more from investing in decreasing technical debt rather than innovation.
But what do you do when 80% of each dollar spent on your application budget goes to keeping the lights on? Certainly, maintenance, firefighting, and root cause analysis don’t usually fall into the “innovation” category. What kinds of technical debt contribute to a draw on company resources, and what can be done about it?
An introduction to technical debt
Ward Cunningham, one of the Manifesto for Agile Software Development coauthors and the developer of the first wiki, once suggested that some issues in code resemble financial debt. The analogy is that it’s okay to borrow against your future if you’re ready and willing to pay that debt when it’s due.
The term “technical debt” sounds more like a financial term than a programming theory for a reason. While developing a financial application in the Smalltalk-80 language, Cunningham used the financial analogy to justify refactoring to his boss.
Some people continue to dispute the precise connotation Cunningham intended to convey: Is it okay to incur technical debt or best not to?
Specific critical tasks, such as refactoring and making improvements to the code and its architecture, are delayed to meet deliverables and deadlines. However, these coding tasks must be completed eventually, resulting in cumulative technical debt. Before development teams know it, they’re overworked and burned out from trying to pay off all the technical debt they’ve incurred over time.
The true cost of technical debt
Have you ever truly contemplated the cost of technical debt? By now, you likely realize that your technical debt is costing your team more over the long term than the perceived benefits of delaying paying off the debt. However, since technical debt isn’t as tangible as monetary debt, you might wonder how and where one begins to estimate its cost.
To understand why technical debt is so easy to ignore, one must understand why people pay tens of thousands of dollars in interest throughout their lives rather than saving and paying cash.
CNBC Select examined the amount of interest the average American pays on loans taken out throughout their lifetime. Its report found that when including a student loan, a used car payment, a single credit card balance, and a mortgage on a median-priced home, they paid on average $164,000 over their lifetime.
With so much hard-earned money going towards paying off interest, one would think that more people would resist taking financial “shortcuts.” But similarly to consumers who accept paying thousands of dollars in interest as a reasonable shortcut, IT professionals often disregard technical debt for an end goal that seems like a good idea — until they’re forced to repay it in interest.
For many borrowers, being able to possess that item now is more important than what they could gain over time by saving and paying cash. This is analogous to the way many IT professionals feel when they focus on getting software into production sooner without fully considering the technical debt accruing. Rather than methodically resolving all past issues, they move forward to complete a new goal, leaving problems in their wake that, sooner or later, must be addressed.
An application’s budget for maintaining accumulated technical debt could be upwards of 87%. That leaves only 13% of its budget going towards innovation. That makes keeping the lights on even harder.
The vicious cycle of technical debt begins when companies prioritize the rapid delivery of new features or products over code quality and long-term system health. Under pressure to stay competitive, teams may cut corners, leaving behind unresolved issues that accumulate over time. This accumulation of tech debt can quickly spiral out of control, leading to a situation where an increasing amount of resources is spent on fixing bugs, patching security vulnerabilities, and addressing system failures, rather than driving business value through innovation.
As technical debt accumulates, it becomes increasingly difficult to repay—each new feature or update risks introducing additional debt, further degrading system quality and efficiency. This cycle not only increases the risk of costly outages and security breaches but also erodes the organization’s ability to respond to changing business needs. To break free from this pattern, companies must make a conscious effort to address technical debt head-on. Investing in tech debt remediation, enhancing code quality, and striking a balance between short-term business goals and long-term sustainability are crucial steps to ensure that technical debt does not hinder the organization’s future success.
The many faces of technical debt
Classifying technical debt will by no means suddenly make it simple and easy to handle. But the classification process focuses development teams and enables them to have more productive conversations.
Technical debt will always be a significant part of DevOps, and how to effectively manage this debt should be regularly taught to both students pursuing a career in the field and those with years of experience. It’s also essential to constantly evaluate where and how technical debt is hindering your team.
Once you’ve identified these factors, it will be easier to increase overall productivity and deliver features in a timely fashion. While there are many types of technical debt, the following are four examples of the types of technical debt that many developers will encounter in their work.
1. Unavoidable technical debt
Organizational change and technological advancement are the primary sources of unavoidable technical debt. It usually occurs when scope modifications are solicited mid-project, followed by the immediate cost of those modifications.
An example could be adding a new feature to a legacy system to better support mobile systems. In other words, unavoidable technical debt is inevitably generated as new organizational requirements or advances in technology cause a system’s code to become obsolete.
2. Software entropy or bit-rot
Software entropy or bit-rot happens over the course of an application’s lifespan as the quality of software gradually degenerates. It eventually leads to usability issues, such as errors, or necessitates frequent updates.
Software entropy also occurs if numerous developers make cumulative modifications that increase a software’s complexity over time. Their changes may also slowly damage the code or violate non-functional requirements such as accessibility, data integrity, security (cyber and physical), privacy (compliance with privacy laws), and a long list of others. Refactoring is generally the solution for software entropy.
3. Poor-quality software code
Agile software development depends on diminishing the scope of a release to guarantee high-quality code rather than prioritizing speed or release. By doing the latter, technical debt is passively generated when the scrum team discovers and tries to solve an issue. The number of times this process is repeated causes the cost of technical debt to increase, resulting in decreased efficiency and productivity as development teams repay their analogical debts.
Technical debt comes in the form of unnecessarily complicated, unreliable, or otherwise convoluted code. No code is perfect, but when it’s saddled with excessive technical debt, it can become a bigger problem than the issue it was designed to resolve.
The more technical debt found in a piece of code, the farther from the intended goal it becomes; The farther from the intended goal it becomes, the longer it will take to iron out the kinks.
Extemporaneous or absent developer onboarding and training, as well as insufficient coding criteria for developers, contribute to poor-quality software code as well. Additionally, having to rewrite outsourced code or poor scheduling adds extra stress to an already demanding job. These examples tend to increase the cost of technical debt exponentially compared to other instances and are common contributors to developer burnout.
4. Poor IT leadership
Poor IT leadership is another major contributor to the cost of technical debt, as well as many of the consequences mentioned before. It materializes in various ways, with many IT managers either unaware or in denial of the problem.
Micromanagement is a perfect example of a leadership style that contributes to varying degrees of technical debt. While it usually works great for small-scale projects, micromanagement causes leaders to develop tunnel vision. Before long, they’ve lost sight of the bigger picture and begun to rub their team the wrong way. All sorts of complications arise from these types of toxic environments. The resulting technical debt only compounds matters.
IT managers contribute to technical debt by not listening, considering, or implementing feedback from their team or scheduling sufficient time in each release to address historical debt issues. By ignoring others merely because they view them as subordinates, errors are overlooked.
In addition to that, cloud and containerization trends evolve at a rapid pace, often bypassing the understanding of both end users and IT management teams. Nevertheless, some organizations don’t want to risk appearing unknowledgeable and thus make poor decisions or adopt unnecessary tools that complicate things.
The role of new technology in technical debt accumulation and management
Technology is a double-edged sword when it comes to technical debt. On one hand, the rapid adoption of new technologies can introduce fresh sources of debt if not managed carefully. On the other hand, advancements in artificial intelligence (AI) and generative AI are transforming how companies approach identifying and resolving technical debt. AI-powered tools can automate code reviews, identify problematic areas in the codebase, and suggest optimizations to improve code quality and maintainability.
However, the implementation of these technologies is not without risk. If AI solutions are hastily integrated or poorly understood, they can inadvertently create new forms of technical debt, compounding existing challenges. That’s why it’s crucial for companies to take a balanced approach that carefully evaluates the risks and benefits of new technologies and invests in solutions that genuinely support long-term software quality and business value. By leveraging technology thoughtfully, organizations can not only address existing technical debt but also build more resilient and adaptable systems for the future.
Generative AI and code quality
A large piece of improving the technical debt landscape has been shifted with the availability of AI. Generative AI is poised to revolutionize software development by automating routine coding tasks, reducing bugs, and enhancing overall code quality. With AI-powered tools, developers can shift their focus from repetitive work to higher-level problem-solving, such as designing innovative features and improving system architecture. This not only accelerates development but also helps maintain a cleaner, more reliable codebase. With some platforms, developers can ask AI to identify and remediate technical debt in a small fraction of the time it would take to do it manually.
However, the rise of generative AI also brings new challenges. There are concerns about job displacement and the evolving skill sets required for developers to work effectively alongside AI. More importantly, if not properly managed, generative AI can introduce new technical debt by generating code that is difficult to maintain or lacks proper documentation. Look no further than poor vibe coding hygiene to uncover this issue. To fully realize the benefits of generative AI, companies must invest in ongoing developer training, prioritize code quality and security, and establish end-to-end processes for reviewing and integrating AI-generated code. By doing so, organizations can harness the power of AI to drive innovation while minimizing the risk of accumulating new technical debt.
How technical debt affects the customer experience
It’s important to remember that technical debt isn’t merely about short-term and long-term deficits. Depending on how much debt is left in an application, system performance can be drastically affected. When it’s time to scale up or add new features to a debt-laden IT infrastructure, the customer experience suffers.
For example, according to CNBC, online spending on Black Friday increased by nearly 22% in 2020 due to the COVID-19 pandemic restrictions. Many brick-and-mortar retailers scrambled to establish a competitive online presence. As a result, technical-debt-related issues such as website lagging, outages, and glitches plagued even major retailers.
Imagine the embarrassment of receiving complaints from customers about items vanishing from their carts at checkout. Worse yet, imagine losing tens of thousands of dollars to competitors during a lengthy crash.
The COVID-19 pandemic has forever changed how consumers interact with organizations. More crucially, the dynamic has shifted dramatically, with consumers having the power of countless choices.
Along with that, digital technology makes it easier for consumers to express their concerns about poor service experiences, posing more challenges than ever before, but increasing awareness of a company’s weak areas.
According to The Wall Street Journal, “Some 66% of consumers surveyed in 2020 said they had experienced a problem with a product or service, up from 56% in 2017, when the survey was last conducted. Further, Gartner posits that an ‘effortless experience’ is the key to loyal customers.
If your customers reach the customer service stage to express their complaints, whether it’s about a product or an inefficient, glitchy platform, you may be too late. If they don’t, you may be a step ahead of your competition. Ultimately, the cost of technical debt can be much greater than you think. Addressing it should be a top priority for your company.
Don’t allow technical debt to drag your team down
Senior developers find it difficult to illustrate the overall impact technical debt has on an organization’s bottom line to nontechnical executives and investors. Unlike financial debt, technical debt is a lot less visible, allowing people to disregard its impact more easily. When determining whether technical debt is worth it or not, context matters. By employing the vFunction Platform, AI and data-driven analysis facilitates, accelerates, and focuses most manual efforts and does much of the heavy lifting. This saves architects and development teams from spending thousands of hours manually examining diminutive fragments of code and struggling to identify and, let alone extract, domain-specific microservices. Instead, they can now focus on refining a reference architecture based on a proper “bird’s eye” view. Contact us for a demo today and start properly managing your company’s technical debt.
With how quickly technology continues to move, it’s no surprise that most organizations struggle with the challenges associated with outdated legacy systems. These systems can directly hinder a company’s ability to adapt, innovate, and remain competitive, thwarting growth, disrupting operational efficiency, and stifling innovation.
Legacy modernization presents a strategic solution, offering a path to transform these existing systems into valuable assets that propel businesses forward. It’s not just about swapping old for new; it’s a strategic process that requires careful planning, deep technical expertise, and a keen understanding of business goals.
We will explore the various modernization strategies, cover best practices, and examine the many benefits you can achieve through this transformative process. We will also address the challenges and considerations involved, offering practical insights and solutions. Real-world case studies will showcase successful legacy modernization initiatives, demonstrating how organizations have overcome obstacles to achieve remarkable results.
Whether you’re considering a modernization initiative or already have one underway, this guide provides a practical roadmap to a more efficient, secure, and scalable path to legacy modernization. Let’s begin by looking more closely at what legacy modernization is.
What is legacy modernization?
Legacy modernization is a strategic initiative undertaken by organizations to revitalize their legacy software applications and systems. This process is essential to align these technologies with current industry standards and evolving business needs. While these existing systems often remain functional, their outdated architecture, reliance on obsolete technologies, and difficulties integrating with modern solutions can hinder operational efficiency and innovation.
Modernization does not necessarily mean a complete replacement of legacy systems. Instead, it involves a range of strategies to improve these systems’ performance, scalability, security, and maintainability while leveraging existing investments. There are several strategies that organizations can leverage to achieve their modernization goals, each with varying degrees of complexity and invasiveness. These strategies range from less invasive approaches like code refactoring and rehosting to more complete transformations like rearchitecting and rebuilding. Organizations can also opt for a hybrid approach, combining multiple strategies to address specific needs and constraints.
Multiple factors influence the selection of the most appropriate modernization strategy. These include the age and complexity of the legacy system, the organization’s budgetary and timeline constraints, risk tolerance, and the desired level of transformation. A thorough assessment of these factors is crucial to ensure the chosen strategy aligns with the organization’s goals and objectives.
A well-executed software modernization initiative can have significant benefits for organizations. This includes reduced operational costs, improved agility and responsiveness to market changes, enhanced security against cyber threats, and increased customer satisfaction through modernized user interfaces and improved service delivery. Next, let’s look at the types of legacy systems and how modernization efforts can be customized and tailored for each.
Types of legacy systems
Legacy systems encompass a range of technologies, each with distinct characteristics that necessitate tailored modernization approaches. Some common categories include:
Mainframe systems
Financial institutions, government agencies, and organizations with high processing demands frequently use these large-scale, centralized systems. Their modernization often involves strategies like rehosting or refactoring to leverage modern infrastructure while preserving critical functionalities.
Client-server applications
This architecture distributes processing between client devices (e.g., desktops, laptops) and servers. Modernizing client-server applications may involve migrating to web-based or cloud-native architectures for improved accessibility and scalability.
Monolithic applications
Monolithic applications, characterized by a tightly coupled architecture, can be challenging to modify and scale. Modernization often involves decomposing them into smaller, independent modules for increased agility and maintainability. These modules could either become microservices or make up a modular monolith.
Custom-built applications
Due to their bespoke nature, these applications, developed in-house to address specific business requirements, can present unique modernization challenges. Teams may rearchitect or replace components to align them with modern standards.
Understanding the type of legacy system you’re dealing with is crucial for determining the appropriate modernization strategy. This knowledge allows organizations to select the most appropriate tools, techniques, and approaches to achieve their modernization goals while minimizing disruption and risk.
Business processes and legacy systems
Legacy systems often create what we call “process debt” that builds up over time. This happens when business logic becomes tightly coupled with workflows, making modernization far more complex than just updating code.
When business rules get hardcoded into process flows, organizations face a common problem: critical business state becomes scattered across multiple systems with no single source of truth. This creates confusion around transaction boundaries, where business transactions span multiple legacy systems without proper coordination. The result? It becomes nearly impossible to maintain data consistency or trace how data transforms across different process boundaries.
Manual integration points between these legacy systems pose another challenge. Each manual handoff creates undocumented dependencies through implicit data formats and business rules. These processes introduce variability that cascades through downstream systems, ultimately creating scalability limits that can’t grow with your business demands.
Before modernizing any code, successful organizations start by mapping their process landscape. This means identifying which systems are authoritative for specific business capabilities, documenting how systems integrate with each other, and understanding what happens when each system fails. You simply can’t effectively break down a monolith without first understanding the business process boundaries it serves.
Modernization strategies
Before selecting a modernization strategy, it is crucial to evaluate the software system’s components:
Hardware infrastructure: Bare-metal servers, on-premise virtualization, cloud, or hybrid setups.
Runtime environment: Web servers, application servers and database servers.
Development frameworks: Web frameworks, business logic frameworks, database frameworks, messaging frameworks, etc.
Business logic: Implemented in programming languages like Java, .NET, etc.
The choice of modernization strategy depends on the type of legacy system, desired outcomes, budget, and risk tolerance. Often, a combination of these components will need updating, with no one-size-fits-all approach. Here are some common approaches:
Encapsulation
This involves creating interfaces or APIs around the legacy system, allowing it to interact with modern applications without significant changes to legacy code and underlying infrastructure. This relatively low-risk approach can provide quick wins in terms of integration.
Rehosting
Also known as “lift and shift,” this strategy involves migrating the legacy application to a newer platform, such as the cloud, mainly keeping legacy code in place. Rehosting can offer immediate benefits like improved infrastructure and scalability.
Replatforming
Like rehosting, re-platforming involves migrating to a new platform but with some code adjustments to leverage the new platform’s capabilities. This can be a good option for systems that are not overly complex.
Refactoring
Refactoring involves restructuring the existing codebase without changing its external behavior. Optimizing existing code and infrastructure improves maintainability, testability, and often performance. It’s a more invasive approach than encapsulation but less risky than a total rewrite.
Rearchitecting
Rearchitecting involves a more radical approach, including redesigning the system’s architecture to leverage modern technologies and design patterns. This can lead to significant improvements in performance, scalability, and agility.
Rebuilding/Replacing
The most expensive and time-consuming option is completely rebuilding or replacing the legacy system with a new solution, but it offers the greatest flexibility and potential for innovation.
Hybrid approach
Organizations often adopt a hybrid approach, combining different strategies to address specific aspects of their legacy systems. This may involve encapsulating some components, rehosting others, and refactoring or rearchitecting critical modules.
Choosing the right strategy requires careful analysis and a deep understanding of the legacy system and the organization’s goals. It is crucial to involve key stakeholders and technical experts in this decision-making process.
Cloud and hybrid infrastructure
The shift to cloud and hybrid infrastructure offers more than just modern hosting—it provides architectural capabilities that enable specific modernization patterns you can’t achieve with traditional infrastructure.
These platforms deliver three key modernization capabilities. First, they enable incremental data synchronization through patterns like event sourcing and change data capture, allowing you to maintain legacy systems while building modern consumers. Second, service mesh integration lets you gradually migrate traffic between old and new systems while maintaining unified observability. Third, they support stateful workload migration through storage abstraction and session state externalization.
The key to successful implementation starts with observability. Deploy monitoring and logging across your hybrid environment before migrating any workloads. This gives you visibility into actual usage patterns, which often differ significantly from what you might assume. From there, you can implement the strangler fig pattern using API gateways as integration points, gradually replacing legacy functionality while maintaining existing interfaces.
Benefits of legacy modernization
Investing in legacy modernization can yield many benefits that touch nearly every aspect of an organization’s operations. Here are some of the key advantages:
Improved efficiency and productivity
Modernized systems streamline processes, automate manual tasks, and eliminate bottlenecks. This results in faster response times, reduced errors, and increased operational efficiency. Due to the modular nature of modernized systems, employees can focus on higher-value activities, improving productivity and job satisfaction.
Also, onboarding new employees becomes quicker. The enhanced efficiency also translates to improved resource utilization and cost savings, as fewer resources are required to achieve the same or better results.
Enhanced agility and innovation
Legacy systems are often rigid and slow to adapt to changing business needs. Modernized systems are more modular, flexible, scalable, and easily integrated with new technologies. This enables businesses to respond quickly to market trends, innovate faster, and stay ahead of the competition.
Reduced costs
Maintaining legacy systems can be a financial burden due to hardware obsolescence, expensive software licenses, and the need for specialized skills. Modernized systems often leverage cloud infrastructure, open-source software, and standardized technologies, which can significantly reduce long-term costs.
Increased security
Legacy systems are more vulnerable to security threats due to outdated software, unpatched vulnerabilities, and lack of support. Modernized systems incorporate the latest security measures, protocols, and best practices, ensuring better protection against cyberattacks, data breaches, and compliance violations. By mitigating security risks, organizations can safeguard their sensitive data, maintain customer trust, and avoid costly legal and regulatory penalties.
Improved customer experience
Modernized systems can deliver a seamless, personalized, and omnichannel customer experience. By integrating various touchpoints and leveraging data-driven insights from loosely-coupled modules, businesses can tailor their interactions to individual customer preferences and needs. This personalized approach increases customer satisfaction, loyalty, and, ultimately, higher revenue. Modernized systems also enable faster and more efficient service delivery, enhancing the overall customer experience.
Improved employee experience
Modernizing applications makes employees’ lives, mainly those of developers and architects, much easier. The developers and architects working on modernized applications will have an easier time working with them and will have more confidence in their ability to scale and adapt to future changes. Instead of a chosen few who know the old application’s architecture and codebase, modernizing the code and architecture makes the application more accessible to all developers. This can have a significant impact on the experience of developers and architects working on the applications.
As a secondary benefit, working in a more modern stack can also help to attract new employees to join your team since architects and developers tend to gravitate towards modern tech when it comes to taking on a new role.
Better data insights
Legacy systems often store data in silos, making it difficult to extract meaningful insights. Modernized systems facilitate data integration and analytics, enabling businesses to make data-driven decisions, drive innovation, and gain a competitive edge.
Future-proofing
Modernization ensures an organization’s IT infrastructure aligns with current and future technological advancements. This avoids becoming obsolete and provides a foundation for continuous innovation. Future-proofing provides a solid foundation for innovation and allows organizations to stay ahead of the curve.
The benefits of legacy system modernization extend beyond the IT department, impacting the entire organization. It’s a strategic investment that can drive business growth, improve competitiveness, and position the organization for long-term success. However, legacy modernization initiatives have challenges and complexities that organizations must carefully consider and address.
Challenges and considerations in legacy modernization
While legacy modernization offers significant advantages for businesses, it is essential to consider potential obstacles such as integration challenges, data migration complexities, and the need for effective change management to ensure a successful transition.
Complexity and risk
Legacy systems are often complex, poorly documented, and intertwined with critical business processes. Modernizing them requires careful planning and risk management. Visualizing the system’s key functional domains, their intricacies, interdependencies, and potential failure points is crucial for minimizing disruptions and ensuring a smooth transition.
Cost and time
Digital transformation projects can be expensive and time-consuming. The costs can vary widely depending on the size and complexity of the system, the chosen strategy, and the resources involved. Establishing realistic expectations and allocating sufficient budget and time are essential for a successful outcome.
Resistance to change
Employees accustomed to the legacy system may resist change due to fear of the unknown, learning curves, or potential workflow disruptions. Effective change management strategies, including communication, training, and stakeholder engagement, are vital for overcoming resistance and ensuring user adoption.
Data migration and integration
Migrating data from legacy systems can be a complex process. Ensuring data accuracy, durability, consistency, and security during the transition is critical. Integrating the modernized system with other existing applications and data sources can pose challenges. Thorough planning, data validation, and testing are necessary to mitigate these risks.
Skills and expertise
Modernization often requires specialized skills and expertise that may not be readily available within the organization. Partnering with experienced vendors or consultants can help bridge the skills gap and ensure the project’s success.
Legacy system interdependencies
Legacy systems are often tightly integrated with other applications and processes. Disentangling these dependencies and ensuring seamless integration with the modernized system can be a major challenge. A well-defined integration strategy and thorough testing are essential for mitigating these risks.
Regulatory and compliance requirements
Certain industries, such as finance, energy and healthcare, have strict regulatory requirements for data management, security, and privacy. Modernization projects must comply with these regulations to avoid legal and financial repercussions.
By proactively addressing these challenges and considerations, organizations can increase the likelihood of a successful legacy modernization initiative. Thorough planning, risk mitigation strategies, and effective communication are key to navigating this complex landscape and realizing modernization’s full potential.
The application modernization journey
to be successful with modernization, this requires what architectural observability. This allows architects and developers to systematically understand existing systems before making decisions about future architecture. This can be done manually, which takes an extended amount of time and large amounts of expertise, or automtically through platforms like vFunction.
The process starts with dependency discovery and technical debt assessment. While static code analysis shows you architectural debt, runtime analysis reveals how your systems actually behave in production. You need to identify dead code, understand which parts of your system handle the majority of traffic, and map all external dependencies along with their potential failure modes. Automated analysis tools, like vFunction, can accelerate this discovery process by analyzing your codebase to reveal hidden dependencies and structural patterns that would take months to document manually. vFunction’s architectural observability platform provides comprehensive dependency mapping and technical debt identification across both monolithic and distributed applications.
Next comes risk-based prioritization. Plot your components against business criticality and technical risk. Components that score high in both areas should be encapsulated first, not rewritten—the risk of introducing new problems often outweighs the benefits of clean code.
For domain boundary discovery, don’t guess at microservice boundaries. Instead, analyze database transactions to find true consistency boundaries, examine team communication patterns (Conway’s Law often reveals good service boundaries), and look at which components change together. Components that change together should generally stay together. Modern architectural analysis platforms can help identify these natural boundaries by examining actual code relationships and data flow patterns within your monolith, providing data-driven recommendations for optimal separation points. Tools like vFunction can automatically analyze class exclusivity and service relationships to suggest the most logical microservice boundaries based on actual code structure rather than assumptions.
Finally, establish continuous modernization through ongoing architectural observability that can validate your decisions over time. This includes monitoring for dependency and architectural drift, performance regression, and security boundary violations.
Case studies and best practices
Modernizing legacy applications is crucial for companies aiming to stay competitive and control their application scalability and costs. Modernization allows for greater scalability, faster deployment cycles, and improved developer morale. While the transition can be complex, the benefits are substantial. Let’s look at two examples below of the benefits of investing in legacy modernization, particularly in shifting from a monolith to a microservices architecture.
Trend Micro, a global leader in cybersecurity, successfully refactored its monolithic Workload Security product suite using vFunction’s AI-driven platform. This modernization led to a 90% decrease in deployment time for critical services and a 4X faster modernization process than manual efforts. The company also reported a significant boost in developer morale due to the improved codebase and streamlined processes.
Intesa Sanpaolo: Banking on modernization
Intesa Sanpaolo, a leading Italian banking group, also began a modernization journey, using vFunction as a critical factor in their strategy. By refactoring its monolithic Online Banking application into microservices, the bank achieved a 3X increase in release frequency and a 25% reduction in regression testing time. This resulted in substantial cost savings, improved application management, and increased customer satisfaction due to enhanced stability and reduced downtime.
These case studies help to highlight the transformative power of legacy modernization. By transitioning applications into a more modern light, such as moving from monolithic architectures to microservices, companies can unlock significant efficiency, cost savings, and customer satisfaction benefits.
Best practices for legacy modernization
These case studies illustrate some essential best practices for legacy modernization:
Start with a clear vision and strategy: Define the modernization project’s goals, objectives, and success metrics.
Conduct a thorough assessment: Assess the current state of your legacy systems, identify pain points, and prioritize areas for modernization.
Adopt a phased approach: Break down the modernization project into smaller, manageable phases to reduce risk and ensure continuous progress.
Address business logic early: For a typical 3-tier application, it is strategic to start with modularizing the business logic. Rewriting the user interface (UI) without addressing the business logic results in only an aesthetic upgrade, with no improvement in the user experience (UX). Conversely, initiating with database modernization is risky because database changes are complex to reverse and limit room for iteration. By modularizing the business logic first, the most significant value improvements can be achieved quickly. Once the business logic is modularized, you can then proceed to modernize the database and the user interface simultaneously, ensuring a comprehensive and effective upgrade.
Involve key stakeholders: Ensure that all relevant stakeholders, including business users, technical teams, and executives, are involved in the planning and decision-making.
Choose the right technology and partners: Select technologies and partners that align with your business goals and have proven expertise in legacy modernization.
Focus on data quality and integration: During the migration process, ensure that data is accurate, consistent, and secure. Plan for seamless integration with other systems.
Emphasize change management: Implement effective change management strategies to address resistance, communicate the benefits of modernization, and ensure user adoption.
Monitor and measure: Continuously monitor the modernized system’s performance, measure its impact, and adjust strategies as needed.
By following these best practices and learning from successful case studies, organizations can increase their chances of a successful legacy modernization initiative and reap its many benefits.
How vFunction can help with legacy modernization
Understanding the current state of your existing system is critical in determining whether it needs modernization and the best path to move forward. This is where vFunction becomes a powerful tool to simplify and inform software developers and architects about their existing architecture and the possibilities for improving it.
Let’s break down how vFunction aids in this process:
Automated analysis and architectural observability
vFunction begins by deeply analyzing an application’s codebase, including its structure, dependencies, and underlying business logic. This automated analysis provides essential insights and creates a comprehensive understanding of the software architecture, which would otherwise require extensive manual effort to discover and document. Once the application’s baseline is established, vFunction kicks in with architectural observability, allowing architects to observe how the architecture changes and drifts from the target state or baseline. As application modernization projects get underway, with every new code change, such as adding a class or service, vFunction monitors and informs architects, allowing them to observe the overall impacts of the changes.
Identifying microservice boundaries
Suppose part of your modernization efforts involves breaking down a monolith into microservices or a modular monolith. In that case, vFunction’s analysis helps identify domains, a.k.a. logical boundaries, based on functionality and dependencies within the monolith. It suggests optimal points of separation to ensure ongoing application resilience and scale.
Extraction and modularization
vFunction helps extract identified components within an application and package them into self-contained microservices. This process ensures that each microservice encapsulates its own data and business logic, allowing for an assisted move towards a modular architecture. Architects can use vFunction to modularize a domain and leverage Code Copy to accelerate microservices creation by automating code extraction and framework upgrades. The result is a more manageable application that is moving toward your target-state architecture.
Key advantages of using vFunction
vFunction analyzes applications and then determines the level of effort to rearchitect them.
Engineering velocity: vFunction dramatically speeds up the process of improving an application’s architecture and application modernization, such as moving monoliths to microservices, if that’s your desired goal. This increased engineering velocity translates into faster time-to-market for products and features and a modernized application.
Increased scalability: By helping architects view and observe their existing architecture as the application grows, application scalability becomes much easier to manage. Scaling is more manageable by seeing the application’s landscape and helping improve each component’s modularity and efficiency.
Improved application resiliency: vFunction’s comprehensive analysis and intelligent recommendations increase your application resiliency and architecture. By seeing how each component is built and interacts with each other, teams can make informed decisions favoring resilience and availability.
Conclusion
Legacy modernization is a strategic initiative for businesses to remain competitive. It involves updating or replacing outdated systems and processes to improve efficiency, reduce costs, and enhance security. Although legacy system modernization can be complex, the advantages are substantial and impact many areas of the business and technical assets. With careful planning and expertise, companies can transform legacy systems back into valuable assets that drive innovation, growth, and long-term success.
Legacy application modernization is a continuous process. As technology evolves, businesses must adapt to remain competitive. By adopting a mindset of continuous modernization using processes like architectural observability, organizations can ensure their systems remain relevant, agile, and capable of supporting their evolving business needs. As Trend Micro and Intesa Sanpaolo demonstrated, the strategic decision to modernize applications can yield substantial returns.
If your organization is grappling with the limitations of legacy systems, vFunction’s AI-driven platform gives teams deep insights and actionable suggestions to help expedite legacy modernization initiatives. Embrace the future of application development and modernization by unlocking new levels of agility, scalability, and innovation with vFunction’s architectural observability platform.
Most application modernization projects fail not because of technical complexity, but because they start with the wrong question. Instead of asking “What technology should we use?” successful organizations ask “What business outcomes do we need to achieve?”
The change in framing makes all the difference. It determines whether modernization becomes a strategic advantage or an expensive technical exercise that delivers little business value.
The business-first modernization framework
Strategic modernization requires a fundamental shift in thinking. Rather than treating modernization as purely a technical initiative, it’s about aligning every architectural decision with broader business goals. That might mean entering new markets, accelerating product launches, improving operational efficiency, or outpacing competitors.
This alignment isn’t just about executive buy-in. It ensures every dollar spent on modernization contributes directly to measurable business outcomes. With traditional modernization efforts, organizations that master this alignment see modernization ROI within 18-24 months. Add in some of the latest AI tooling, and these timelines become even more accelerated and cost-effective. Even with AI helping to reduce costs, those who focus purely on technical metrics often struggle to justify their investments.
The portfolio assessment reality check
Once business goals are clear, the real strategic work begins: deciding what to modernize, when, and how. This requires evaluating your application portfolio through two critical lenses simultaneously.
Business value evaluation examines each application’s revenue impact, customer experience contribution, operational criticality, and strategic importance. A customer-facing e-commerce platform that generates 60% of revenue clearly deserves different prioritization than an internal reporting tool used quarterly.
Technical health analysis evaluates architectural complexity, performance, maintainability, and integration risk. Sometimes, business-critical applications are technical time bombs, draining resources while delivering diminishing returns.
vFunction looks at your total application portfolio to pinpoint and prioritize fixes for technical debt.
The magic happens when you overlay these perspectives using systematic decision frameworks like TIME (Tolerate, Invest, Migrate, Eliminate) developed by Gartner®. That lens creates objective, consistent prioritization that balances business impact with technical feasibility— so you modernize what matters most.
Implementation sequencing: the strategic pilot approach
Even with clear priorities, the implementation sequence can make or break the success of modernization. The most effective approach follows a proven three-phase pattern:
Strategic pilots start with applications that prove concepts and build organizational capabilities, delivering meaningful value. These shouldn’t necessarily be your most business-critical systems, but they should be significant enough to matter and contain enough to learn from quickly.
Phased scaling takes successful patterns from pilots and applies them across broader portfolios, carefully managing dependencies and organizational capacity. This approach expands capabilities without disrupting core business processes or overwhelming teams.
Continuous improvement treats modernization as an ongoing discipline rather than a one-time project. The best organizations establish systematic practices for evaluation, planning, and implementation that continuously evolve based on lessons learned and changing business needs.
The timelines for rolling this out vary depending on the project size and the applications you are modernizing. Small efforts can be modernized in time periods as short as a few days or weeks before a pilot is ready to be deployed. With larger initiatives, you may see weeks, months, or potentially even years. However, the emphasis of this approach is to do smaller iterations and incremental modernization. That said, adding AI into the mix can condense this immensely, potentially even letting initial pilots be tested within a few hours before being deployed more widely.
Real-world strategic alignment in action
The power of business-driven modernization becomes clear when you examine how industry leaders approached their transformations:
Amazon didn’t break up its monolith because microservices were trendy. They did it because their giant monolithic “bookstore” application limited their speed and agility, requiring complicated coordination across every development team for each release and making it difficult to innovate at scale. As described in Amazon’s 1998 Distributed Computing Manifesto (republished by Werner Vogels in 2022), their transformation to a service-oriented architecture enabled them to scale dramatically and deploy independent services without impacting the entire system.
Netflix learned resilience the hard way—after a three-day outage from database corruption. Their seven-year cloud transformation wasn’t just technological; it built the foundation to scale from millions of DVD subscribers to over 300 million streaming users while maintaining 99.99% uptime.
Walmart knew competing with digital-first retailers required more than a website. They needed seamless omnichannel experiences that could handle massive traffic spikes. Their microservices transformation enabled them to handle 500 million Black Friday page views without performance degradation.
Turo took an architecture-first approach, using advanced analysis tools to understand their monolithic dependencies before refactoring. That groundwork accelerated their transformation and improved developer velocity—proof that the right tools and methodology can significantly shape outcomes.
Not just for enterprises: Why ISVs must modernize, too
It’s easy to picture modernization as an issue only for decades-old enterprises weighed down by legacy code. But even modern ISVs—whose applications are their business—face the same pressures.
Software built just a few years ago can quickly outgrow its original architecture as customer expectations, data demands, and AI-driven capabilities evolve. For ISVs, modernization isn’t just about fixing technical debt; it’s about protecting growth, staying competitive, and ensuring the product can scale with the business.
The stakes are often higher because architectural decisions directly affect revenue and customer loyalty. An ISV with an inflexible platform risks slower feature delivery, difficulty integrating with AI and partner ecosystems, and ultimately losing ground to faster-moving competitors.
Second only to the acceleration factor, organizations must modernize architectures to a state where they can rapidly integrate AI-driven features like real-time personalization, intelligent automation, and predictive analytics. Just as real-time data became an industry expectation, users now demand these AI capabilities as standard. Architectural modernization makes this possible by transforming monoliths into modular domains or microservices, enabling workloads to fully leverage cloud-native services such as Amazon EKS, Lambda, and Azure Functions—unlocking the scalability, elasticity, cost efficiency, and faster innovation needed to deliver on those expectations.
Complex monolithic systems often can’t support these AI workloads effectively. They lack the architectural flexibility required for rapid API integrations, real-time data processing, and continuous deployment cycles that AI capabilities necessitate. This creates a compelling dual motivation: modernize to leverage AI, and use AI to modernize faster.
Success requires tracking both business and technical outcomes. This involves measuring customer satisfaction, time-to-market, and revenue per app, alongside deployment frequency, system performance, and developer velocity.
Organizations that succeed establish success criteria upfront and instrument systems to measure progress continuously. They understand modernization isn’t just about “modern tech”—it’s about using that tech to deliver better business outcomes.
Turning strategy into results with vFunction
This is exactly where vFunction comes in, helping organizations align modernization with business outcomes by providing a systematic, architecture-first assessment of application portfolios. Instead of guessing what to modernize, vFunction surfaces insights on business value and technical health side by side, making it easier to prioritize based on impact.
vFunction helps organizations align modernization with business outcomes, such as engineering velocity, cloud readiness, and resiliency.
That means modernization efforts can be directly linked to outcomes like improving resiliency, scaling to handle unpredictable demand, accelerating engineering velocity, or preparing applications for cloud-native architectures.
And with built-in GenAI capabilities, vFunction accelerates both analysis and refactoring by generating optimized architectural prompts that flow directly into code assistants like Amazon Q Developer or GitHub Copilot. This guided approach brings architectural context into the developer’s IDE, enabling assistants to act on precise refactoring tasks instead of working blindly at the code level. The result: a faster path to modernization patterns that scale, with outcomes directly tied to the needs of the business.
vFunction generates optimized architectural prompts that flow directly into code assistants like Amazon Q Developer or GitHub Copilot.
The strategic imperative
In an era where application architecture directly impacts business performance, organizations that approach modernization strategically will gain significant advantages in operational efficiency, security posture, and innovation velocity.
The key insight is simple but powerful: start with business goals and work backward to technical requirements. This ensures that every modernization decision contributes to business value, not just technical elegance.
Organizations that master this alignment don’t just modernize their applications; they modernize their competitive position. Ready to build a modernization roadmap anchored to your business goals? Visit our platform page to see how vFunction uses architectural modernization to turn application complexity into a clear, actionable path forward.
In 2025, the massive wave of changes in the software landscape continues to grow. Cloud native architectures, microservices, serverless functions, and AI have created huge shifts and unprecedented opportunities, complexity, and risk. Understanding what’s happening inside these intricate systems when things go wrong, or even when they operate as expected, is harder than ever. Traditional monitoring, which relies on predefined dashboards and alerts, can tell you that a problem exists, but struggles to tell you why.
This is where software observability comes in. More than just monitoring 2.0, observability is the ability to infer the internal state and health of complex systems by analyzing the data they produce in the form of logs, metrics, and traces. These logs, metrics, and traces are known as external outputs, which observability tools analyze to gain insights into the internal system state. In this blog, we will cover everything you need to know about software observability tools and the best ones to add to your stack. Let’s get started by digging a bit further into what observability is.
What is observability in software systems?
At its core, software observability is the ability to measure and infer the internal state of a complex system based solely on the data it produces. The term comes from control theory, where it describes understanding a system by observing its external signals. In the context of modern software, especially distributed systems like microservices running in the cloud, observability means having the tools and data to understand why something is happening, not just that it’s happening (a staple of more traditional monitoring).
Observability is more than just collecting data from within an application; it’s about implementing high-quality, contextualized telemetry that allows you to explore behavior and performance effectively. Traditionally, observability has “three pillars”:
Logs: These are discrete, timestamped records of events that occurred over time. Logs provide detailed, context-rich information about specific occurrences, such as errors, warnings, application lifecycle events, or individual transaction details. They are essential in most apps for troubleshooting issues and tracking the steps that lead to a problem.
Metrics: Metrics are numerical representations of system health and performance measured over time. Think CPU utilization, memory usage, request latency, error rates, or queue depth. Metrics are usually aggregated so they can be easily assessed in dashboards, used for alerting on predefined thresholds, and understanding trends and overall system behavior.
Traces: Traces track the end-to-end journey of a single request or transaction as it flows through multiple services in a distributed system. Each step in the journey (a “span”) contains timing and metadata. Traces are key for visualizing request flows, identifying bottlenecks, and understanding inter-service dependencies. They can also be very helpful in diagnosing latency issues in microservices architecture and highly complex systems with a lot of moving parts.
While these three pillars (metrics, logs, and traces) are the foundation, the ultimate goal of combining them is to give teams the visibility to ask any question about their system’s behavior, especially the “unknown unknowns” or emergent issues that teams couldn’t have predicted, and get answers quickly.
Observability vs. traditional monitoring
While related, observability and traditional monitoring serve distinct purposes in understanding software systems. Monitoring typically involves tracking predefined metrics to check system health, whereas observability enables deeper exploration to understand why systems behave the way they do, especially when encountering unexpected issues. Monitoring is often a component that feeds into a broader observability strategy. A traditional monitoring solution focuses on collecting and analyzing preset data and generating alerts based on known thresholds, while observability tools gather all generated data for a more comprehensive view.
Here’s a breakdown of the key differences:
Comparison aspect
Traditional monitoring
Software observability
Primary goal
Health/status checking; alerting on known thresholds
Deep understanding; Debugging unknown & complex issues
Approach
Uses predefined metrics, dashboards, and alerts
Exploratory analysis using rich, correlated telemetry
Question focus
Answers predefined questions (“Is CPU usage high?”)
Enables asking arbitrary questions (“Why is this slow?”)
When comparing the two, you can think of monitoring as the dashboard warning lights in your car: they tell you if something pre-determined is wrong (low oil, engine hot). On the other hand, observability goes a step further, providing a comprehensive diagnostics toolkit, enabling the identification of root causes such as a sensor failure affecting fuel mix, and insights into how different systems interact. Observability gives a deeper look at issues that traditional monitoring cannot provide.
Limitations of traditional runtime observability
Traditional observability and application performance monitoring (APM) tools are great for monitoring runtime performance – identifying latency, errors, and resource usage (the “what” and “when”) – but often fall short in explaining the deeper “why” rooted in application architecture. They don’t have visibility into the structural design, complex dependencies, and accumulated architectural technical debt that causes recurring runtime problems. They highlight the symptoms of poor architecture (slow transactions or cascading failures) rather than the underlying structural issues. This means you can’t leverage these insights to fix root causes or proactively plan for modernization.
Emerging areas in observability: beyond runtime telemetry data
To fill the gaps left by runtime-focused tools, several purpose-built observability areas are emerging that complement standard observability tools offering deeper insights into specific domains:
Architectural observability: Focuses on understanding the application’s static and dynamic structure, component dependencies, architectural drift, and technical debt. Tools like vFunction analyze how the application is built and identify structural issues in the business logic, guiding modernization or refactoring efforts and supporting software architecture governance.
Data observability: Concentrates on the health and reliability of data pipelines and data assets. Monitors data quality, freshness, schema changes, and lineage so you can trust the data used for analytics and operations.
API observability: Provides deep visibility into the performance, usage, and compliance of APIs, which are the communication points in modern distributed systems. Helps track API behavior, identify errors, and understand consumer interactions. Some platforms, such as Moesif, can also use the observability data for monetization and API governance.
These emerging areas complement runtime observability and give you a more complete picture of complex software systems.
Underpinning much of the progress in both traditional and emerging observability areas is OpenTelemetry (OTel). Its rapid adoption across the industry is a big shift towards standardized, vendor-neutral instrumentation. OTel provides a common language and set of tools (APIs, SDKs, Collector) to generate and collect logs, metrics, and traces across diverse technology stacks. By decoupling instrumentation from specific backend tools, OTel prevents vendor lock-in, ensures that observability can be future-ready, and captures rich telemetry data needed to power all forms of observability. OTel helps power almost all types of observability from runtime APM to architectural, data, and API analysis.
Why observability matters in modern software
With traditional monitoring being around for so long, why is observability so important in modern software? As software systems evolve into complex webs of microservices, APIs, cloud infrastructure, and third-party integrations, simply knowing if something is “up” or “down” is no longer sufficient. The dynamic, distributed nature of modern applications demands deeper insights. Observability has shifted from a ‘nice-to-have’ to a necessity for building, running, and innovating in 2025 and beyond.
Here’s why observability is so important for modern development teams and stakeholders:
Taming complexity and speeding up incident resolution
Modern systems can fail in countless ways. When something goes wrong, pinpointing the root cause across dozens or hundreds of services is impossible with traditional monitoring. Observability gives you the correlated telemetry (traces, logs, metrics) to follow the path of a failing request, understand component interactions, and find the source of the problem, reducing Mean Time To Detection (MTTD) and Mean Time To Resolution (MTTR). It lets you debug the “unknown unknowns”, a.k.a. the things you couldn’t have anticipated.
Building more reliable and resilient systems
By giving you a deep understanding of how systems behave under different loads and conditions, observability helps you identify potential bottlenecks, cascading failure risks, and performance degradation before they cause user-facing outages. This allows you to target improvements and architectural changes that make the system more stable and resilient in a data-driven manner.
Boosting developer productivity and enabling faster innovation
When developers can see the impact of their code changes in production-like environments, this visibility accelerates the whole development lifecycle. Integrating observability into your workflow early (known as “shifting left”) lets engineers debug more efficiently, gain confidence in their releases, and understand performance implications of code changes. The end result is faster and safer deployments within DevOps and SRE frameworks.
Better end-user experience
It’s no surprise that system performance impacts user satisfaction. Slow load times, intermittent errors, or feature failures can drive users away. Observability lets you identify and troubleshoot issues affecting specific user cohorts or transactions, even subtle ones that won’t trigger standard alerts, for a better, more consistent customer experience.
Optimizing performance and cost
Cloud environments offer scalability and potential cost savings. However, on the flip side, they can also lead to runaway costs if not managed. Observability helps you identify inefficient resource usage, redundant service calls, or performance bottlenecks that waste compute power and inflate cloud bills. Knowing where time and resources are spent by leveraging observability tools allows you to target optimization efforts, improve efficiency, and reduce operational expenses.
Enhanced security posture
There is an increasing overlap between operational monitoring and security, with observability as a major factor in these blurring lines. Observing system behavior, network traffic, and API interactions can reveal anomalies that indicate security threats, such as unusual access patterns, data exfiltration attempts, or compromised services. This allows observability data to provide context for security investigations and detection of exploits. The best defense in terms of security is always being proactive; however, when vulnerabilities do slip through, the early detection that observability can provide is critical.
Overall, observability is part of the fabric of modern applications. Beyond simply writing and publishing code, applications are expected to be scalable, secure, and performant. Observability is a major part of ensuring that applications are able to hit these expectations.
How to choose the right software observability tool
Knowing the necessity of observability leads to the crucial step of selecting the right tools. Decide by evaluating key features and capabilities offered by each platform. Focus on:
Data coverage and integration
When selecting an observability tool, ensure that the tool supports the essential data types (logs, metrics, traces) needed for your specific application and use case. Assess the tool’s ability to efficiently ingest, store, and correlate data. Understand costs and data storage impact, if relevant. Check the tool’s compatibility with key technologies in your stack, including:
Cloud providers such as AWS, Azure, and GCP
Container platforms like Kubernetes
Programming languages and frameworks: Java, Python, Go, Node.js, .NET
Databases, message queues, and serverless infrastructure
Additionally, look for support for OpenTelemetry (OTel), which offers vendor-neutral instrumentation, ensuring there are no lock-ins and allowing for future flexibility.
Correlation and contextualization
Isolated data points don’t offer much value on their own; it’s the connected insights that truly matter. The standout feature for observability tools is their capability to automatically link related logs, metrics, and traces from a single user request, transaction, or system event. Attempting to manually combine this data across various systems is not only slow but is also likely to lead to mistakes. Moreover, consider how well the tool can enrich telemetry data with additional context and automate subsequent actions. Can the tool associate performance data with specific actions like code deployments, changes in feature flags, infrastructure updates, or details of user sessions? This extra layer of context is vital for effective problem-solving and debugging.
Analysis, querying, and visualization
The freedom to explore your data is essential for effective observability. Evaluate the platform’s query capabilities—are they powerful and flexible enough to handle complex, ad-hoc queries, especially for high cardinality data? Also, the depth of visualization features should be considered. Do the dashboards provide intuitive, customizable, and effective tools for displaying complex system interactions? Key visualization features to look for include service maps showing dependencies, distributed trace views like flame graphs, and clear metric charting.
It’s also crucial to ensure the platform supports analysis and querying at your required scale, as telemetry data from modern applications often demands substantial storage and processing resources.
Beyond manual exploration, many platforms now incorporate AI and machine learning (AI/ML) features for tasks such as automated anomaly detection, noise filtering to reduce alert fatigue, root cause analysis, and even predictive insights. While AI features are becoming standard, it’s important to assess their maturity and usefulness in practice.
Ease of use and learning curve
A tool is only as effective as its usability. The platform should offer an intuitive interface tailored to the needs of developers, engineers, SREs, and operations teams. Evaluate the effort required to set up the tool, including application instrumentation (automatic vs. manual), alert configuration, and ongoing support and maintenance. Strong documentation, responsive vendor support, and a user community are equally critical—they can significantly impact both the ease of adoption and the long-term success of the tool within your organization.
Cost and pricing model
Observability can quickly become a big operational expense. Make sure you fully understand the pricing model and available options. Is the pricing model based on data volume (ingested or stored), number of hosts or nodes monitored, active users, specific features, a combination of these, or other pricing variables for emerging solutions? Ensure that the pricing model is transparent and predictable so that you can forecast the costs as you scale. Before committing, calculate the Total Cost of Ownership (TCO) including data egress fees and storage costs as your applications scale. Also be aware of any professional services or training that your team will need in order to deploy and use the tool. Your best bet is to look for vendors with flexible models that match your usage patterns and expected scale.
Specific needs and future goals
Ultimately, ensure the tool’s capabilities align with your specific goals. Are you primarily focused on APM, or do you also require features like in-depth infrastructure monitoring, log aggregation and analysis, or security event correlation? Critically, do your goals go beyond runtime monitoring? Do you need to understand the underlying application architecture, identify technical debt hotspots or gain visibility for modernization initiatives? Some tools are great at performance monitoring while others are more specialized such as vFunction, focused on deeper architectural observability and analysis.
Start by prioritizing your key requirements, then shortlist 2-3 tools for Proof of Concept (POC) testing using realistic workloads. Involve the daily users—developers, SREs, or operations teams—to gather actionable feedback. Use POC results to make an informed decision on the best tool for your needs.
Core capabilities of modern observability tools
Modern observability tools are engineered to deliver comprehensive insights into the performance, health, and behavior of today’s complex systems. As organizations increasingly rely on distributed architectures, microservices, and cloud infrastructure, the need for robust observability platforms has never been greater. These tools go beyond basic monitoring by offering a suite of core capabilities that empower teams to proactively manage, troubleshoot, and optimize their environments.
Key capabilities of modern observability tools include infrastructure monitoring, distributed tracing, log management, and metrics collection. Together, these features enable organizations to gain a holistic view of their systems, quickly identify issues, and make informed decisions to enhance reliability and performance. By leveraging these capabilities, teams can transform raw observability data into actionable insights, ensuring their systems remain resilient and efficient even as complexity grows.
Infrastructure monitoring
Infrastructure monitoring is foundational to any observability strategy. Modern observability tools continuously collect and analyze data from critical infrastructure components such as servers, virtual machines, containers, networks, and databases. By monitoring metrics like CPU utilization, memory consumption, disk I/O, and network throughput, these tools provide real-time visibility into the health and performance of the underlying infrastructure.
In addition to metrics, observability tools gather logs and traces from infrastructure components, enabling teams to detect anomalies, forecast capacity needs, and respond to incidents before they escalate. Effective infrastructure monitoring helps organizations optimize resource allocation, minimize downtime, and maintain high availability across their environments. By making infrastructure data accessible and actionable, monitoring tools empower teams to keep their systems running smoothly and efficiently.
Distributed tracing
Distributed tracing is a critical capability for understanding the flow of requests across complex, distributed systems. Observability tools equipped with distributed tracing can track individual transactions as they traverse multiple services, APIs, and infrastructure layers. By collecting detailed logs and traces at each step, these tools reveal how requests are processed, where delays occur, and how different components interact.
This end-to-end visibility is essential for diagnosing performance bottlenecks, identifying sources of latency, and ensuring optimal system performance. Distributed tracing enables teams to pinpoint issues that would be difficult or impossible to detect with traditional monitoring, making it an indispensable tool for maintaining the health of modern, interconnected applications.
Log management
Log management solutions are another cornerstone of modern observability. Observability tools collect, store, and analyze log data from a wide range of sources, including applications, infrastructure, and security systems. By centralizing log data, these tools provide a unified view of system behavior, making it easier to detect anomalies, investigate incidents, and track changes over time.
Advanced log management capabilities allow teams to search, filter, and correlate log entries, uncovering patterns and root causes that might otherwise go unnoticed. In addition to supporting operational troubleshooting, robust log management helps organizations meet compliance requirements by maintaining a secure, auditable record of system activity.
Metrics collection tools
Metrics collection tools play a vital role in observability by gathering quantitative data on system performance, usage, and health. These tools collect telemetry data from applications, infrastructure, and services, tracking key performance indicators such as response times, error rates, throughput, and resource utilization.
By visualizing and analyzing metrics, organizations gain comprehensive insights into system behavior and can quickly identify trends or deviations from expected performance. Metrics collection tools enable proactive optimization, helping teams reduce downtime, improve reliability, and deliver a better user experience. With the ability to collect telemetry data from diverse sources, observability platforms provide a single source of truth for monitoring and managing complex environments.
Together, these core capabilities ensure that observability tools provide the comprehensive insights needed to monitor, analyze, and optimize modern systems. By unifying data from logs, metrics, and traces, observability platforms empower organizations to make data-driven decisions, enhance system reliability, and deliver exceptional user experiences.
Top 10 software observability tools (2025)
Selecting the right software observability tool is complex, with each tool offering unique strengths in data gathering, analysis, and application. Here, we spotlight the top ten tools and frameworks of 2025, showcasing both established solutions and innovative newcomers that tackle the critical issues faced in contemporary software development and operations. Let’s take a look:
vFunction
We’ll kick off our list of observability tools with vFunction, an emerging solution founded in 2017. Purpose-built to manage complexity and technical debt in modern software systems, it also complements traditional APM tools by providing deep architectural insight. As business logic becomes more distributed—and harder to trace—vFunction helps teams improve system understanding to regain architectural control, reduce risk, and accelerate decision-making. Using architectural observability, vFunction continuously visualizes application architecture, detects issues like redundancies and circular dependencies, and automates the identification and resolution of complex service interactions across both monoliths and microservices.
Key observability features:
vFunction provides real-time architectural insight across both monoliths and distributed services—using patented static and dynamic analysis to uncover hidden dependencies, dead code, and complex flows in Java and .NET monoliths, and leveraging runtime observation with OpenTelemetry to automatically visualize service interactions in distributed environments (supporting Node.js, Python, Go, and more).
Intelligent analysis is applied across the application architecture to address surface architectural concerns such as service redundancies, circular dependencies, tight coupling, overly complex flows, performance anomalies, and API policy violations, enabling teams to act before issues escalate.
Correlation of OpenTelemetry traces with AI-driven architectural analysis to uncover structural bottlenecks contributing to performance issues, going beyond traditional APM trace visualization.
Architectural insights from the vFunction analysis produce a list of ‘To Do’ development tasks that automatically feed Generative AI code assistants like Co-pilot and Amazon Q, which generate the actual code fixes for the architectural issues identified by vFunction.
vFunction’s observability features are best suited for engineering teams needing visibility and control over complex distributed applications or those modernizing monolithic systems. It helps teams who want to go beyond surface-level service maps to understand the why behind architectural complexity, identify hidden dependencies impacting performance and resilience, and proactively manage architectural drift and technical debt.
Datadog, founded in 2010, is a leading SaaS observability platform designed for cloud-scale applications and infrastructure. It is known for providing a unified view across diverse monitoring domains, consolidating data from hundreds of integrations into a single interface. Datadog helps teams monitor infrastructure, applications, logs, security, and user experience in complex, dynamic environments.
Key observability features:
A unified observability platform integrates infrastructure, APM, logs, real user monitoring (RUM), synthetics, security, and network monitoring.
Extensive library of pre-built integrations for cloud providers, services, and technologies.
Real-time dashboards, alerting, and collaboration features enable teams to track key metrics like request latency, CPU utilization, and error rates.
Datadog’s observability features are best suited for DevOps teams, SREs, and developers who need comprehensive, end-to-end visibility across their entire cloud or hybrid stack within a single platform. Datadog excels at correlating data from different sources to provide context during troubleshooting.
New Relic, established in 2008, pioneered the APM market as a SaaS solution. New Relic is known for its deep insights into application performance and has expanded into a full-stack observability platform. New Relic helps engineering teams understand application behavior, troubleshoot issues, and optimize performance throughout the software lifecycle.
Key observability features:
Deep code-level APM diagnostics and distributed tracing.
Full-stack monitoring including infrastructure, logs, network, and serverless functions.
Digital experience monitoring (Real User Monitoring (RUM), and Synthetics).
New Relic’s observability features are best suited for teams prioritizing application performance management, reliability, and understanding the root cause of issues within the application code itself. It provides developers and SREs with the detailed data needed to optimize complex applications.
Prometheus (started 2012) is a Cloud Native Computing Foundation (CNCF) open-source project focused on time-series metric collection and alerting, while Grafana is the leading open-source platform for visualization and analytics, often used together. They are known as a de facto standard for metrics monitoring and dashboarding, especially in Kubernetes and cloud-native ecosystems.
Key observability features:
Prometheus: Efficient time-series database, powerful PromQL query language, service discovery, and alerting via Alertmanager.
Grafana: Highly customizable dashboards, support for numerous data sources (including Prometheus, Loki, Tempo), extensive plugin ecosystem.
Often combined with Loki for logs and Tempo for traces to build a full open-source observability stack (PLG/LGTM stack).
Grafana & Prometheus observability features are best suited for teams seeking powerful, flexible, and often self-managed open-source solutions for monitoring and data visualization. They excel in metrics-driven monitoring and alerting, providing deep customization for technical teams managing cloud-native environments.
Elastic Observability evolved from the widely adopted ELK Stack (Elasticsearch, Logstash, Kibana), initially known for its powerful open-source log aggregation and search capabilities. Elastic Observability now integrates metrics, APM, and security analytics (SIEM) into a unified platform, available both as a self-managed infrastructure and via Elastic Cloud.
Key observability features:
Robust log aggregation, storage, search, and analysis powered by Elasticsearch.
Integrated APM with distributed tracing and service maps.
Infrastructure and metrics monitoring using Elastic Agent (integrating capabilities previously in Beats).
Elastic Observability’s features are best suited for teams requiring strong log analytics as a core capability, often starting with logging use cases and expanding into APM and infrastructure monitoring. Elastic Observability is valuable for operations, security, and development teams needing integrated insights across logs, metrics, and traces.
Splunk, founded in 2003, is a market leader in analyzing machine-generated data, renowned for its powerful log management and SIEM capabilities. It has extended its platform into the Splunk Observability Cloud, integrating APM and infrastructure monitoring with its core data analysis strengths.
Key observability features:
Industry-leading log data indexing, searching using Splunk Search Processing Language (SPL), and analysis capabilities.
Full-fidelity APM with NoSample tracing.
Real-time infrastructure monitoring, RUM, and synthetic monitoring.
Splunk’s observability features are best suited for organizations, often large enterprises, that need powerful data investigation capabilities across IT operations and security. Teams benefit from its ability to correlate observability data (metrics, traces, logs) with deep log insights and security events.
Dynatrace, with origins in 2005, provides a highly automated, AI-powered observability and security platform. It is known for its OneAgent technology for automatic full-stack instrumentation and its Davis AI engine for automated root cause analysis and anomaly detection across complex enterprise environments.
Key observability features:
Automated discovery, instrumentation, and topology mapping via OneAgent.
AI-driven analysis (Davis) for automatic root cause detection, anomaly detection, and predictive insights.
Full-stack visibility including infrastructure components, applications, logs, user experience (RUM/Synthetics), and application security.
Dynatrace’s observability features are best suited for medium-to-large enterprises seeking a high degree of automation and AI-driven insights to manage complex hybrid or multi-cloud environments. It reduces manual effort in configuration and troubleshooting for IT Ops, SRE, and DevOps teams.
AppDynamics, founded in 2008 and now part of Cisco, is a leading APM platform, particularly known for its ability to connect application performance to business outcomes. It helps organizations monitor critical applications and understand the business impact of performance issues.
Key observability features:
Deep APM with code-level visibility.
Business transaction monitoring, mapping user journeys, and critical workflows.
Correlation of IT performance metrics with business KPIs (Business IQ).
AppDynamics’ observability features are best suited for enterprises where understanding the direct link between application performance and key business metrics (like revenue or conversion rates) is crucial. It’s ideal for application owners, IT and business analysts focused on business-critical systems.
OpenTelemetry (OTel) is not a vendor platform but an open-source observability framework stewarded by the CNCF, created from the merger of OpenTracing and OpenCensus around 2019. It is known for standardizing the way applications and infrastructure are instrumented to produce telemetry data (logs, metrics, traces).
Key observability features:
Vendor-neutral APIs and SDKs for code instrumentation across multiple languages.
OpenTelemetry Collector for receiving, processing, and exporting telemetry data to various backends.
Standardized semantic conventions for telemetry data, ensuring consistency.
OpenTelemetry is best suited for any organization building or operating modern software that wants to avoid vendor lock-in for instrumentation. It empowers developers and platform teams to instrument once and send data to their choice of observability backends, ensuring portability and flexibility.
AWS CloudWatch is the native monitoring and observability service integrated within Amazon Web Services, evolving significantly since its initial launch in 2009. It is known for providing seamless monitoring for resources and applications running on the AWS platform.
Key observability features:
Automatic data collection of metrics and logs from dozens of AWS services.
Customizable dashboards and alarms based on metrics or log patterns.
Integration with AWS X-Ray for distributed tracing within the AWS ecosystem.
AWS CloudWatch’s observability features are best suited for teams whose operations are primarily in the AWS cloud. It offers convenient, built-in monitoring for AWS services, making it ideal for administrators and developers managing AWS infrastructure and applications.
Conclusion
As we advance into 2025, software becomes even more complex, especially with the widespread use of microservices and cloud-native approaches. Now, having a clear, full view of your systems is crucial. But achieving this insight goes beyond simple monitoring—it’s about observability.
Observability allows us to see not just what’s happening in our systems but also why issues like slow performance or errors arise. It sheds light on the hidden issues like bottlenecks and technical debt that can compromise system efficiency and growth. Combining insights from both the operational side and the architectural perspective helps teams identify and tackle root causes rather than just patching up symptoms.vFunction empowers teams to go beyond runtime monitoring by providing deep architectural insights. With patented tools that identify hidden dependencies, structural bottlenecks, and technical debt, vFunction enables you to fix root causes, not just symptoms. Simplify modernization, boost resilience, and scale with confidence. Ready to take your observability to the next level? Discover vFunction today!
Let’s start our comprehensive discussion of software architecture with an overview of the software design process. We’ll uncover key design patterns that define various application architectures and then explore the tools, best practices, and challenges faced in modern software architecture. Along the way, we’ll gain insights into the minds of the software architects who design these systems and help bring them to life.
Introduction to software architecture
Software architecture is the fundamental organization of a software system, encompassing the structures, components, and relationships that define how the system operates and evolves. At its core, software architecture provides a blueprint for software development, guiding how different parts of the system interact to fulfill both functional and non-functional requirements. This foundational organization is essential for ensuring that the software system is robust, scalable, and adaptable to change. By establishing clear architectural guidelines from the outset, development teams can create systems that are easier to maintain, extend, and optimize over time. Ultimately, a well-conceived architecture serves as the backbone of any successful software project, shaping the system’s behavior and supporting its long-term growth.
Importance of software architecture
The significance of software architecture extends far beyond initial design—it is a decisive factor in the overall success of a software system. A thoughtfully crafted architecture directly influences the system’s performance, reliability, and maintainability, making it easier for development teams to manage complexity and deliver high-quality results. Good architecture streamlines software development by providing clear structure and boundaries, which simplifies modifications and the addition of new features. It also enhances scalability and fault tolerance, ensuring the system can handle growth and recover gracefully from failures. Moreover, a strong architectural foundation reduces operational and maintenance costs by minimizing the need for extensive rework and enabling more efficient development processes. In essence, investing in sound software architecture pays dividends throughout the system’s lifecycle, supporting both immediate business needs and future innovation.
Software architecture design
Software architecture design determines how to build a software system that satisfies functional and non-functional requirements, balancing factors such as maintainability, effort, engineering velocity, resilience, robustness, scalability and operational costs. In the design phase, architects specify a multi-faceted blueprint for the software, much like building architects specify plans. This specification includes the software’s components and sub-components, interfaces and boundaries, interdependencies, the underlying technology stack, and the frameworks they use. It also outlines key data and control flows and constraints to address non-functional requirements such as response times, redundancy, and security controls.
Typically, the software development life cycle (SDLC) is carried out iteratively and incrementally on top of the same architectural foundation. As the system evolves with new capabilities and requirements, the architecture should remain stable, providing a consistent backbone for the evolving application.
Architectural changes typically incur considerable costs, as they involve system-wide rewrites with high risk of regressions and unexpected production issues. It’s essential to address requirements and anticipate the consequences of design decisions. Choosing the best software architecture pattern is crucial to the software’s performance, security, maintainability, and cost-effectiveness throughout its development and deployment. The many critical architectural choices made in the design phase justify the investment, as they often determine the final product’s success or failure.
Software architectural design patterns
Architects often rely on established software architecture patterns — proven solutions to common design challenges. These patterns offer blueprints for building adaptable systems while avoiding common pitfalls. Let’s explore some of the most renowned architectural design patterns.
Layered software architectural design
The layered pattern partitions an application into “horizontal layers,” each responsible for a category of functions. For example, the persistence layer manages reading and writing data to persistent storage (e.g., a database), while the presentation layer handles incoming API requests from external clients and routes them to lower layers for processing. Components within a layer may send requests to other components in the same or lower layers, but not to those in higher layers. A common layering for monolithic enterprise applications is the three-layer design, which defines presentation, application (or business) and data (or persistence) as the three standard layers. Some applications use four layers: presentation, business logic, services, and data.
The main advantage of a layered architecture is its separation of concerns via modularity. Developers can focus on building or modifying a component within a layer without considering the implementation details of lower layers or their usage by upper layers. This approach increases engineering velocity, maintainability, reliability and usability.
Let’s look at a typical example of a Java Spring application: “Order Management System” which we use in our vFunction modernization workshops. We call the presentation layer “web” and it contains a set of controller classes that receive REST API requests from clients. The requests are interpreted and processed by calling methods on objects in the service layer which calls APIs of external systems (e.g., to send emails) and uses the object in the persistence layer to read and write data from/to database tables.
vFunction’s Order Management System application demonstrates a typical three-layer architecture.
Layers deployed as independent runnable components are called tiers. Deploying layers as tiers can improve scalability and performance through horizontal scaling, where a load balancer manages multiple instances of the entire tier. For example, in a three-layer application with a resource intensive business logic layer, you can convert the layers into tiers and scale it horizontally. The presentation tier routes requests through a load balancer to the business logic tiers which then call the persistence tier.
Microservices in software design and architecture
In microservices architecture, developers build the application as a set of loosely coupled, independently developed and deployable services. Every service has its own APIs and manages a specific functional domain, such as inventory, a service to manage the product catalog, or shipments. Teams develop and deploy these services independently, enabling greater flexibility and scalability.
In this Order Management System example of microservices the partition is “vertical” where every service covers a functional domain, instead of partitioning the application “horizontally” in layers.
Microservices represent a “vertical separation of concerns” in contrast to the horizontal separation in layered architecture. Key benefits include:
1. Faster delivery and improved productivity. Teams deliver services independently enabling faster deployment of new domain-specific capabilities. 2. Accelerated development. Teams handle smaller code bases for quicker development and testing. 3. Efficient, scalability and performance. Scaling is domain specific, so teams can allocate computational resources where needed most. 4. Resilience. Services operate independently and are often fault tolerant. If deployed on platforms like Kubernetes, systems can auto-recover from service failures. 5. Technology flexibility. Every service can use a different tech stack, supporting cloud-native development. 6. Simplified debugging. Monitoring and auditing individual services make it easier to locate failures.
However, microservices also have drawbacks, including potential data inconsistencies, increased complexity due to distributed systems, network latencies, and end-to-end testing challenges.
A detailed description and comparison with monolithic applications can be found here.
Modular monoliths
A modular monolith is a software design approach where a monolithic application is divided into distinct, self-contained modules. Each module represents a specific functional domain and encapsulates its logic, data, and behavior, while the entire system is deployed as a single unit. The application is “vertically sliced” to implement a functional domain, much like microservices. This approach combines the simplicity of a monolith with the organization and separation of concerns found in microservices, without the added complexity of distributed systems.
Modular monoliths are relatively easy to manage because they deploy a single binary from one repository. They often deliver better performance and provide developers with clear visibility across the entire application.
Event-driven software architecture
Event-driven architecture (EDA) triggers behaviors and interactions through events, enabling flexible modification of system behavior. An event is a “significant occurrence” which might be a message reception, a user action, sensor reading, etc. In this architecture, producers generate and publish new events, event routers filter and push events to the appropriate components called “consumers” which perform the behavior as a reaction to the event.
Event driven architecture example.
To illustrate EDA let’s look at our Order Management System example. In a traditional architecture, creating a new order requires calling APIs to log the order, check inventory, charge the client, and more. In contrast, an EDA system publishes an “Order Created” event with the order details to an event broker. All components (payments, shipping, and inventory) receive this event and act on it independently.
EDA further decouples services in a microservices architecture, and is widely used in real-time systems such as IoT systems, chat applications, etc. and in monitoring and analytic systems, including anomaly detection. However, EDA adds complexity and overhead by mandating an event broker such as Apache Kafka, Active MQ, etc. and makes the overall flows harder to trace and debug.
Although not an exhaustive list of software architecture patterns, these cover the bulk of systems most software architects will design and interact with. Despite choosing a specific pattern, architects must follow best practices to guarantee the software’s reliability and scalability. Next, let’s take a look at these best practices.
Best practices in software architecture
Building software that lasts requires following best practices for quality, maintainability and scalability, as well as applying strategic thinking to ensure architectural decisions align with business goals. Here’s how to build software that stands the test of time:
Design for modularity. Embrace vertical and horizontal separation
Break your system into smaller, independent modules or services, each focused on a well-defined functional domain. Within every module or service, organize the components into layers. For a monolithic application, choose a modular monolith design. For a microservices architecture, split the components within the service into layers such as API, service and persistence.
Another type of separation is avoiding coupling between services or modules. Ensure flows are unidirectional, avoiding circular dependencies where A calls B and B calls A. Additionally, minimize cross-service or cross-module flows for a single business function to improve resilience as well as performance in the case of microservices.
Plan for the future
Design your architecture to handle heavier workloads, adopt new technologies, and adapt to evolving user behaviors. For example, even if your application is meant to run on-prem, avoid barriers to future cloud migration, like local file storage. Opt for standardized tools instead of vendor-specific ones like stored procedures, or application server specific features or code generation from vendor specific languages. Plan for growth with horizontal (add more servers) and vertical (upgrade existing hardware) scaling for the future.
Implementing software architecture best practices
Implementing software architecture effectively demands a collaborative approach and a dedicated architect to steer design decisions and maintain best practices. Utilizing established architectural patterns like microservices or event-driven architecture ensures consistency and problem-solving. Essential to this process is maintaining thorough documentation for clarity on design and system structure, enabling easier maintenance. A strong testing strategy, integrating automated and manual testing, detects issues promptly. Continuous integration and delivery (CI/CD) pipelines streamline and speed up development.
Avoid accumulating technical debt
As the system matures to support more capabilities, ensure the architecture adapts to changing requirements and maintains quality. Neglecting this leads to accumulating technical debt, which slows engineering velocity and degrades quality, eventually requiring partial or complete system rewrites.
To prevent this, regularly assess and address technical debt as part of the SDLC. Prioritize issues, schedule their remediation, and integrate this process into your development routine.
Scaling in software development architecture
Scalable systems need architectures that support expansion through:
– Horizontal scaling—adding servers or nodes to distribute load, and – Vertical scaling—enhancing existing hardware or software capabilities like memory and CPU.
Microservices and cloud-native architectures excel in vertical scaling due to their flexibility. Load balancing is crucial for managing traffic distribution across servers, enhancing performance, and ensuring high availability, often facilitated by reverse proxies or service meshes for optimal fault tolerance.
Security in software architecture design
To ensure software security, integrate security measures into the architecture from the outset. This means implementing robust authentication and authorization mechanisms, such as multi-factor authentication and role-based access control, to verify user identities and control access to resources. A defense-in-depth approach with multiple security layers like firewalls, intrusion detection systems, and encryption is essential to protect against a wide range of threats. In a microservices architecture, securing communication between services is critical, with API gateways, service meshes, and mutual TLS authentication playing key roles.
Tools and techniques for software architecture
You don’t have to build software architecture from scratch. A rich ecosystem of tools and techniques exists to help design, visualize, and manage software systems. Here are a few examples:
Software architecture modeling tools
Visualization is key to understanding complex systems. Software architecture tools, specifically modeling tools, help architects create visual representations of software architecture to communicate and analyze.
UML (Unified Modeling Language) is a standard diagrammatic language that provides a visual blueprint for software systems. It uses class, sequence, and component diagrams to illustrate system relationships and interactions, making architecture easier to understand and communicate. With 14 diagram types, UML offers diverse views of a system model. Tools like Microsoft Visio, Plant UML, Magic Draw, and Rhapsody support UML diagram creation, providing powerful platforms for visualizing architecture. Advanced UML tools such as Magic Draw and Rhapsody maintain consistency across diagrams, enforce rules and support simulations and analytics. While UML is widely used in regulated industries, its complexity and subtleties can make it challenging to master.
A more lightweight diagrammatic language is C4 which only has four types of diagrams (Context, Container, Component and Code). This language was developed by Simon Brown. Some tools, like PlantUML, support both UML and C4 diagrams. Another C4 modeling tool is IcePanel, which is a collaborative tool that helps developers create and share interactive diagrams, enhancing team communication and understanding.
While modeling tools provide the means to design software architecture, they verify that the implementation satisfies the architectural model. Some UML tools generate code from UML models, but this approach adds overhead, requiring teams to constantly update the model instead of directly modifying the code. Although many vendors promoted code-generation its complexity and additional effort led to limited adoption.
Other tools parse code to generate visual diagrams, such as UML class diagrams. While helpful for communication, these tools provide only a partial view, failing to verify alignment with the overall architectural design or identify architectural issues.
Monitoring and optimizing software architecture
Monitoring and optimizing software architecture is crucial for maintaining system health and performance. AI tools like vFunction visualize your architecture in runtime, identifying bottlenecks and optimizing for scalability and efficiency. vFunction helps maintain the health of both monoliths and microservices by providing deep architectural insights, automated analysis, and actionable recommendations. It identifies architectural drift and structural issues, such as technical debt and overly complex flows, as the architecture evolves, enabling teams to address them proactively. With real-time visualization and governance capabilities, vFunction helps maintain resilient, scalable architectures that stay aligned with best practices, ensuring applications remain high-performing and easy to manage over time.
Communication in software architectural design
Effective communication is key to successful software architecture. Architects must communicate their design to stakeholders, developers, and other team members clearly using concise language, visual aids, and comprehensive documentation. Tools like wikis, shared workspaces and design reviews can help with communication and collaboration.
Challenges in software architecture
Choosing and specifying an architecture is a non-trivial task with long-lasting implications and should ideally be done and reviewed by a team, not by a single person. Here are a some common challenges architects are likely to face:
1. Understanding the environment the application operates in
Business applications operate in complex environments and need to integrate with other systems.
Applications must integrate with existing systems through APIs, message queues, event buses, or specialized protocols while considering constraints and evolving infrastructure.
2. Modernizing legacy applications
Replacing legacy systems requires a deep understanding of their functional domains, interfaces, and data usage. Techniques like Domain-Driven Design (DDD) and Design Thinking can help, but insights into the existing system are critical. vFunction aids modernization by providing detailed insights and leveraging AI to analyze and optimize legacy architectures.
3. Non-functional requirements
Non-functional requirements may not be apparent during the analysis and design phase of the application — you may need to pivot after deploying to production. Scalability, response times, security, and operational costs often emerge post-deployment. Thoughtful design must happen before and as the system evolves, including mechanisms like redundancy, monitoring, and logging to address failures and prevent pitfalls such as microservices-related complexity.
4. Choosing the right technology stack and platform dependence
Architecture needs a balance between mature technologies that might be discontinued and emerging technologies that may be error prone and don’t have a well established user community. Design the architecture to be platform independent as much as possible by scoping the platform specific parts in well defined layers.
5. Defining functional domains
The various components of an application should align with functional domains, balancing granularity with simplicity. Overly complex topologies or shared resources can cause data integrity (race conditions) and performance issues, so precise scoping of domains is key for good architectural design.
6. Maintaining good architecture over time
Evolving applications often accumulate technical debt from delivery compromises or inconsistent implementation. Architects must monitor development and ensure adherence to the design. vFunction helps by providing architectural observability, identifying drift from the baseline, and addressing issues before they escalate or become so challenging that the application needs to be rewritten.
Future trends in software architecture
Software architecture evolves rapidly, requiring developers to stay current with emerging practices and technologies. Here are key trends shaping the future:
1. Serverless architecture
Serverless computing abstracts infrastructure management, enabling developers to focus on code and business logic. It offers scalability, cost-efficiency, and faster deployments but requires expertise in cloud platforms and comes with challenges like vendor lock-in, cold starts (latency in initializing functions), and debugging.
2. AI integration
AI, including Large Language Models (LLMs), is transforming architecture with tools for code generation, testing, and optimization. While promising, architects must address challenges like algorithmic bias and data privacy.
3. Edge computing
By processing data closer to its source, edge computing reduces latency for real-time applications like IoT and analytics. However, it introduces complexities in managing distributed infrastructure, ensuring data consistency, and security at the edge.
4. Increased emphasis on security
Architectures must embed security at every layer as cyber threats rise. Trends like DevSecOps (part of the “shift-left” movement, integrating security into the development process) and zero-trust (requiring strict identity verification, continuous authentication, and least-privilege access) are becoming critical security models throughout architecture and design.
5. Sustainable software engineering
Growing ecological concerns push architects to design energy-efficient, resource-optimized applications that minimize environmental impact, especially in AI-driven systems. Ecological advantages can be integrated from the outset, shaping how software is designed, coded, and deployed. Software architects are increasingly adopting cutting-edge trends at the heart of their work, which are becoming more accessible over time.
How vFunction helps with software architecture
vFunction is an AI-driven architectural observability platform that monitors software architecture, detects architectural drift and provides actionable tasks (TODO’s) for developers to address architectural issues.
The platform analyzes distributed and monolithic applications. Here are several ways vFunction can assist you in enhancing software architecture management, boosting developer productivity, and delivering superior software.
vFunction helps structure and manage distributed applications.
Architectural observability
vFunction provides a real-time, visual view of software architecture. It uses patented static and dynamic analysis methods powered by AI to automatically untangle unnecessary dependencies across business domains, services, database calls, classes, and other resources. This visibility helps architects and developers understand interactions, identify bottlenecks, and plan changes effectively.
vFunction’s spheres represent different business domains within applications, with colors indicating their level of exclusivity: green spheres are highly exclusive and easier to extract. In contrast, red spheres are less exclusive and more challenging to extract.
Automated refactoring and decomposition
vFunction automates the decomposition of monolithic applications into microservices by identifying and extracting business capabilities. This breaks down complex, tightly coupled systems into smaller, more manageable, and independently deployable units. Its refactoring solutions help architects and developers scale more effectively and achieve better application performance.
Technical debt management
To address accumulating technical debt, vFunction continuously tracks architectural events, such as new dependencies, domain changes, and increasing complexity over time. The vFunction platform measures an application’s technical debt using defined architectural issues, allowing architects and engineers to pinpoint areas for optimization.
Microservices governance
vFunction’s architecture governance sets guardrails that empower teams to maintain control over their microservices architecture by providing real-time visibility, automated rule enforcement, and actionable insights. It helps prevent architectural drift, microservices sprawl, and technical debt by monitoring service boundaries, identifying complex flows, and ensuring compliance with best practices.
Final thoughts
Software architecture is critical for creating scalable, secure, and maintainable applications. By leveraging proven patterns and best practices, companies can design flexible, high-performing systems that seamlessly adapt to evolving demands.
With vFunction’s architectural observability platform, engineers and architects can transform legacy applications into cloud-native architectures in weeks rather than months or years. Beyond modernization, vFunction empowers teams to manage and govern microservices by offering real-time visibility, monitoring architectural drift, and enforcing best practices to prevent sprawl and complexity. By prioritizing software architecture, organizations ensure their applications remain resilient, scalable, and prepared to meet future demands.
Contact us to discover how vFunction can help manage and modernize your software architecture to support best practices and better business outcomes.
There are two primary types of code analysis: static and dynamic. Both are essential in software development, helping to identify vulnerabilities, enhance code quality, and mitigate risks. Static code analysis automates source code scanning without the need for code execution. It scrutinizes the source code before execution, focusing on structural integrity, adherence to standards, and potential security flaws. Static and dynamic code analysis are critical for maintaining an application’s security by identifying vulnerabilities and ensuring compliance with strict security and privacy standards.
Make sure your applications have comprehensive static and dynamic analysis with vFunction.
In contrast, dynamic analysis evaluates software behavior during runtime, revealing performance bottlenecks and vulnerabilities that only occur during execution. By understanding the nuances of these complementary techniques, developers can make informed choices about tools, integration, and best practices, ultimately creating robust, reliable, and secure software.
Introduction to code analysis
Code analysis is fundamental in the software development lifecycle, ensuring high-quality, secure, and reliable software. By analyzing code for potential issues, code analysis is a preventative measure, catching errors and vulnerabilities before they become significant issues. This proactive approach not only enhances the overall quality of the software but also reduces the time and cost associated with fixing problems later in the development process.
Static code analysis involves examining the source code without its execution. It offers a proactive means to identify and rectify issues early in the development lifecycle. Integrating static code analysis into the code review process allows teams to systematically evaluate code quality and security, ensuring that issues are identified and addressed in a structured manner. It analyzes the application source code for adherence to coding standards for better readability and maintenance, syntactic and semantic correctness, and potential vulnerabilities. Additionally, it is typically paired with software composition analysis (SCA). SCA tools scrutinize the third-party components for possible vulnerabilities, compliance, versioning, and dependency management.
Dynamic analysis evaluates the software’s behavior during runtime, providing valuable insights into runtime errors, potential security vulnerabilities, performance, and interactions with external systems that might not be apparent during static analysis.
By understanding these two complementary approaches, developers can choose the right tools and techniques to ensure their software meets the highest quality and security standards. Employing a variety of testing methods, including both static and dynamic analysis, is essential for comprehensive coverage and for meeting compliance requirements.
Understanding static code analysis
What is static code analysis?
Static code analysis examines software source code without execution, contributing significantly to technical debt management. Often integrated into the development workflow, this process is an automated method for comprehensive code review. Static analysis tools systematically scan the codebase, identifying potential issues ranging from coding standards violations to security vulnerabilities.
These tools promote clean, readable, and maintainable code by flagging coding style and conventions inconsistencies. They can identify issues such as inconsistent indentation, unused variables, commented-out code that affects readability, and overly complex functions. Static analysis tools are crucial in identifying potential security weaknesses like input validation errors, insecure data handling, or hard-coded credentials. Discovering these vulnerabilities early in the development cycle allows for timely mitigation. These tools can detect logical errors, including infinite loops, unreachable code, or incorrect use of conditional statements, which could lead to unexpected behavior or system failures.
Benefits and limitations of static code analysis
Like any tool, a static code analysis tool has its limitations, and understanding these is crucial to maximizing its effectiveness. Here are a few key benefits and limitations:
Benefits
Limitations
Early bug detection: Identifies issues early in development, preventing them from becoming more complex and costly.
False positives and negatives: May flag issues that are not actual problems and, conversely, may not detect issues arising from the runtime context, requiring manual review.
Improved code quality: Enforces coding standards and best practices, leading to a more consistent, readable, repeatable, and maintainable codebase.
Focus on code structure: Primarily analyzes code structure and syntax, not runtime behavior.
Enhanced security: Uncovers security vulnerabilities before deployment, reducing the risk of compromise.
Limited scope: Cannot detect runtime vulnerabilities like memory leaks or race conditions.
Developer education: Detailed explanations of detected issues, facilitating learning and skill improvement.
Automated feedback: Offers rapid and automatic feedback integrated into the development environment, allowing immediate fixes.
Understanding dynamic analysis
What is dynamic code analysis?
Dynamic code analysis involves testing software while it is running to uncover vulnerabilities, performance issues, and other problems that only become apparent during execution. This approach includes various types of analysis, including Dynamic Application Security Testing (DAST), performance testing, memory analysis, concurrency testing, and runtime error detection. By interacting with the application in real-time and simulating actual operating conditions, dynamic analysis provides insights that are difficult to obtain through static analysis alone. It excels at identifying security vulnerabilities, performance bottlenecks, resource management issues, and concurrency problems that might not be apparent just by looking at code statically.
Benefits and limitations of dynamic code analysis
It’s important to consider both the advantages and disadvantages of dynamic code analysis to get the most from integrating it into your development process. Here’s a breakdown of the key benefits and limitations:
Benefits
Limitations
Identification of runtime issues: Excels at detecting problems that only surface during execution, such as memory leaks, race conditions, or performance bottlenecks.
Incomplete coverage: Only detects issues in code paths executed during testing, potentially missing problems in unexercised areas.
Realistic testing: Analyzes software in a real or simulated environment with all the integrations and data in place to validate production functional performance.
Resource intensive: This can require significant computing power and time for thorough testing.
Improved software performance: Pinpoints bottlenecks and inefficiencies for optimization.
Setup complexity: Establishing a realistic test environment can be challenging.
Enhanced security:Effectively identifies security issues concerning parameters that are available during runtime only, like user input, authentication, data processing, and session management.
Code coverage limitations: Dynamic analysis may result in incomplete code coverage, as it only measures the extent of source code tested during execution, leaving some parts untested.
Static vs dynamic code analysis: Key differences
Both static and dynamic code analysis are valuable tools for developers, offering unique perspectives on improving software quality and security. While they aim to identify and resolve issues, their approaches differ significantly. Understanding these key differences is essential for selecting the right tools and strategies for your development needs.
Timing of analysis
You can perform static code analysis as soon as you commit the first line of code because a running codebase is not required to begin testing and analyzing the application. Conversely, dynamic code analysis requires the code to be running, meaning it cannot provide insights until you execute the code. Therefore, dynamic analysis is typically conducted later in the development process, once an application has taken shape.
Execution
Static code analysis does not require code execution. It examines the source code, looking for patterns, structures, and potential issues.
Dynamic code analysis, however, necessitates code execution. It works on a more “black-box” paradigm that is unconcerned with the internal implementation or code structure. It observes how the software behaves during runtime, monitoring factors such as memory usage, performance, and interaction with external systems.
Detection of issues
Static code analysis primarily detects issues related to coding standards violations, potential security vulnerabilities, and logical errors in the code structure. Developers can often identify these issues by examining the code without executing it. Dynamic code analysis detects problems that only occur when the code runs, including memory leaks, performance bottlenecks, and runtime security vulnerabilities.
Dead Code
Static code analysis detects unreachable classes and functions from the entire codebase. This well-understood practice guides developers to simply delete dead code from the codebase. However, dynamic analysis can detect dead code in the context of specific application flows. This can help remove unnecessary dependencies from specific classes and methods. The context-specific dead code adds complexity to the application and is often mistakenly overlooked.
Understanding the differences between static and dynamic analysis enables developers to choose the right tools and techniques to ensure their software’s quality, security, and performance throughout the entire development lifecycle.
Code quality considerations
Ensuring high code quality is fundamental to successful software development. Code quality encompasses factors such as adherence to coding standards, readability, maintainability, and performance. Static code analysis tools are instrumental in upholding these standards by automatically detecting coding errors, enforcing best practices, and highlighting code smells that could lead to future problems. These tools help teams maintain a clean and consistent codebase, making it easier to manage and extend over time.
Dynamic code analysis tools further enhance code quality by identifying runtime errors, memory leaks, and performance bottlenecks that may not be apparent from static inspection alone. By combining static and dynamic code analysis, software teams gain a comprehensive view of both the structure and behavior of their code. This holistic approach ensures that code not only meets technical requirements but is also robust, efficient, and maintainable throughout the software development process.
Ultimately, integrating static and dynamic code analysis into development workflows empowers teams to deliver higher-quality software, reduce technical debt, and respond more effectively to changing requirements.
Early detection of issues
Early detection of issues is a cornerstone of efficient and cost-effective software development. Static code analysis enables software teams to identify potential problems—such as security vulnerabilities, logic errors, or non-compliance with coding standards—before the code is ever executed. This proactive approach allows developers to address issues at the earliest possible stage, minimizing the risk of costly rework later in the development process.
Dynamic code analysis also contributes to early detection by uncovering runtime errors and unexpected behaviors as soon as the code is executed in a test environment. By integrating both static and dynamic code analysis into the development process, teams can catch defects before they escalate into major problems, reducing the time and resources required for debugging and remediation.
Prioritizing early detection not only streamlines the development process but also leads to higher-quality, more reliable software releases, giving software teams a competitive edge.
Complementary techniques for code analysis
Static and dynamic code analysis are most powerful when used together as complementary techniques. Static code analysis excels at identifying coding errors, compliance violations, and security flaws within the source code, while dynamic code analysis uncovers runtime errors, memory leaks, and performance bottlenecks that only emerge during execution. By combining static and dynamic code analysis, software teams achieve comprehensive coverage, ensuring that both code structure and runtime behavior are thoroughly examined.
Beyond these foundational methods, additional application security testing techniques—such as interactive application security testing (IAST) and dynamic application security testing (DAST)—can further strengthen a project’s security posture. IAST tools analyze code in real time as it runs, providing detailed insights into vulnerabilities and system interactions. DAST tools simulate attacks on running applications to identify security risks that may not be visible through static analysis alone.
By leveraging these complementary techniques, software teams can identify vulnerabilities, coding errors, compliance violations, and security risks more effectively, resulting in more secure and resilient applications.
False positives in code analysis
False positives are a common challenge in code analysis, occurring when a tool incorrectly flags code as defective or vulnerable. These inaccuracies can lead to wasted time and effort as software teams investigate and attempt to fix issues that do not actually exist. Static code analysis tools, in particular, may generate false positives if not properly configured or tailored to the specific codebase. Similarly, dynamic code analysis tools can produce misleading results if the testing environment does not accurately reflect production conditions or if the tools are not finely tuned.
To minimize false positives, it’s important for software teams to carefully configure and calibrate their static code analysis tools and dynamic analysis tools. Using multiple code analysis techniques and cross-validating results can help distinguish real issues from false alarms. Regularly reviewing and updating tool configurations, as well as incorporating feedback from manual code reviews, further enhances accuracy.
By reducing false positives, teams can focus their efforts on genuine issues, making their code analysis processes more efficient and ensuring that resources are directed toward improving actual code quality and security.
Choosing the right tool for code analysis
Choosing appropriate code analysis tools is essential to maximizing the effectiveness of the software development process. With many options available, it’s essential to consider several factors before deciding on a tool or multiple tools for code analysis.
Factors to consider when selecting a tool
Type of analysis required: Determine whether you need static, dynamic, or both types of analysis. Some tools offer comprehensive solutions combining both approaches, while others specialize in one or the other.
Areas of improvement: While static and dynamic analysis are general concepts, different tools have different focus areas. Some may focus on security, while others may focus on performance. An often overlooked area is application complexity, which greatly hinders the engineering velocity, scalability, and resilience of your application.Prioritize your focus area and choose a corresponding tool.
Programming languages and platforms supported: Ensure the tool is compatible with the languages and platforms used in your projects. Compatibility issues can hinder the tool’s effectiveness and integration into your workflow.
Integration with existing development tools and workflows: Choose a tool that integrates well with your existing development environment, such as your IDE (Integrated Development Environment), CI/CD pipeline, or version control system.
Cost and resource requirements: Evaluate the cost of the tool, including licensing fees, maintenance costs, and any potential hardware or infrastructure requirements. Consider your budget and resource constraints when making your choice.
Popular tools for static and dynamic analysis
There are plenty of tools available for static and dynamic code analysis. Below, we will look at a few of the most popular in each category to get you started on your research.
Static code analysis tools:
SonarQube: A widely used open-source platform for continuous code quality inspection, supporting multiple languages and offering a rich set of features.
CodeSonar: A commercial tool specializing in deep static analysis, particularly effective for identifying complex security vulnerabilities.
DeepSource: A cloud-based static analysis tool that integrates seamlessly with GitHub and GitLab, providing actionable feedback on code quality and security.
Pylint (Python): A widely used static analyzer for Python code, checking for errors, coding standards compliance, and potential issues.
Dynamic code analysis tools
New Relic: A comprehensive observability platform that provides real-time insights into application performance, infrastructure health, and customer experience.
AppDynamics: A powerful application performance monitoring (APM) tool that helps you identify and resolve performance bottlenecks and errors.
Dynatrace: An AI-powered observability platform that provides deep insights into application behavior, user experience, and infrastructure performance.
Dynamic and static code analysis tools
vFunction: A pioneer of AI-driven architectural observability, vFunction uses its patented methods of static and dynamic analysis to deliver deep insights into application structures to identify and address software challenges.
Fortify: A range of static and dynamic analysis tools with a focus on software vulnerabilities
Veracode: Another popular commercial suite of products focusing on application security
This list will give developers and architects a good spot to start, with many of these tools leading the pack in functionality and effectiveness. That being said, when it comes to judging a tool’s effectiveness, there are a few factors we can hone in on, which we will cover in the next section.
Evaluating tool effectiveness
When selecting code analysis tools, it is crucial to assess their capabilities through several key metrics. Accuracy is critical; the tool should reliably identify genuine issues while minimizing false positives. A high false-positive rate can be frustrating and time-consuming, leading to unnecessary manual reviews.
Another significant factor is the ease of use. The tool should have an intuitive interface with transparent reporting, making it easy for developers to understand and act on the analysis results. Consider how well the tool integrates into your existing workflow and whether it provides actionable recommendations for fixing identified issues.
Finally, focus on the tool’s performance in detecting the specific types of issues that are most relevant to your projects. Some tools specialize in security vulnerabilities, while others may be better suited for finding performance bottlenecks or code smells. Evaluate the tool’s strengths and weaknesses with your specific needs in mind to make an informed decision about which tool fits best.
Implementing code analysis in your workflow
Seamless integration of code analysis tools is critical to optimizing your development process. Start by automating static analysis by incorporating it into your CI/CD pipeline and using IDE plugins. This allows for automatic scans whenever you make changes, providing rapid feedback and catching issues early in the development cycle.
In addition, schedule static analysis scans regularly throughout the project’s lifecycle to ensure ongoing code quality and security. Complement these automated checks with dynamic analysis during functional testing and on production deployments to gain deeper insights into runtime behavior. Observing your software during runtime can uncover performance bottlenecks, memory leaks, and vulnerabilities that may not be apparent in static code alone.
Combining static and dynamic analysis creates a comprehensive quality assurance process. This approach allows for early issue detection, performance optimization, and robust security measures, resulting in more reliable and resilient applications.
Best practices for code analysis
Integrating code analysis early and consistently into your development workflow is crucial to maximize effectiveness. Start by automating scans to catch issues promptly, preventing them from becoming more significant and complex. Prioritize addressing critical vulnerabilities and high-impact bugs, utilizing tools to assess severity and streamline your remediation efforts.
It’s also important to make code analysis an ongoing process. Continuously monitor code quality and security trends to identify and mitigate potential problems proactively. Leverage static and dynamic analysis for comprehensive coverage, ensuring you thoroughly examine code structure and runtime behavior.
Choose tools that align with your technology stack and prioritize accuracy, low false-positive rates, and ease of use. Customize analysis rules to your project’s needs and educate your team on properly using and interpreting the tools’ results. Code analysis tools are just one part of a robust quality assurance process. Implementing manual code reviews, thorough testing, and a commitment to continuous improvement are equally important.
Using vFunction for dynamic and static code analysis
vFunction provides patented static and dynamic code analysis to give architects and developers insights into their application’s inner workings. It is the only platform with a focus on application architecture to support scalable and resilient microservices and efficiently modernize legacy applications.
Dynamic analysis with vFunction
During runtime, vFunction observes your application in action, capturing valuable data on how components interact, dependencies between classes, and resource utilization patterns.
vFunction uses AI and dynamic analysis to understand and map application domains and their dependencies during runtime, represented by spheres and connections in image one. A deeper dive visualizes “entrypoint” methods that form domain boundaries and corresponding runtime interactions with a call tree.
This dynamic analysis helps vFunction understand the actual behavior of your application, revealing hidden complexities and potential bottlenecks.
Static analysis with vFunction
vFunction complements its dynamic analysis with a deep dive into the static structure of your code. By analyzing the codebase, vFunction identifies architectural issues, technical debt, and areas in need of application modernization.
Static code analysis is much easier to interpret as it is viewed inside bounded contexts in vFunction using automation.
This dual approach gives vFunction a comprehensive understanding of your application, allowing it to make intelligent decisions about effectively decomposing it into microservices and keeping existing microservices running smoothly.
Conclusion
Static and dynamic code analysis are essential to a comprehensive software development strategy. Understanding and effectively integrating their strengths and limitations into your workflow can significantly enhance software quality, security, and performance.
For organizations seeking to modernize legacy applications and maintain modern microservices, vFunction offers a unique solution that leverages advanced static and dynamic code analysis and automated refactoring capabilities. With vFunction’s architectural observability platform, architects and developers can unlock the full potential of their legacy systems and modern cloud-based applications to ensure their software remains relevant and competitive.
Get comprehensive coverage for static and dynamic analysis with vFunction.
Monolithic Java apps, often 20 years old or more, were never designed to run or leverage the cloud. As the technology scene takes shape, companies need to modernize and migrate their Java apps to make them suitable for the cloud.
Are you looking to make changes to your legacy apps and systems easier, faster, and more frequent? Moving them to a cloud-native architecture is the first step in the right direction. Cloud-native solutions are also more agile, cost-efficient, and easier to scale. However, you will need to conduct an application assessment for cloud migration before getting down to business.
While the benefits of cloud architectures are available for all to see, many organizations lack the tools or time needed to move their applications to the cloud. Organizations that fail to accurately assess and plan their application migrations to the cloud end up with a time-consuming and error-prone process.
Large enterprises (automotive, retail, insurance, and banking) have hundreds, sometimes thousands of these apps lying in data centers using old architectures. Building a comprehensive inventory of IT assets—including applications, infrastructure, and dependencies—is a foundational step in the assessment process. Moving these applications to the cloud lets you maintain or improve their business value and eliminate growing technical debt. Portfolio analysis is essential to map out and prioritize which applications should be migrated, ensuring alignment with business goals and effective migration planning.
Introduction to cloud migration
Cloud migration is the process of transferring an organization’s digital assets—including applications, data, and entire infrastructures—from on-premises environments to a cloud environment. This transition is a foundational step for businesses seeking to modernize their IT operations and leverage the flexibility and scalability of cloud technologies. A successful migration can deliver significant cost savings, improved performance, and enhanced agility, enabling organizations to respond quickly to changing business needs.
However, cloud migration is not without its challenges. Potential risks such as data loss, security breaches, and operational downtime must be carefully managed. That’s why a thorough cloud migration assessment is essential before embarking on any migration strategy. This assessment helps organizations identify which assets are suitable for migration, evaluate the best migration approach, and proactively address potential risks. By investing time in a comprehensive assessment, businesses can lay the groundwork for a successful migration and maximize the benefits of their new cloud environment.
Why should you conduct a Cloud Migration Assessment for Cloud Migration?
Before you start your cloud migration initiatives, you’ll need to assess your current application ecosystem. Application assessments are essential to help you determine the apps that you should move to the cloud and those that will remain in your legacy environment. Generally, some applications are more suitable for cloud architectures compared to others.
Using a data-driven and analytical approach, your organization can determine the relative difficulty in migrating an app to the cloud with a cost-benefit analysis. You can also assess the business value of making such a move. Additionally, the assessment identifies key aspects such as application code, cost implications, and overall feasibility for migration. Besides, a rigorous assessment lays the groundwork for an automated and efficient transformation.
In addition to application modernization, cloud migrations assessments typically assess infrastructure and integration with other applications or data sources. Prepping this in advance will help sequence migrations so you know what happens if you migrate an application to the cloud and the other interconnected apps remain in-house. Your assessment team should come into the process with service level agreements (SLAs), service level response time, and performance requirements as part of the planning process. Service level agreements SLAs are a key consideration when planning migration requirements, as they define acceptable performance and availability standards for your workloads.
Pre-migration planning is as critical as the implementation processes. The assessment phase is a critical first step in the migration journey. Initial planning should include a comprehensive understanding of existing IT assets and dependencies. This essential first step provides a solid footing before you proceed with your cloud migration plans. The idea behind these assessments is to ensure that you minimize the impact on your operations once you begin the migration process
How, then, should you conduct an application assessment for cloud migration?
Identify your Business Objectives
It is always necessary to understand why you want to implement a cloud migration initiative before starting the process. Failing to have clear objectives can derail your migration plans. Some of the typical business objectives for cloud migration include:
Accelerating business agility
Improving scalability
Minimizing maintenance costs from in-house data centers
Enhancing your failover capacity to boost app resilience
Enhancing remote collaboration
Ensure that all your team members are on board with your cloud migration objectives. Once you are clear about why you need to move to the cloud, you’ll have an easier time deciding which applications you should migrate.
Besides, it would help if you had well-defined and realistic goals before making decisions concerning other migration processes. Your initial goals will help you select an appropriate cloud service provider and settle on the ideal migration strategy.
Identify Business Transactions
An essential discovery method consults with the business to find individual business transactions. Then, your team can document your app’s end-to-end flow within your IT infrastructure before mapping your business requirements. Remember to enter the value of different technological considerations that support your various applications.
While assessing your business transactions, you’ll need to identify end-users, transaction characteristics, and response time sensitivity. Ensure that you evaluate each transaction that has a unique flow through your IT systems. Migration complexity will depend on the different available interfaces, the functional requirements, and the integration standards. Understanding specific business transactions usually helps to reveal performance requirements. Collecting performance data for these transactions is essential to accurately assess their suitability for cloud migration.
Remember also to list and prioritize your applications, indicating both their business purpose and use case. Highlight applications that you may choose to retire and those that you’ve already deployed in the cloud.
Assess the Response Time Impact Risks
Your application assessment for cloud migration should also evaluate the impacts of migrating your apps. Assess the response characteristics of integration and sensitivity to delay. Assign all your transactions a rating that factors in sensitivity to delay and business importance.
If you rank any of your transactions as high importance and sensitive to delay, you’ll label them as high impact risk. With this information, you can test the response time impact, especially for more sensitive transactions. Assessing the response time impact ensures that your team can easily quantify the potential impact of changes while identifying potential migration opportunities.
Additionally, automated cloud migration solutions will help you prioritize your apps based on this data and information. Your team will quickly seamlessly assess the modernization complexity of your legacy apps and help you make a data-driven decision. From the assessment reports, you’ll easily determine the readiness and ease of modernization for your apps.
Ideally, you’ll need to establish a baseline of current transaction response times and then model the changes that occur once you switch the application and network conditions. It is also crucial to evaluate network connectivity and communication patterns to identify potential bottlenecks or issues that may arise during migration. Always prioritize real-time synchronous applications and those you consider response time-sensitive. Once you identify areas of concern, create an elaborate testing plan that focuses on assessing network flows of concern. While such pre-migration evaluations may involve lots of time and resources, you’ll make decisions based on credible information. Ultimately, you’ll understand how things work and make discoveries that can change pre-formed conclusions regarding your cloud migration initiatives
Assess Infrastructure and Security Requirements
Your organization can predict its cloud environment needs once you are clear about the applications you want to move to the cloud. You might need to change specific applications to ensure that they suit your new architecture. Depending on your preference, you may adopt minor changes to an application’s core or completely rebuild the application. Alternatively, you might settle for software-as-a-service (SaaS) applications.
While at it, you may need to look at the security component of cloud applications. Government and industry regulations often take a prominent role when it comes to your cloud security posture. It is good practice to know where you have stored your data resources. This information helps you identify your security needs for different data sets.
Cloud experts suggest that organizations can exploit specific data securing approaches, including:
Data encryption
Monitoring and controlling access management
Web application firewalls
Setting up disaster recovery mechanisms
From your assessment, you can compare your requirements with your cloud service provider’s security offerings. If your organization runs in a specific sector like insurance or healthcare, you may need a niche provider who’ll support particular compliance requirements, like HIPAA. You may also want to identify potential vulnerabilities and make plans that address and mitigate those risks.
Evaluate Available In-house Resources
When considering available resources, you may want to answer a few questions, including:
How much money are you willing to set aside?
Is your IT team well-equipped for cloud migration?
Does your team have enough availability to focus on your migration initiatives?
Do you have any competing priorities at the moment?
Evaluate the skill-set of your in-house team with regard to cloud migration. It is essential to determine if your team has the required skills to guarantee a smooth cloud transition. Identify their weak points and educate them accordingly.
Empower your IT staff with relevant cloud migration skills to ensure that you apply best practices during this initiative. You can consider setting up a training program that educates your IT team. Alternatively, you can source for a strategic partner to help your team with the end-to-end execution of the migration process.
While assessing your resources, you may want to know if your IT team has any prior experience in cloud migration initiatives. Be sure also to evaluate whether you have the necessary migration tools and technologies.
Your in-house team understands your unique environment and specific needs. Empowering them adequately ensures that you implement a customized cloud environment. However, you could also opt to outsource your cloud migration services. A seasoned expert will fill the resource and knowledge gaps, albeit at an additional cost.
Classify Data
Determine sensitive data points, looking at the risk and corruption damage. Rank your data based on the risk factor, identifying those prone to deletion, intellectual property, and competitive theft.
Determine Migration Requirements and Compliance
Work with your tech department to determine all your top business and technical goals for cloud-based applications. Ensure that you are clear about any compliance requirements while figuring out how you’ll minimize licensing costs.
A rigorous assessment will deliver insights needed to make more informed strategic decisions regarding cloud migration. Evaluating your existing applications is often considerably time-consuming. The time you’ll spend on your assessments will depend on:
Internal technical capabilities
Quantity and complexity of legacy applications
Available budgets
Cloud Environment Selection
Selecting the right cloud environment is a pivotal decision in the cloud migration process. The choice of cloud provider can have a lasting impact on your organization’s ability to scale, secure, and manage workloads efficiently. When evaluating cloud providers such as Google Cloud Platform, Amazon Web Services (AWS), or Microsoft Azure, it’s important to consider factors like scalability, security features, compliance with industry regulations, and overall cost structure.
A thorough cloud migration assessment will help you match your organization’s requirements with the offerings of each cloud platform. For example, some providers may offer advanced analytics, managed services, or specialized compliance certifications that align with your business objectives. Additionally, consider the ease of integration with your existing systems and the level of support available. By carefully assessing these factors, you can select a cloud environment that not only supports a successful migration but also positions your organization for long-term growth and innovation.
Calculating Total Cost of Ownership
Understanding the total cost of ownership (TCO) is crucial for any cloud migration initiative. TCO encompasses all costs associated with the migration process, including initial migration expenses, ongoing operational costs, licensing fees, and any potential costs related to downtime or data loss. Accurately estimating TCO enables organizations to make informed decisions about their migration strategy and ensures that the move to the cloud delivers the expected cost efficiency.
A thorough cloud migration assessment will help you identify all relevant cost factors, such as resource allocation, infrastructure requirements, and licensing costs. It’s important to account for both direct and indirect expenses, including training, support, and potential changes in operational processes. By evaluating these elements early in the migration process, you can develop a realistic budget, avoid unexpected expenses, and ensure that your cloud migration delivers measurable business value.
Assess your Operational Readiness
Although cloud operations deliver cutting-edge tools and technologies, you may face significant challenges once you implement these solutions. Therefore, it is essential to determine how you will manage the cloud’s operational aspects.
Brainstorm your preferred operational model for deployment. You can also discuss and agree with the various stakeholders regarding the different roles, responsibilities, and operating models. It may also be helpful to craft operational best practices as well as a robust plan that addresses business continuity or disaster recovery.
Proper assessments will put you in an excellent place to handle the post-migration cloud operations. This information also allows you to decide whether you’ll need to leverage a cloud-managed service provider to manage your cloud operation tasks for your organization.
Determine Your Timeline and Budget
Settling on an ideal timeline to complete the migration and determine the associated costs can be challenging for many organizations. Create reasonable timelines for specific milestones that you intend to hit during the cloud migration process. Timelines ensure that you track the progress you’ve made while outlining the reasons for any setbacks along the way. Be sure to consider your application migration methodology when setting your timelines.
Your budget, on the other hand, should align with your migration needs. Remember to include your ownership calculations, migration labor, migration training, and licensing costs. The total cost of ownership of the cloud typically determines your overall budget. To resolve this amount, you’ll need to factor in:
The average resource unit size
Monthly usage
Estimated workload growth rate
Security requirements
Infrastructure overhead and management requirements.
Without adequate planning, your cloud networks may surpass the costs you incur when running your on-premise infrastructure. You also run the risk of exceeding your estimated timelines.
Choosing Migration Tools
Selecting the right migration tools is a key factor in achieving a successful cloud migration. The right tools can automate complex tasks, streamline the migration process, and help minimize risks such as data loss or extended downtime. When evaluating migration tools, consider their compatibility with your existing systems, scalability to handle your workloads, and built-in security features.
Dependency mapping tools are invaluable for visualizing application interdependencies and planning migration waves. Application performance monitoring tools provide real-time insights into system health before, during, and after migration, ensuring that performance standards are maintained. Cloud readiness assessment tools help evaluate your current environment and identify potential challenges ahead of time. By leveraging these tools, organizations can enhance their migration readiness, minimize risks, and set the stage for a smooth and successful cloud migration.
Experimenting and Designing Proofs of Concept
Experimenting with cloud technologies and designing proofs of concept (POCs) are essential steps in the cloud migration process. POCs allow organizations to test their migration strategy on a small scale, validate assumptions, and identify potential risks before committing to a full-scale migration. This iterative approach enables teams to refine their migration process, address unforeseen challenges, and build confidence in their chosen cloud technologies.
A thorough cloud migration assessment will inform the design of effective POCs by highlighting critical workloads, integration points, and performance requirements. Best practices for POCs include setting clear objectives, measuring key performance indicators, and involving stakeholders from both IT and business units. By experimenting with different migration strategies and cloud services in a controlled environment, organizations can ensure a successful migration and accelerate their overall cloud adoption journey.
Do you need to outsource your Cloud Migration Assessments?
Your application assessment for cloud migration goes beyond simply knowing your operating systems and server platforms. Working with an experienced service provider offers access to discovery and analysis services that will better inform your decision-making processes. Besides, you’ll receive data-driven insights necessary to re-platform your applications for the cloud.
An ideal provider will help you analyze your apps and technologies. You’ll also receive insightful data that highlights your privacy requirements, operational costs, and business value. Outsourcing these assessments also helps you appreciate the health of your portfolio.
You’ll also have an easier time identifying the workloads and platforms that require early transitions. Ensure that your service provider works with your in house team to create a plan that prioritizes workloads that have the highest impact on your operations. Ideal service providers will also generate reports from your cloud assessment data.
Ace your Cloud Migration Application Assessments
An application assessment for cloud migration is essential for achieving a smooth migration process. With a comprehensive evaluation, you’ll have an easier time setting up a cloud environment that matches your organization needs and requirements. Formulating a clear plan for your cloud migration assessment helps you minimize the time and money you’ll spend while migrating.
Different cloud providers deliver tools and services needed to help you assess your applications for cloud migrations. vFunction’s cloud readiness assessments help you gain a clear understanding of your current IT infrastructure. We’ll also help you identify potential pitfalls that you can expect during your cloud migration.
Count on us to help you meticulously plan the steps that you’ll need to guarantee the success of your migration initiative. We’ll deploy robust solutions that help you eliminate the time, risk, and cost constraints associated with modernizing business applications. Do you want to learn more? Request a free demo today!
If you are a developer or architect, chances are you have either heard of or are using microservices within your applications stack. With the versatility and benefits the microservices offer, it’s no surprise that development teams have made microservices a mainstay of modern applications, particularly within the Java ecosystem. Instead of constructing monolithic applications with tightly coupled components, microservices promote breaking down an application into smaller, independent, focused services.
Learn how vFunction supports and accelerates your monolith-to-microservices journey.
Adopting a microservices architecture supports modern applications in many ways, with two of the big highlights being enhanced scalability and improved application resilience. As two essential factors in performant applications and with the demands of modern users, using microservices and leveraging the benefits they bring can be an indispensable tool for architects and developers.
If you’re a Java architect or developer seeking to grasp the essence of microservices, their advantages, challenges, and how to build them effectively, you’ve come to the right place. In this blog, we will cover all of the basics and then jump straight into examples of how to build microservices with various Java frameworks. Let’s begin by looking at what a microservice is in more detail.
What are microservices?
Microservices are an architectural approach that brings a different outcome than traditional monolithic applications. Like other approaches to implementing a service-oriented architecture, Instead of building a single, large unit, microservices advocate for decomposing applications into smaller, independently deployable services. Microservices communicate with one another using lightweight protocols such as REST (REpresentational State Transfer) or gRPC (general Remote Procedure Calls). This means that each microservice focuses on a specific business domain or capability, simplifying development, testing, and understanding of what each component does. Their loose coupling allows for independent updates and modifications, enhancing system flexibility and many other benefits.
What are microservices in Java?
Microservices in Java leverage the Java programming language, its rich ecosystem, and specialized web service frameworks to construct applications that follow a microservices architecture. As mentioned in the previous section, this approach decomposes applications into smaller, focused, and independently deployable services. Each service tackles a specific business function, communicating with others. Generally, services use lightweight mechanisms such as exposing functionality through RESTful APIs or messaging systems, such as gRPC, etc. Popular Java frameworks like Spring Boot, Dropwizard, Quarkus, and others further simplify the process of creating microservices by providing features and functionality that lend themselves well to building microservices and distributed systems.
Why should development teams opt to use microservices? There are many reasons microservices should be used. Let’s look at a high-level breakdown of some of the critical benefits microservices bring:
Scalability: Microservices allow you to scale individual services independently, optimizing resource usage and cost-efficiency.
Resilience: Microservices improve fault tolerance; failures in one service are less likely to bring down the entire application.
Technology agnosticism: Choose the most suitable technologies and programming languages for each service, promoting flexibility and preventing technology lock-in.
Simplified deployment: Roll out changes to individual services quickly and easily, enabling faster iterations without redeploying an entire application.
Improved maintainability: The well-defined boundaries of microservices make them easier to understand, modify, and test, simplifying development and support.
Depending on the project, microservices may offer these advantages plus many more when it comes to building and supporting an application. That being said, microservices aren’t necessarily the silver bullet for all the problems of modern development. Next, we will cover some of the challenges that microservice implementations bring with them.
Challenges in microservices
As with anything good, there always come some drawbacks. While microservices offer the advantages we talked about above, it’s essential to acknowledge the complexities they introduce as well:
Increased operational complexity: Managing a distributed system with multiple microservices inherently presents more significant operational overhead in deployment, monitoring, and service communication.
Distributed data management: Ensuring data consistency across multiple microservices becomes more complex, often requiring strategies like eventual consistency to replace traditional database transactions.
Communication overhead: Sinceservices communicate via a network, this introduces potential latency and the need to handle partial failures gracefully. Choosing suitable protocols and patterns (like circuit breakers) must be factored into the system design.
Testing: Testing in a microservices environment involves individual services, interactions, and dependencies, demanding more complex integration and end-to-end testing.
Observability: Gaining visibility into a distributed system requires extensive logging, distributed tracing, and metrics collection. Monitoring each service and the overall system’s health can be relatively complex as the system’s architecture expands.
Despite these challenges, the benefits of microservices often outweigh the complexities. Careful planning, appropriate tools, service, discovery, and a focus on best practices can help manage these challenges effectively.
Examples of microservices frameworks for Java
If you’re looking to build microservices, the Java world offers a diverse collection of frameworks that excel in building them. Because of the technology agnosticism of microservices, different programming languages and frameworks can be used from service to service. If one framework excels in the functionalities you need for a particular microservice, you can use it as a one-off choice or build out your entire microservice stack in a single technology. Here’s a look at some of the top frameworks for building microservices with Java.:
Spring Boot
One of the most well-known enterprise frameworks for Java, Spring Boot sits atop the Spring Framework. Spring Boot helps to simplify microservice development with features like auto-configuration, embedded servers, and seamless integration with the vast Spring ecosystem that many organizations are already using.
Why it’s well-suited:
Ease of use and rapid development
Extensive community and resources
Excellent for both traditional and reactive microservice approaches
Dropwizard
Provides a focused and opinionated way to build RESTful microservices, bundling mature libraries for core functionality.
Why it’s well-suited:
Streamlined setup and quick project starts
Emphasis on production-readiness (health checks, metrics)
Ideal for RESTful services
Quarkus
A Kubernetes-native Java framework engineered for fast startup times, low memory footprints, and containerized environments.
Why it’s well-suited:
Optimized for modern cloud deployments
Prioritizes developer efficiency
Outstanding performance characteristics
Helidon
From Oracle, Helidon is a lightweight toolkit offering both reactive and MicroProfile-based programming models.
Why it’s well-suited
Flexibility in development styles
Focus on scalability
Jersey
Jersey is the JAX-RS (Java API for RESTful Web Services) reference implementation, providing a core foundation for building RESTful microservices.
Why it’s well-suited:
Standards-compliant REST framework
Allows for granular control
Play Framework
Play is a high-productivity, reactive framework built on Akka and designed for web applications. It is well-suited for both RESTful and real-time services.
Why it’s well-suited:
Supports reactive programming paradigms
Strong community and backing
As you can see, many of these frameworks are focused on building RESTful services. This is because most microservices are exposed via API, most of which are REST-based. Now that we know a bit about the frameworks, let’s take a dive into exactly what the code and configuration looks like when building a service with them.
How to create Microservices using Dropwizard
In this example, we will create a basic “Hello World” microservice using Dropwizard. This service will respond to HTTP GET requests with a greeting message.
Step 1: Setup Project with Maven
First, you’ll need to set up your Maven project. Add the following to your project’s pom.xml file to include Dropwizard dependencies:
Replace the “2.1.0” version with the latest version of Dropwizard or the version that you wish to use if there is a specific one.
Step 2: Configuration Class
Create a configuration class that will specify environment-specific parameters. This class should extend io.dropwizard.Configuration.
import io.dropwizard.Configuration; public class HelloWorldConfiguration extends Configuration { // Add configuration settings here }
Step 3: Application Class
Create an application class that starts the service. This class should extend io.dropwizard.Application.
import io.dropwizard.Application; import io.dropwizard.setup.Bootstrap; import io.dropwizard.setup.Environment; public class HelloWorldApplication extends Application<HelloWorldConfiguration> { public static void main(String[] args) throws Exception { new HelloWorldApplication().run(args); } @Override public void initialize(Bootstrap<HelloWorldConfiguration> bootstrap) { // Initialization code here } @Override public void run(HelloWorldConfiguration configuration, Environment environment) { final HelloWorldResource resource = new HelloWorldResource(); environment.jersey().register(resource); } }
Step 4: Resource Class
Next, create a resource class that will handle web requests. This class will define the endpoint and the method to process requests.
import javax.ws.rs.GET; import javax.ws.rs.Path; import javax.ws.rs.Produces; import javax.ws.rs.core.MediaType; @Path(“/hello-world”) public class HelloWorldResource { @GET @Produces(MediaType.TEXT_PLAIN) public String sayHello() { return “Hello, World!”; } }
Step 5: Build and Run
To build and run your application:
1. Compile your project with Maven: mvn clean install
2. Run your application: java -jar target/your-artifact-name.jar server
After running these commands, your Dropwizard application will start on the default port (8080). You can access your “Hello World” microservice endpoint by navigating to “http://localhost:8080/hello-world” in your web browser or using a tool like cURL:
curl http://localhost:8080/hello-world
This should return the greeting: “Hello, World!”
This is a simple introduction to creating a microservice with Dropwizard. From here, you can expand your service with more complex configurations, additional resources, and dependencies as needed.
How to create microservices using Spring Boot
In this second example, we will develop a “Hello World” microservice using Spring Boot. This service will respond to HTTP GET requests with a personalized greeting message, similar to our previous example.
Step 1: Setup Project with Spring Initializr
Start by setting up your project with Spring Initializr:
– Choose Maven Project with Java and the latest Spring Boot version
– Add dependencies for Spring Web
– Generate the project and unzip the downloaded file
Step 2: Application Class
With the base project unzipped, we will create the main application class that boots up Spring Boot. This is automatically generated by Spring Initializr, but here’s what it typically looks like:
package com.example.demo; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public class HelloWorldApplication { public static void main(String[] args) { SpringApplication.run(HelloWorldApplication.class, args); } }
Step 3: Controller Class
Create a controller class that will handle the HTTP requests. Use the @RestController annotation, which includes the @Controller and @ResponseBody annotations that result in web requests returning data directly.
This controller has a method, sayHello, that responds to GET requests at “/hello”. It uses @RequestParam to optionally accept a name value, and if none is provided, “World” is used as a default.
Step 4: Build and Run
To build and run your application:
1. Navigate to the root directory of your project via the command line.
2. Build your project with Maven:
mvn clean package
3. Run your application:
java -jar target/demo-0.0.1-SNAPSHOT.jar
You’ll need to replace demo-0.0.1-SNAPSHOT.jar file name with your actual jar file name.
Once again, to access your “Hello World” microservice, navigate to “http://localhost:8080/hello” in your web browser or use a tool like cURL:
curl http://localhost:8080/hello?name=User
If everything works as it should, this should return: “Hello, User!” in the response.
This example demonstrates a basic Spring Boot application setup and exposes a simple REST endpoint. As you expand your service, Spring Boot makes it easy to add more complex functionalities and integrations.
How to create Microservices using Jersey
Next, we’ll build a “Hello World” microservice using Jersey that responds to HTTP GET requests with a greeting message just as the other examples so far have.
Step 1: Setup Project with Maven
First, you’ll need to create a new Maven project and add the following dependencies to your pom.xml to include Jersey and an embedded server, Grizzly2:
You can replace “2.35” with the latest version of Jersey if needed or another version if you have a specific one you need to use.
Step 2: Application Configuration Class
Create a configuration class that extends ResourceConfig to register your JAX-RS components:
package com.example.demo; import org.glassfish.jersey.server.ResourceConfig; public class JerseyConfig extends ResourceConfig { public JerseyConfig() { register(HelloWorldResource.class); } }
Step 3: Resource Class
We will also need to create a resource class that will handle web requests:
package com.example.demo; import javax.ws.rs.GET; import javax.ws.rs.Path; import javax.ws.rs.Produces; import javax.ws.rs.core.MediaType; @Path(“/hello”) public class HelloWorldResource { @GET @Produces(MediaType.TEXT_PLAIN) public String sayHello() { return “Hello, World!”; } }
Step 4: Main Class to Start Server
Our last bit of code is where we will create the main class to start up the Grizzly2 HTTP server:
package com.example.demo; import org.glassfish.grizzly.http.server.HttpServer; import org.glassfish.grizzly.http.server.NetworkListener; import org.glassfish.jersey.grizzly2.httpserver.GrizzlyHttpServerFactory; import javax.ws.rs.core.UriBuilder; import java.net.URI; public class Main { public static void main(String[] args) { URI baseUri = UriBuilder.fromUri(“http://localhost/”).port(8080).build(); JerseyConfig config = new JerseyConfig(); HttpServer server = GrizzlyHttpServerFactory.createHttpServer(baseUri, config, false); NetworkListener listener = new NetworkListener(“grizzly”, “localhost”, 8080); server.addListener(listener); try { server.start(); System.out.println(“Server started at: http://localhost:8080/hello”); System.in.read(); server.shutdownNow(); } catch (Exception e) { System.err.println(“Error starting Grizzly server: ” + e.getMessage()); server.shutdownNow(); } } }
Step 5: Build and Run
To build and run your application:
1. Navigate to the root directory of your project via the command line.
2. Compile your project with Maven:
mvn clean package
3. Run your application:
java -jar target/your-artifact-name.jar
Replace your-artifact-name.jar with your actual jar file name that your build has output.
Just as we have with the previous examples, to access your “Hello World” microservice, navigate to “http://localhost:8080/hello” in your web browser or use cURL:
curl http://localhost:8080/hello
This API call should return “Hello, World!” in the API response.
This example demonstrates how to set up a basic Jersey application that can be used as a REST-based microservice.
How to create Microservices using Play Framework
Lastly, we will look at how to build a similar microservice using Play Framework. This example will mirror the previous ones and build a “Hello World” microservice that responds to HTTP GET requests with a greeting message.
Step 1: Setup Project with sbt
First, set up your project using sbt (Simple Build Tool), which is the standard build tool for Scala and Play applications. Here’s how you can set up a basic structure:
1. Install sbt: Follow the instructions on the official sbt website to install sbt.
2. Create a new project: You can start a new project using a Play Java template provided by Lightbend (the company behind Play Framework), which sets up everything you need for a Play application. Here is the command to do so:
sbt new playframework/play-java-seed.g8
This command creates a new directory with all the necessary files and folders structured.
Step 2: Controller Class
Modify or create a Java controller in the app/controllers directory. This class will handle the HTTP requests:
package controllers; import play.mvc.*; public class HomeController extends Controller { public Result index() { return ok(“Hello, World!”); } }
In Play, Result types determine the HTTP response. The ok() method creates a 200 OK response with a string.
Step 3: Routes Configuration
Next, we will define the application’s routes in the conf/routes file. This file tells Play what controller method to run when a URL is requested:
# Routes # This file defines all application routes (Higher priority routes first) # ~~~~ # An example controller showing a sample home page GET /hello controllers.HomeController.index()
This configuration means that HTTP GET requests to “/hello” will be handled by the index() method of HomeController.
Step 4: Build and Run
To run your Play application, you’ll need to do the following:
1. Open a terminal and navigate to your project’s root directory.
2. Execute the following command to start the application:
sbt run
Just like the previous examples, once the application is running, you can access it by visiting http://localhost:9000/hello in your web browser or using cURL:
This should also return the “Hello, World!” response we saw in the other examples.
This example gives a straightforward introduction to building a microservice with Play Framework.
Load balancing and scaling in Java microservices
Load balancing and scaling are fundamental to achieving high availability and performance in Java microservices architecture. Load balancing distributes incoming requests evenly across multiple instances of a service, ensuring that no single service instance becomes a bottleneck. This not only improves responsiveness but also enhances fault tolerance, as traffic can be rerouted if one instance fails.
In Java microservices, load balancing can be implemented using various strategies, such as round-robin DNS, hardware load balancers, or software-based solutions integrated with cloud services. For example, cloud platforms like AWS and Azure offer built-in load balancing features that work seamlessly with Java microservices, allowing services to handle fluctuating workloads efficiently.
Scaling complements load balancing by enabling services to automatically increase or decrease the number of running instances based on demand. This elasticity ensures that the system can handle spikes in traffic without over-provisioning resources during quieter periods. By combining load balancing and scaling, Java microservices can deliver consistent performance and reliability, even as workloads change.
Together, these practices empower developers to build robust microservices architectures that can grow with business needs, maintain high availability, and provide a seamless experience for end users.
API gateway for Java microservices
An API gateway is a vital component in Java microservices architecture, acting as the single entry point for all client requests to the system. Instead of clients interacting directly with multiple services, the API gateway receives incoming requests and routes them to the appropriate service instance. This approach simplifies client interactions, as clients only need to know about the gateway, not the internal structure of the microservices.
In Java microservices, popular frameworks like Spring Cloud Gateway and Netflix Zuul are commonly used to implement API gateways. These tools offer advanced features such as load balancing, security enforcement, rate limiting, and request transformation, all of which help streamline communication between clients and services.
The API gateway also plays a crucial role in service discovery, dynamically routing requests to available service instances and handling failover scenarios. By centralizing cross-cutting concerns like authentication and monitoring, the API gateway reduces the complexity of individual services and improves the overall security and manageability of the microservices architecture.
By leveraging an API gateway, Java microservices architectures can achieve better scalability, simplified service discovery, and a more consistent and secure interface for clients accessing multiple services.
Service discovery and communication
Service discovery and communication are essential for the dynamic and distributed nature of Java microservices architecture. As services are deployed, scaled, or updated, they need a reliable way to locate and interact with each other, even as the environment changes.
Service discovery enables this by allowing services to register themselves and discover other services at runtime. In Java microservices, this is often achieved using dedicated service registries like Netflix Eureka or through DNS-based solutions. These registries maintain an up-to-date list of available service instances, enabling dynamic discovery and routing.
Once services have discovered each other, they communicate using lightweight protocols such as REST or gRPC. These protocols are well-suited for microservices because they are efficient, language-agnostic, and support asynchronous messaging and HTTP requests. This flexibility allows services to exchange data reliably, regardless of the underlying programming languages or platforms.
By implementing robust service discovery and communication mechanisms, Java microservices architectures can adapt to changing workloads, maintain high availability, and ensure seamless data exchange between services. This is critical for building scalable, resilient, and maintainable distributed systems that can evolve alongside business requirements.
Best Practices for Microservices
Before you begin designing and implementing microservices, let’s take a look at some best practices to start you off on the right foot. To maximize the benefits and successfully navigate the challenges of microservices, here are some essential best practices to keep in mind:
Domain-driven design (DDD): Align microservice boundaries with business domains or subdomains to ensure each service has a clear and well-defined responsibility.
Embrace loose coupling: Minimize dependencies between microservices, allowing them to evolve and be deployed independently.
API versioning: Implement a thoughtful versioning strategy for your microservice APIs to manage changes without breaking clients.
Decentralized data management: Choose appropriate data management strategies for distributed systems (eventual consistency, saga patterns, etc.).
Architectural drift: Once the application’s baseline is established, ensure you can actively observe how architectural drift is changing from the target state or baseline to avoid costly technical debt.
Observability: Implement end-to-end logging, monitoring, and distributed tracing to gain visibility across all services individually and across the entire system.
Resilience: Design for failure using patterns like circuit breakers, retries, and timeouts to prevent cascading failures.
Security: Secure your microservices at multiple levels. This includes adding security at the network level, API level, and within individual service implementations.
Automation: Automate as much of the build, deployment, and testing processes as possible to streamline development.
Containerization: Use containers to package microservices in containers (e.g., Docker) for portability and easy deployment via orchestration platforms (e.g., with Kubernetes).
With these best practices, your microservices should start off on the right path. It’s important to remember that best practices can evolve and your organization may also have recommendations you should take into consideration. When it comes to designing and implementing microservices, architecture plays a big role. Next we will take a look at how vFunction and architectural observability can help with Java microservice creation and support.
How vFunction can help with microservices design in Java
The choice to refactor existing services into microservices or to build them net new can be challenging. Refactoring code, rethinking architecture, and migrating to new technologies can be complex and time-consuming. This is where vFunction becomes a powerful tool to simplify and inform software developers and architects about their architecture as they begin to adopt microservices or rewrite existing monolithic architectures into microservices.
vFunction analyzes and assesses applications to identify and fix application complexity so monoliths can be more modular or move to microservices architecture.
Let’s break down how vFunction aids in this process:
1. Automated analysis and architectural observability: vFunction begins by deeply analyzing your application’s codebase, including its structure, dependencies, and underlying business logic. This automated analysis provides essential insights and creates a comprehensive understanding of the application, which would otherwise require extensive manual effort to discover and document. Once the application’s baseline is established, vFunction kicks in with architectural observability, allowing architects to actively observe how the architecture is changing and drifting from the target state or baseline. With every new change in the code, such as the addition of a class or service, vFunction monitors and informs architects and allows them to observe the overall impacts of the changes.
2. Identifying microservice boundaries: One crucial step in the transition is determining how to break down an application into smaller, independent microservices. vFunction’s analysis aids in intelligently identifying domains, a.k.a. logical boundaries, based on functionality and dependencies within the overall application, suggesting optimal points of separation.
3. Extraction and modularization: vFunction helps extract identified components and package them into self-contained microservices. This process ensures that each microservice encapsulates its own data and business logic, allowing for an assisted move towards a modular architecture. Architects can use vFunction to modularize a domain and leverage the Code Copy feature to accelerate microservices creation by automating code extraction. The result is a more manageable application that is moving towards your target-state architecture.
Key advantages of using vFunction
Engineering velocity: vFunction dramatically speeds up the process of creating microservices and moving monoliths to microservices, if required. This increased engineering velocity translates into faster time-to-market and a modernized application.
Increased scalability: By helping architects view their existing architecture and observe it as the application grows, scalability becomes much easier to manage. By seeing the landscape of the application and helping to improve the modularity and efficiency of each component, scaling is more manageable.
Improved application resiliency: vFunction’s comprehensive analysis and intelligent recommendations increase your application’s resiliency by supporting more modular architecture. By seeing how each component is built and interacts with each other, informed decisions can be made in favor of resilience and availability.
Conclusion
Microservices offer a powerful way to build scalable, resilient, and adaptable Java applications. They involve breaking down applications into smaller, independently deployable services, increasing flexibility, maintainability, and the ability to scale specific components.
While microservices bring additional complexities, Java frameworks like Spring Boot, Dropwizard, Quarkus, and others simplify their development. Understanding best practices in areas like domain-driven design, API design, security, and observability is crucial for success.Whether you’re building a system from scratch or refactoring an existing one, vFunction is the architect’s direct vision into the current state of your application and helps you to understand how changes affect the architecture as it evolves. Architectural observability is a must have tool when considering microservice development or promoting good architectural health for existing microservices. To learn more about how vFunction can help you modularize your microservice architecture, contact our team today.
Transform monoliths into microservices with vFunction.
Explaining a complex software system to your team or stakeholders can be challenging, especially in fast-paced agile environments where systems evolve rapidly. Architecture diagrams give a clear way to represent system structures, relationships, and interactions, making it easier to design, understand, and communicate your ideas. The right tools keep the diagram in sync with the implementation, fostering alignment across teams even in the midst of rapid releases and dynamic microservices architectures.
In this guide, we’ll explore the importance of software architecture diagrams, outline common types, and offer practical advice to create diagrams that enhance collaboration and decision-making even as evolving functionality reshapes existing architecture. Whether troubleshooting a legacy system or designing something new, you’ll walk away with actionable insights to communicate your ideas effectively and keep your entire team aligned and informed.
What is an architecture diagram?
An architecture diagram is a blueprint of a software system, showcasing its core components, their interconnections, and the communication channels that drive functionality. Unlike flowcharts that describe behavioral control flows, architecture diagrams capture the structural aspects of the system, including modules, databases, services, and external integrations. This comprehensive overview enables developers, architects, and stakeholders to grasp the system’s organization, identify dependencies, and foresee potential challenges. Because architecture diagrams provide a clear snapshot of the system’s design, they are essential tools for planning, development, and ongoing maintenance. Modern approaches emphasize structural aspects, because the core architecture of a system tends to evolve gradually, providing a stable foundation, while the behavior of individual components and their interactions are constantly changing as new features and updates are introduced.
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Architecture diagrams show how a software system is structured by focusing on key elements like components, connectors, and relationships.
Components: These represent the system’s fundamental building blocks, such as individual modules, databases, services, and external systems. For instance, in a web application, components include mobile and web based clients, authentication service, load balancers, and database engines.
Relationships: These define how components are related and interact with each other at the logical level. Architecture diagrams help establish relationships between components, making it easier to identify dependencies and communication pathways. For example, a mobile client app may use an identity provider service for single sign-on using an SDK. Understanding these relationships helps identify dependencies and potential bottlenecks within the system.
Connectors: These depict the messaging interactions and data flow channels between components. Connectors can show various communication protocols, such as HTTP requests between a front-end application and an API server or database connections between an application and its database.
Architecture diagrams are visual tools that help explain the structure of a system, making it easier for stakeholders to understand how everything fits together. They break down complex systems at varying abstraction levels, making the information more accessible to people with different levels of technical knowledge. These diagrams are important documentation and a reference for building and maintaining the system over time. They also play a key role in decision-making because they provide a clear view of the system’s design, which can be helpful when it comes to planning for scalability, performance, and other technical details. Architecture diagrams are invaluable for troubleshooting because they help identify potential issues or bottlenecks.
During the planning phase, they guide the design process, offering a roadmap for scalability and modularity. They also ensure the system meets security and regulatory standards by showing how data moves and where sensitive information is stored or processed.
Why architecture diagrams matter in system design
Modern software systems are increasingly complex, often involving many components, services, and integrations. While advanced tools such as Microsoft Copilot, Sonarqube/cloud, APMs and others are valuable to ensure code quality and performance, they don’t replace the need to visually represent the system’s architecture.
Keeping diagrams updated to accurately reflect the system’s architecture is essential for risk mitigation and effective communication throughout development.
The importance of visual system design includes the following key aspects:
Enabling informed decision-making
A comprehensive architecture diagram allows developers and architects to understand the overarching system at a glance. For example, when deciding between a microservices architecture and a monolithic design, a detailed diagram can highlight how services interact, helping stakeholders assess scalability, maintainability, and deployment implications.
Accelerating development time
With a clear architectural blueprint, development teams can work more efficiently. The diagram is a reference point that reduces ambiguity, aligns team members on system components, and streamlines the development process. This clarity minimizes misunderstandings and rework, thereby shortening development cycles.
Enhancing system maintainability
Maintenance and updates are inevitable in software development. Architecture diagrams make it easier to identify which components may be affected by a change. For instance, if a particular service needs an update, the diagram can help determine its dependencies, ensuring that modifications do not inadvertently disrupt other parts of the system.
At the end of the day, architecture diagrams are more than just visual aids; they facilitate better design, efficient development, and smoother maintenance of the systems they describe. By clearly depicting the system, they help teams navigate complexities and collaborate effectively to build robust software solutions.
Common types of architecture diagrams
No single architecture diagram can capture every aspect of a system’s complexity. Different types of architectural diagrams are intended to highlight a viewpoint about the system’s components, interactions, and perspectives. Below are some of the most common types of architecture diagrams and their unique applications.
Architecture diagrams in UML
Unified Modeling Language (UML), defined by the Object Management Group (OMG), remains one of the most widely used modeling standards in software engineering. It is a staple in software engineering education, supported by numerous tools, and many methodologies have adopted a subset of its diagrams, making it a versatile choice for system design. Some of the tools are open source (like PlantUML) and some are commercial tools (like MagicDraw and Rhapsody) with advanced capabilities like code generation, simulation and formal verification of models.
Of UML’s 14 diagram types, class and object diagrams are among the most commonly used, often combined into a single diagram to describe architecture at different abstraction levels. These diagrams define relationships between classes and objects, such as association, aggregation, composition, inheritance, realization, and dependency, which can be further customized using UML profiling extensions. UML class diagrams are commonly used to define data models, representing how data is organized and related within the system. While UML allows for semantically precise specifications, it can also introduce complexity or potential over-specification, potentially causing confusion among stakeholders.
Here is an example UML class diagram along with an explanation of its various elements.
And here is an example object diagram from the same source:
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While developers use component and deployment diagrams less frequently in UML, these diagrams play a crucial role in showcasing high-level architectural elements. The following is an example of a deployment diagram:
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In summary, UML architectural diagrams are among the most expressive and detailed tools for conveying complex system designs. They are best suited for technical stakeholders, such as architects conducting deep-dive reviews or developers optimizing an architecture based on detailed requirements. However, effectively using UML requires a solid understanding of the language and a well-defined methodology, contributing to its declining adoption.
C4 model
The C4 model, created by Simon Brown between 2006 and 2011, builds on the foundations of UML as a lean, informal approach to visually describe software architecture. Its simplicity and practicality have made it increasingly popular since the late 2010s.
Unlike UML, the C4 model focuses on the foundational building blocks of a system — its structure — by organizing them into four hierarchical levels of abstraction: context, containers, components, and code. This organization provides a clear, intuitive way to understand and communicate architectural designs. Some of the UML tools, like PlantUML, also support C4 diagrams, but C4 is still not as widely accepted as UML and has less tool support overall.
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This context diagram (shown at the highest level of abstraction) represents an Internet Banking System along with its roles and the external systems with which it interacts.
This container diagram “zooms in” on the Internet Banking System from the context diagram above. In C4, a container represents a runnable or deployable unit, such as an application, database, or filesystem. These diagrams show how the system assigns capabilities and responsibilities to containers, details key technology choices, maps dependencies, and outlines communication channels within the system and with external entities like users or other systems.
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Below is a component diagram that zooms in on the API application container from the above container diagram. This diagram reveals the internal structure of the container, detailing its components, the functionality it provides and requires, its internal and external relationships, and the implementation technologies used.
C4 introduces a clear and intuitive hierarchy, where container, component, and code diagrams provide progressively detailed “zoom-in” views of entities at higher abstraction levels. This structure offers a straightforward and effective way to design and communicate architecture.
In summary, C4 offers standardized, tool- and method-independent views, making it versatile for communicating designs to various stakeholders. However, it lacks the level of detail and richness that UML provides for intricate specifications.
Architectural diagrams for designing cloud solutions
Cloud vendors like AWS, Azure, and Google provide tools for creating architecture diagrams to design and communicate solutions deployed on their platforms. These diagrams often use iconography to represent various cloud services and arrows to illustrate communication paths or data flows. They typically detail networking elements such as subnets, VPCs, routers, and gateways since these are crucial for cloud architecture. Additionally, cloud architecture diagrams often illustrate the physical layout of hardware and software resources to optimize deployment and communication between components.
A typical pattern which is shown in the above diagram is to add numbered labels on the lines (as shown above) and have a list describing a main interaction across the components (as shown here)
Free drawing tools such as https://app.diagrams.net/ enable drawing these diagrams by having the icons of the various cloud services out of the box. Other more cloud-specific commercial tools like Cloudcraft and Hava.io offer various automations such as diagram synthesis from an existing cloud deployment, operational costs calculation and more.
It is nearly impossible to design and communicate cloud solutions without visualizing the architecture. Unlike UML and C4, cloud architecture diagrams focus on the deployment of cloud services within the cloud infrastructure, illustrating their configuration, interactions, and usage in the system.
System and application architecture diagrams
Other widely used diagrams include system architecture and application architecture diagrams. System architecture diagrams provide a high-level overview of an entire system, showcasing its components—hardware, software, databases, and network configurations—and their interactions. In contrast, application architecture diagrams focus on a specific application within the system, highlighting internal elements such as the user interface, business logic, and integrations with databases or external services. These diagrams offer stakeholders valuable insights into the overall system structure, operational flow, and application-specific details.
Benefits of using architecture diagrams
Architecture diagrams are essential tools that bring significant value throughout the software development lifecycle. By providing a clear visual representation of system components, interactions, and dependencies, they help streamline communication, identify risks early, and support informed decision-making. Here are some of the key benefits:
Enhancing collaboration and communication
Architecture diagrams are a visual tool that connects technical and non-technical stakeholders. By illustrating the system’s structure and components, they help everyone—developers, designers, project managers, and clients—understand how the system works. This clarity reduces misunderstandings and ensures that everyone stays aligned throughout the development process.
Risk mitigation and issue identification
Visualizing the system’s architecture early in the process makes identifying potential risks, bottlenecks, and design flaws easier. Spotting these issues upfront allows developers to address them proactively, preventing problems from escalating during development or after deployment. This leads to more reliable and robust systems.
Streamlining scalability and efficiency
Architecture diagrams help teams understand system dependencies and interactions, which is crucial for planning future scalability and maintaining efficiency. By visualizing how components interact, developers can make well-informed decisions about scaling, optimizing performance, and planning for growth.
In short, architecture diagrams play a crucial role in creating better software by improving communication, minimizing risks, and supporting scalability and efficiency. By integrating them into your development process, you can build systems that are more reliable, maintainable, and better equipped to grow with your business and meet the evolving needs of your users.
Challenges with traditional architecture diagrams
While architecture diagrams are vital tools for planning and communication, they often face a significant challenge: keeping up with the pace of modern software development. Diagrams are typically created during the initial design and development stages when teams map out how they expect the system to function. However, as the software evolves—through updates, new features, and shifting requirements—the reality of the system’s architecture can drift far from the original design.
This “architectural drift“ occurs because manual updates to diagrams are time-consuming, easily deprioritized, and prone to oversight. The result is a disconnect: diagrams remain static artifacts while the software grows more dynamic and complex. Teams are left relying on outdated visuals that fail to reflect the actual architecture, making it harder to troubleshoot issues, onboard new developers, or plan for scalability.
In the early 2000s, some UML modeling tools, like IBM Rhapsody, attempted to tackle this challenge with features like code generation (turning models into code) and round-tripping (syncing code back into models). They even integrated these modeling capabilities into popular integrated development environments (IDE) like Eclipse, allowing developers to work on both models and code as sort of different views in a single environment. However, this approach didn’t catch on. Many developers found the auto-generated code unsatisfactory and opted to write their own code using various frameworks and tech stacks. As a result, the diagrams quickly became irrelevant.
Bridging the gap: The need for dynamic, real-time architecture
Modern tools and practices must move beyond static representations to deliver value and real-time architectural insights. Automated solutions can continuously monitor and visualize system components and interactions as they change, ensuring diagrams stay accurate and actionable. Tools such as vFunction automatically document your live application architecture, generating up-to-date visualizations that reflect the actual system and its runtime interactions, not just the idealized design. By ensuring architecture diagrams keep pace with the working system, teams can make informed decisions, uncover hidden dependencies, and confidently manage complexity as their software evolves.
Export and import C4 container diagrams with vFunction
DevOps and integration: The evolving role of architecture diagrams
As software development evolves with the rise of DevOps practices and microservices architecture, the role of architecture diagrams has expanded to meet new challenges. DevOps architecture diagrams are now vital for visualizing the components of a DevOps system and illustrating how these components interact throughout the entire pipeline—from code integration to deployment. These diagrams help development teams understand the flow of processes, pinpoint areas for automation, and ensure that the devops system operates smoothly and efficiently.
Integration architecture diagrams, meanwhile, focus on how different components interact with each other and with external systems. By highlighting the protocols and methods used for integration, these diagrams make it easier to identify potential issues, streamline communication, and ensure seamless data flow across the system. In environments built on microservices architecture, integration architecture diagrams are especially valuable for mapping out the complex web of service interactions and dependencies.
Architecture diagrams provide a common language for development teams, stakeholders, and even external partners, ensuring that everyone has a shared understanding of how the system works. By visually representing how components interact, these diagrams facilitate collaboration, reduce misunderstandings, and help teams build more resilient and adaptable software systems.
Step-by-step guidelines for creating and using architecture diagrams
Architecture diagrams, including the above mentioned types, cannot be formally validated (except in some advanced UML cases with specialized tools). To avoid confusion, follow a systematic approach when creating and using these diagrams. For instance, inconsistencies or duplications between diagrams can lead to misunderstandings, as can ambiguous notations. Be cautious with elements like colors, which may not have a universally understood meaning, or arrows and lines that could be misinterpreted, such as representing data flows instead of dependencies. A clear and consistent approach ensures better communication and understanding among stakeholders.
Before we go into the step-by-step procedure, here are a few guidelines:
Using standardized symbols and notations: Using standardized symbols and notations is a way to avoid misinterpretations. If you draw a cloud architecture, make sure to use the correct icons and labels by selecting them from a catalog. If you are using C4, make sure to follow the notation and use the right terminology, if you are using UML make sure to follow the standard and opt to use a tool that can check and enforce semantic correctness. This will also save lengthy explanations when presenting to stakeholders familiar with the language.
Focusing on clarity and simplicity: Keeping your architecture diagrams clear and simple is essential for effective communication. It’s best to avoid too many details that can make the diagram confusing. Instead, focus on the key components and their interactions. For example, when mapping out a web application’s architecture, focus on the frontend, backend, database, and external APIs without including every minor module. Use concise, clear labels and consistent symbols to ensure everyone can easily understand the system’s structure. Deployment architecture diagrams should also clearly indicate deployment environments, such as development, staging, and production, to facilitate planning and optimization.
Selecting the right diagram type and tool: As discussed earlier, each diagram type serves a specific purpose. Select the type that best suits your needs and the information you want to convey. Leverage diagramming tools with helpful features like automated layouts, version control, and collaboration options. These tools can make the diagramming process more efficient and improve the quality of your diagrams.
Make the diagrams visually appealing: Aesthetic diagrams are easier to communicate and appeal more to stakeholders, in the same way that aesthetic presentations and documents are better received by stakeholders.
Here is a high-level step-by-step procedure to design an architecture of a new system:
Step 1: Choose the right tool and language according to your purpose
The stakeholders and purpose of the diagram should determine the language and tool used for the specification. For cloud architecture use tools that support the iconography and conventions of your cloud provider such as draw.io, lucidchart, etc. For high level architectural specification use one or more of the C4 diagrams, a list of available tools is specified here. For detailed technical specifications for technical stakeholders use UML, which has a wide set of tools. For a list of UML tools click see this Wikipedia page.
Step 2: Start from the system context and elaborate the internal structures
When specifying a new architecture, it is recommended to start the specification with the roles and external entities and the relationship with the system as a whole and then elaborate the subsystems and their components and then zoom in to the components internal structure. Capturing user interactions in architecture diagrams is important for understanding how user actions trigger events and system responses, especially in event-driven architectures. This is consistent with the C4 approach of context->containers->components->code, but the same holds for cloud architecture diagrams and UML diagrams.
If needed, include non-functional elements or resources that are key to the architecture such as subnets, protocols and databases.
Step 3: Verify the architecture with a few scenarios
Choose a few main scenarios and verify that the components and the relationships support them. This will also help in reviewing the architecture with others. You can do it at the system context level as well as at the detailed design level. A common technique to do this is to label the steps on top of the relationships and describe the interaction. Another way is to use UML sequence diagrams to describe the interactions across the components and ensure that every communication between lifelines has a supporting relation in the architecture. Sequence diagrams provide the means to include details such as alternative and parallels interactions, loops and more and are frequently used for detailed designs. Sequence Diagrams are useful in defining APIs as well as serving as a basis for the definition of unit, integration and system tests.
Step 4: Review, annotate and iterate
Once you have a baseline architecture, always review it with relevant stakeholders, add their comments on top of the diagrams and make the necessary refinements based on their feedback. Some tools have built in collaboration features that include versioning, annotations and more.
Creating an architecture diagram for an existing system
Designing architecture diagrams for new systems is straightforward, but understanding and communicating the architecture of an existing complex system—whether monolithic or distributed—can be significantly more challenging. Reverse-engineering code to create diagrams is difficult, especially in distributed applications with multiple languages and frameworks. Even tools like Enterprise Architect or SmartDraw often produce outputs that are overly complex and hard to interpret.
The C4 model simplifies software visualization with context, container, component, and code diagrams. Using OpenTelemetry, vFunction analyzes distributed architectures and allows teams to export live architecture into C4 container diagrams for visualization with tools like PlantUML. This approach helps engineers conceptualize, communicate, and manage architectures more effectively.
vFunction can also import C4 container diagrams as a reference architecture and create TODO items (tasks) based on its analysis capabilities to bridge the gaps between the current architecture and the to-be architecture.
With its “architecture as code” capability, vFunction aligns live systems with C4 reference diagrams, detecting architectural drift and ensuring real-time flows match the intended design. It helps teams understand changes, maintain architectural integrity, and keep systems evolving cohesively.
Microservices architecture visualized in vFunction and exported as “architecture as code” to PlantUML using the C4 framework.Image Pop-out with Cursor Icon
The critical role of architecture diagrams in software development
In software development, systems can become so complex that explaining ideas through words or traditional documentation often falls short. Architecture diagrams simplify this complexity, providing a clear and concise way to communicate intricate concepts, including your system’s structure, components, and interactions. For these diagrams to truly add value, they must break out of the ivory tower—evolving from static, theoretical artifacts into dynamic, living tools that accurately reflect the current state of your software.
Keeping these diagrams accurate with minimal effort from developers allows them to integrate into day-to-day workflows seamlessly. This enables teams to foster collaboration, uncover hidden issues, and ensure systems evolve with clarity and purpose. You can build more efficient, maintainable, and scalable software by incorporating these diagrams into your development toolkit. To learn more about how vFunction helps keep architecture diagrams aligned with real-time applications, visit our architectural observability for microservices page or contact us.
Companies today depend on legacy applications for some of their most business-critical processing. Many businesses rely on these systems to support essential business processes and daily operations. Legacy software and existing applications often present significant challenges for organizations seeking to modernize their IT environments.
In many cases, these apps still perform their intended functions quite well. However, to retain or expand their value in this era of accelerated innovation, they must be fully integrated into today’s dominant technological environment: the cloud. Digital transformation is a key driver for organizations to migrate legacy applications, enabling them to stay competitive and agile. Legacy application migration refers to the process by which organizations migrate their legacy applications—moving outdated software to modern platforms or cloud platforms — to enhance performance, security, and scalability.
Legacy systems struggle, and that’s why legacy application migration has become a high priority for so many organizations. Migrating to cloud platforms and modern platforms offers benefits such as cost savings, reduced operational costs, improved performance, and improved efficiency, making it a strategic move for businesses. A recent survey reveals that 48% of companies planned to migrate at least half of their apps to the cloud within the past year.
Yet for many organizations, the ROI they’ll reap from their legacy application migration efforts will fall short of expectations. Maintenance costs, the need for significant code changes, and ensuring data integrity during migration are common challenges. According to PricewaterhouseCoopers (PwC), “53% of companies have yet to reap substantial value from their cloud investments.” And McKinsey estimates that companies will waste approximately $100 billion on their application migration projects between 2021 and 2024.
Why Legacy System and Application Migration Falls Short
Why does legacy application migration so often fail to provide the expected benefits? In many cases, it’s because companies believe the quickest and easiest way to modernize their legacy apps is to move them to the cloud as-is, with no substantial changes to an app’s architecture or codebase. However, significant changes to the application architecture or code may be necessary to ensure compatibility with modern platforms and fully realize the benefits of migration.
However, that methodology, commonly referred to as “lift and shift,” has proven to be fundamentally inadequate for fully leveraging the benefits of the cloud. Yet companies often adopt it as the foundation for their app modernization efforts based on some widespread but fallacious beliefs about the advantages of that approach. A thorough assessment process should include analyzing the application architecture, reviewing existing applications, and evaluating the current system and existing systems to identify limitations and plan for modernization.
In this article, we want to examine some of the most pernicious lift and shift fallacies that frequently lead companies astray in their efforts to modernize their legacy app portfolios. Outdated legacy software, old system architectures, and old systems often present challenges such as high maintenance costs, integration issues, and technical debt. Migrating to the cloud can help reduce operational costs, lower maintenance costs, and deliver improved performance and improved scalability. The legacy application migration process should be carefully planned to ensure minimal disruption, maintain data integrity, and follow a solid modernization strategy. Let’s start with an issue that’s fundamental to the inadequacy of lift and shift as a company’s primary method for moving apps to the cloud: technical debt.
Understanding Business Needs
Before initiating any legacy system migration, it’s essential to have a clear understanding of your organization’s business needs and objectives. The migration process should be driven by specific goals—whether that’s improving efficiency, reducing operational costs, enhancing security, or enabling access to data on mobile devices. Begin by thoroughly assessing your current legacy system, examining its architecture, functionality, and performance to identify areas that require improvement and to determine the optimal migration strategy for your business.
A successful legacy system migration strategy also requires a careful evaluation of how the migration will impact ongoing business operations. Consider potential downtime, service disruptions, and the need for high data protection measures, especially if your organization handles sensitive information or faces significant security threats. By aligning the migration process with your business needs, you can ensure that the new system not only meets current requirements but is also flexible enough to adapt to future demands.
Taking the time to understand your business needs helps you avoid common pitfalls, such as migration fails or unexpected operational costs. It also enables you to select the best migration strategy—one that supports business continuity, minimizes risk, and delivers measurable value. Ultimately, a well-planned migration process tailored to your organization’s unique needs is the foundation for a smooth transition to a modern, secure, and efficient new system.
Pre-Migration Considerations
Migrating legacy applications to a new environment is a complex undertaking that requires careful planning and preparation. One of the first and most critical decisions is selecting the right cloud platform, as compatibility with your existing legacy system can vary significantly between providers. Evaluating the strengths and limitations of different cloud services, including cloud native applications and hybrid cloud solutions, will help you choose a cloud solution that aligns with your technical and business requirements.
Data migration is another key consideration. Protecting against data loss and security issues is paramount, especially when dealing with outdated technology that may not meet modern security standards. Develop a comprehensive migration plan that outlines a clear timeline, budget, and allocation of resources. This plan should also address user training needs to ensure a smooth transition for your team and minimize operational disruptions.
Business continuity should remain a top priority throughout the migration process. Strategies for minimizing downtime and maintaining essential business operations are necessary to avoid costly interruptions. Additionally, consider whether your migration project requires specialized knowledge or expertise, particularly if your legacy applications are built on outdated technologies that may present unique technical challenges.
By addressing these pre-migration considerations, organizations can set the stage for a successful legacy migration. Careful planning, risk assessment, and the right mix of cloud services will help ensure a seamless transition to a new system, allowing your business to fully leverage the benefits of modern technology while avoiding common pitfalls associated with legacy system migration.
The Role of Technical Debt
The greatest hindrance to a company fully benefiting from the cloud is the failure to modernize their applications . Monolithic applications carry a large amount of architectural technical debt that make integrating them into the cloud environment a complex, time-consuming, risky, and sometimes nearly impossible undertaking. And that, in turn, can negatively impact a company’s long-term marketplace success. A McKinsey report on technical debt puts it this way:
“Poor management of tech debt hamstrings companies’ ability to compete. The complications created by old and outdated systems can make integrating new products and capabilities prohibitively costly.”
“Technical debt is the cost incurred when poor design and/or implementation decisions are taken for the sake of moving fast in the short-term instead of a better approach that would take longer but preserve the efficiency, maintainability, and sanity of the codebase.”
By modern design standards, legacy apps are, almost by definition, permeated with “poor design and/or implementation decisions.” For example, such apps are typically structured as monoliths, meaning that the codebase (perhaps millions of lines of code) is a single unit with functional implementations and dependencies interwoven throughout.
Such code can be a nightmare to maintain or upgrade since even small changes can ripple through the codebase in unexpected ways that have the potential to cause the entire app to fail.
Not only does technical debt make legacy code opaque (hard to understand), brittle (easy to break), and inflexible (hard to update), but it also acts as a drag on innovation. According to the McKinsey technical debt report, CIOs say they’re having to divert 10% to 20% of the budget initially allocated for new product development to dealing with technical debt. On the other hand, McKinsey also found that by effectively managing technical debt, companies can free their engineers to spend up to 50% more of their time on innovation.
The Fallacies of Lift and Shift
Because it involves little to no change to an app’s architecture or code, lift and shift typically moves apps into the cloud faster and with less engineering effort than other legacy application migration approaches. However, the substantial benefits companies expect to reap from that accomplishment rarely materialize because those expectations are often based on flawed assumptions about the actual benefits of simply migrating legacy apps to the cloud.
Let’s look at some of those fallacies.
Fallacy #1: Lift and Shift = Modernization
Companies often migrate their legacy apps to the cloud as a means, they think, of modernizing them. But in reality, simple as-is migration (which is what lift and shift is all about) has very little to do with true modernization. To see why, let’s look at a definition of application modernization from industry analyst David Weldon:
“Application modernization is the process of taking old applications and the platforms they run on and making them ‘new’ again by replacing or updating each with modern features and capabilities that better align with current business needs.”
Lift and shift migration, which by definition transfers apps to the cloud with as little change as possible, does nothing to update them “with modern features and capabilities.” If the app was an opaque, brittle, inflexible monolith in the data center, it remains exactly that, with all the disadvantages and limitations of the monolithic architecture, when lifted and shifted to the cloud. That’s why migration alone has little chance of substantially improving the agility, scalability, and cost-effectiveness of a company’s legacy apps.
True modernization involves refactoring apps from monoliths to a cloud-native microservices architecture. Only then can legacy apps reap the benefits of complete integration into the cloud ecosystem. In contrast, lift and shift migration only defers the real work of modernization to some future time.
Fallacy #2: Lift and Shift Is Faster
It’s true that lift and shift migration is usually the quickest way to get apps into the cloud. But cloud migration is often not the quickest way of making apps productive in the cloud. That’s because cloud management of apps that were never designed for that environment, and that retain all the technical debt and other issues they had in the data center, can be a complex, time-consuming, and costly process.
The ITPro tech news site provides a good example of the kind of post-migration issues that can negate or even reverse the supposed speed advantage of lift and shift:
“Compatibility is the first issue that companies are liable to run into with lift-and-shift; particularly when dealing with legacy applications, there’s a good chance the original code relies on old, outdated software, or defunct libraries. This could make running that app in the cloud difficult, if not impossible, without modification.”
To make matters worse, the complexity and interconnectedness of monolithic codebases can make anticipating potential compatibility or dependency issues prior to migration extremely difficult.
Fallacy #3: Lift and Shift Is Easier
In the past, architects lacked the tools needed for generating the hard data required for building a business case to justify complex modernization projects. This made lift and shift migration appear to be the easiest path toward modernization.
But today’s advanced AI-based application modernization platforms provide comprehensive analysis tools that enable you to present a compelling, data-driven business case demonstrating that from both technical and business perspectives, the long-term ROI of true modernization far exceeds that of simple migration.
Fallacy #4: Migration Is Cheaper
Because lift and shift migration avoids the costs associated with upgrading the code or structure of monolithic legacy apps, it seems to be the least expensive alternative. In reality, monoliths are the most expensive architecture to run in the cloud because they can’t take advantage of the elasticity and adaptability of that environment.
Migrated monolithic apps still require the same CPU, memory, and storage resources they did in the data center, but the costs of providing those resources in the cloud may be even greater than they were on-prem. IBM puts it this way:
An application that’s only partially optimized for the cloud environment may never realize the potential savings of (the) cloud and may actually cost more to run on the cloud in the long run.
IBM also notes that because existing licenses for software running on-site may not be valid for the cloud, “licensing costs and restrictions may make lift and shift migration prohibitively expensive or even legally impossible.”
Fallacy #5: Migration Reduces Your Technical Debt
As we’ve seen, minimizing technical debt is critical for effectively modernizing legacy apps. But when apps are simply migrated to the cloud, they take all their technical debt with them and often pick up more when they arrive. For example, some migrated apps may develop debilitating cloud latency issues that weren’t a factor when the app was running on-site.
So, migration alone does nothing to reduce technical debt, and may even make it worse.
How to Truly Modernize
In a recent technical debt report, KPMG declared that “Getting a handle on it [technical debt] is mission-critical and essential for success in the modern technology-enabled business environment.”
If your company relies on legacy app processing for important aspects of your mission, it’s critical that you prioritize true modernization; that is, not just migrating your essential apps to the cloud, but refactoring them to give them full cloud-native capabilities while simultaneously eliminating or minimizing technical debt.
The first step is to conduct a comprehensive analysis of your legacy app portfolio to determine the amount and type of technical debt each app is carrying. With that data, you can then develop (and justify) a detailed modernization plan.
Here’s where an advanced modernization tool with AI-based application analysis capabilities can significantly streamline the entire process. The vFunction platform can automatically analyze the sources and extent of technical debt in your apps, and provide quantified measures of its negative impact on current operations and your ability to innovate for the future.
If you’d like to move beyond legacy application migration to true legacy app modernization, vFunction can help. Contact us today to see how it works.
