Top microservices frameworks: Python, Go, and and more

Like some software development conspiracy, there are literally “microservices everywhere.” If you contrast microservices with the more legacy monolithic approach, it’s likely no surprise why they have become so popular. Microservice adoption has revolutionized software application design, development, and deployment. Organizations seeking agility, scalability, and resilience can get this by building new applications as microservices or breaking down monolithic applications into smaller, independent services leveraging a microservices architecture. However, building microservices can be complex, so picking the right framework from the many available options is essential.

This blog post explores the facets of microservices frameworks, diving into popular options for Python and Go and other top contenders in different languages. We’ll examine the benefits and challenges and discuss how to evaluate the right framework for your current needs and future trends. Let’s start by looking at why choosing the right framework is so critical.

Choosing the right microservices framework

Building microservices-based applications is akin to assembling a complex machine. Each service has its place in the overall system and the framework can either help or hinder your ability to seamlessly combine services into a cohesive whole. Choosing the proper framework can make or break your project, impacting everything from development speed to application performance and long-term maintainability.

Key benefits of microservices frameworks

A framework generally contains the essential tools and blueprints to simplify microservices development. Frameworks provide ready-made components and libraries that eliminate the need to reinvent the wheel, enforcing standardized design patterns and best practices. Access to tools, in particular, is a significant benefit of a framework because they can simplify service discovery, community support, and data serialization, which makes it easier to develop your service and ultimately connect and manage your microservices. Depending on the framework chosen, some frameworks have built-in support for features like load balancing, fault tolerance, and distributed tracing, all capabilities of a well-thought-out microservices implementation and deployment. If not included in the particular framework you chose out of the box, many have middleware and plugins available that can support these features.

Challenges addressed by modern frameworks

Microservices come with unique challenges, and many of them are well-understood by the wider development community. But, imagine you’ve never built a microservice before or are trying to figure out how to take your monolith and break it down into microservices. Frameworks can help manage both of these scenarios by taking best practices and baking them directly into their design. This makes your development path forward much clearer, and because it’s directly spelled out in the framework documentation, it removes any guesswork or uncertainty you may have about this process for your services if you are inexperienced with developing microservices. 

For example, a framework can be very helpful with regards to data consistency, which is often tricky to manage. However, your chosen framework may automatically integrate with distributed transaction management tools to help maintain data consistency across independent services or service instances.

Another significant challenge presented by the distributed nature of a microservices architecture is monitoring and debugging. Most frameworks incorporate logging, tracing, and metrics tools to improve observability and debugging out-of-the-box, while potentially giving you options such as leveraging OpenTelemetry with a few minor changes. 

Frameworks minimize microservices challenges, which is why choosing one with a solid team and community behind it is important. A framework helps to guide teams toward building more resilient, maintainable, and scalable systems.

Best microservices frameworks

Now that we understand why choosing the right framework is important, let’s look at top choices across popular languages. First, we’ll start with popular options for Python and Go, followed by some other leading frameworks worth considering if you’re working in different languages.

Python microservices frameworks

The popularity of Python means that there are quite a few solid options for microservice frameworks. If you’re working in Python, you may already be using some of these frameworks in your current stack. This means that repurposing them for microservice development might be super simple to get started. Let’s take a look at two of the more popular options.

Django + Django REST framework

Django is a high-level Python web application framework initially released in 2005. Django has built-in features like object-relational mapping (ORM), a templating engine, and an admin panel. When paired with the Django REST Framework (DRF), it becomes a great solution for rapidly building RESTful APIs.

The highlights of this framework include:

• Comprehensive toolset: Offers a wide array of built-in components (ORM, templating, admin) that can accelerate building your services.
• Robust security: Features built-in protections against XSS, CSRF, and SQL injection.
• Strong community: Large and active user base, extensive documentation, and numerous third-party packages.
• Rapid development: DRF makes creating and managing APIs straightforward, letting you focus on business logic instead of reinventing the wheel to get endpoints up and running.

Of course, with the good also comes some challenges and drawbacks. For Django, these include:

• Feature-heavy for lightweight services: Out-of-the-box, Django can feel heavy for smaller or more lightweight microservices if you don’t need many or most of the included features. 
• Steeper learning curve: The breadth of built-in features can be overwhelming for beginners vs a smaller framework that’s more focused on simple, light-weight services.
• Performance: While suitable for many applications, Django might not match the raw speed of more performance-focused frameworks.

FastAPI

FastAPI is a high-performance web framework for Python, introduced in 2018. It was built on Starlette (for async server components) and Pydantic (for data validation), making it well-suited for creating fast and efficient APIs in Python 3.6+. Its emphasis on service performance and developer experience has allowed it to quickly gain popularity in the microservices world.

Highlights of FastAPI include:

• High performance: Designed to handle a large volume of requests with minimal latency.
• Automatic documentation: Generates interactive API docs with OpenAPI/Swagger by default.
• Developer-friendly: Clean and intuitive syntax, easy to learn for newcomers to Python or microservices.
• Asynchronous support: Natively supports async/await, which simplifies building highly concurrent applications.

Along with some downsides, which include:

• Growing ecosystem: While it’s rapidly expanding, FastAPI’s ecosystem and community are still maturing compared to more established frameworks like Django.
• Less feature-rich: You may need to integrate additional packages or write more boilerplate for complex use cases.

Go microservices frameworks

If you’re looking for performance, many developers head towards Golang-based frameworks. Known for its speed and lightweight nature, the Go programming language offers several strong frameworks with which to build microservices. Let’s look at two that are amongst the most popular.

Go Micro

Go Micro is a pluggable Remote Procedure Call (RPC) framework explicitly designed for building distributed services in Go (Golang). It provides foundational building blocks for service discovery, load balancing, and other distributed system essentials. Its design aims to simplify creating, connecting, and managing microservices for developers working in Go.

Some  key highlights of Go Micro include:

• Service discovery: Offers built-in mechanisms for microservices to register and discover each other.
• Load balancing: Includes out-of-the-box load-balancing capabilities for better scalability and reliability.
• Message encoding flexibility: Supports multiple encodings (Protobuf, JSON), allowing easy service interoperability.
• Pluggable architecture: Enables swapping out components (e.g., transports, brokers) to fit specific infrastructure needs.

Alongside the highlights, there are a few challenges that developers should be aware of as well:

• Less prescriptive: Go Micro’s pluggable approach leaves some architecture decisions up to the developer, which can overwhelm newcomers.
• Community size: While Go has a strong community, the Go Micro community is smaller than established frameworks in other languages.

Go Kit

Go Kit is a toolkit rather than a full-fledged framework, emphasizing best practices and core software engineering principles for microservices. It originated from the need to build microservices that focus on maintainability, reliability, and scalable design in Go without relying on a larger, more complex framework.

Highlights of this framework include:

• Layered architecture: Encourages separation of core business logic, transport code, and infrastructure, promoting clean design.
• Modularity and composability: Builds services with small, reusable components that are easy to test and maintain.
• Observability: Provides built-in support and patterns for logging, metrics, and distributed tracing.
• Best-practice guidance: Steers developers toward clear service boundaries, proper error handling, and interface-driven design.

Similar to the other libraries we discussed, the challenges for Go Kit include:

• Steep learning curve: The emphasis on best practices and patterns can be daunting for less experienced Go developers.
• Not a one-stop solution: Since it’s a toolkit, you might need additional libraries or deeper configuration to get all desired features.

Other top frameworks for microservices

Besides Python and Go, almost every other language has frameworks that can aid with microservice development. Although we can’t cover all of them within this blog, let’s look at a few other popular alternatives for languages such as Java and C# (.NET).

Spring Boot (Java)

If you work in enterprise Java, you’ve likely used or encountered Spring, one of the most popular Java libraries. Spring Boot is a widely adopted framework for building Java-based applications derived from the larger Spring ecosystem. Released in 2014, it simplifies Spring application development by reducing configuration overhead and providing production-ready features out of the box. 

For developers using Spring Boot as a Java microservices framework, highlights include:

• Convention over configuration: Automatically configures much of the application based on added dependencies.
• Embedded servers: Includes Tomcat, Jetty, or Undertow, eliminating the need for separate server deployment.
• Production-ready features: Offers health checks, metrics, and built-in externalized configuration.
• Extensive ecosystem: Leverages the vast Spring community and its robust set of libraries, including technologies like Spring Cloud.

With the upside also comes a few downsides and challenges that the framework poses for developers as well:

• Resource intensive: Spring Boot applications often consume more memory and resources than lighter frameworks.
• Complexity: While “auto-configuration” helps, the Spring ecosystem is large and can become complex for smaller-scale microservices.

Micronaut (Java, Groovy, Kotlin)

Micronaut is a Java Virtual Machine (JVM) framework introduced to address the performance drawbacks of traditional frameworks like Spring. It supports the Java, Kotlin and Groovy programming languages and uses ahead-of-time (AOT) compilation to reduce startup time and memory usage, making it particularly appealing for microservices and serverless applications.

Framework highlights include:

• Fast startup: AOT compilation pre-computes many framework related tasks, reducing startup times.
• Cloud-native: Provides integrations for various cloud services, facilitating development in containerized and serverless environments.
• Reactive support: Allows building responsive and resilient microservices through reactive programming models.

Downsides of Micronaut include:

• Smaller community: Micronaut is newer than Spring, so the user community and available resources, while growing, may be more limited.
• Learning curve: Switching from a more traditional Java framework to reactive programming may require new ways of thinking about software development.

Quarkus (Java)

Quarkus is newer but quickly gaining steam within the Java microservices space. Its growing popularity is due to its capacity for fast startup times and reduced resource consumption. It’s optimized for GraalVM and OpenJDK HotSpot. It focuses on containerized deployments and serverless functions, making it a popular choice for the Kubernetes-based infrastructures that are quite common within microservices deployments.

Working with Quarkus as their framework of choice, developers can expect to gain the following advantages:

• Container-first approach: Optimized for running in containers with minimal resource overhead.
• Kubernetes integration: Seamlessly supports service discovery, configuration, and health checks in Kubernetes environments.
• Reactive programming: Integrates with libraries like Vert.x for building reactive microservices and supports a functional programming style for those who want it.

They can also expect to come across these challenges as well:

• Younger ecosystem: While adoption is growing rapidly, Quarkus still trails more established frameworks in community size and third-party integrations.
• Less familiar: Developers deeply rooted in traditional Java EE or Spring might need a learning period to adapt to Quarkus’s approach.
• Learning curve: Reactive programming may be a large hurdle for teams not experienced with this way of developing microservices.

ASP.NET Core

If you’re working in C# or VB, you’ve likely come across Microsoft’s .NET framework. ASP.NET Core is Microsoft’s cross-platform, open-source framework for building modern web and cloud-based applications. First released in 2016 as a reimagining of the .NET ecosystem, it has evolved quickly to become a popular choice for high-performance microservices on Windows, macOS, and Linux.

This popularity comes from the inclusion of many of these highlights:

• High performance: Known for handling large traffic loads with minimal resource consumption.
• Modular design: This lets you include only the necessary components, keeping microservices lightweight.
• Rich ecosystem: Benefits from Microsoft’s extensive tooling, libraries, and a large community.

Even though the ecosystem is mature, there still are some downsides to using .NET Core, including:

• Evolving platform: Although mature, ASP.NET Core continues to advance rapidly; keeping up with changes and various releases can require ongoing effort.
• Windows-centric heritage: While cross-platform now, some developers may still encounter friction or have limited existing .NET exposure on non-Windows systems.

As with most technical decisions, each framework offers unique strengths. So, with so many choices, how should you evaluate the possible solutions to best suit your use case? Let’s look at how to break this down in the next section.

How to evaluate a microservices framework

With so many frameworks vying for your attention, how do you choose the right one for your project? There’s no one-size-fits-all answer; the best choice depends on your needs and priorities and other factors, such as the language you want to build with and the business capabilities needed. It’s also important to consider your team’s experience with the languages or frameworks you’re evaluating. However, here are some crucial factors to consider when evaluating a microservices framework.

Performance and scalability

Performance is at the core of microservices architecture. To accurately gauge how a framework will perform, you should consider a few key questions and match them to the capabilities of the framework. These questions include:

• How many requests per second does your application need to handle?
• How quickly/what latency is acceptable for a response?
• How well does the framework scale horizontally to handle increased traffic?

Overall resource consumption is another metric to consider when choosing the best framework for your needs, depending on the use case. Examining benchmarks and performance tests to fully understand the framework’s capabilities can help answer these questions.

Ease of integration with other tools

Microservices rely on various tools and technologies, such as databases, message queues, and monitoring systems. To ensure the framework will play nicely with the ecosystem you already have in place, you’ll need to ask questions such as:

• Does the framework support your cloud provider’s services and APIs?
• How easily does it integrate with other tools, such as those that provide logging and monitoring? Are these capabilities built in?

The ease of integration with the tools you are already using is a key factor in the speed and ease with which you can build microservices.

Learning curve and community support

It’s essential to consider both how easy it will be for you and your team to learn the framework and how easy it will be to work with it. A big part of this comes down to the documentation. Is it regularly updated, and does it cover all the bases? Alongside quality documentation, an active community, especially with an active forum or StackOverflow presence, is also an excellent resource for getting the most out of your chosen framework when you have questions. A framework with a gentle learning curve, excellent documentation, and a vibrant community can save you time and effort in the long run.

Comparison of Python vs. Go for microservices

Python and Go are popular choices for building microservices, but they have distinct strengths and weaknesses. Let’s compare these two languages to help you decide which fits your project better.

Which should you choose?

Ultimately, the choice between Python and Go depends on your context. Before making a decision, carefully consider your project’s requirements, the team’s expertise, and the strengths of each language. If a rich ecosystem of libraries for diverse tasks and improved developer productivity is important, and your team is already familiar with Python, that’s likely the way to go. On the other hand, Go is an excellent choice if performance and scalability are critical requirements and you need to build highly concurrent microservices. Of course, this only works if your team is already familiar with or willing to learn Go. Overall, most developers will first choose the language they are already using (or most familiar with) and then move to the tougher decision of the precise framework they want to use within the bounds of that language.

Future trends in microservices frameworks

New trends and technologies in microservices are constantly emerging to address the challenges and opportunities of building modern distributed systems. Here are some key trends shaping the future of microservices frameworks.

Enhanced observability

Frameworks increasingly integrate tools like OpenTelemetry to provide built-in support for monitoring, logging, and tracing, simplifying troubleshooting in distributed systems. As we move into the future, we expect many frameworks to either have observability built directly into them or easily integrate with existing observability technologies.

Stronger security

Zero-trust models and features like mutual transport layer security (mTLS) are becoming standard to secure service communication. Frameworks are also adding compliance-focused tools to address regulations like GDPR and CCPA. In the future, expect to see frameworks with best practices for these technologies and standards baked right in, making security a core piece of the framework without developers having to do much to enable it.

Cloud-native integration

Frameworks are evolving to work seamlessly with Kubernetes, serverless platforms, and service meshes like Istio, improving scalability, security, and traffic management. As containers and orchestration platforms become more popular, expect to see more specialized frameworks emerge that natively play within this domain.

Within microservice frameworks, these trends will continue to evolve and grow. Many of the future trends above are already well underway. The ecosystem, compared to just a few years back, has made immense strides in solving many of the issues discussed above. 

How vFunction can help

Whether you’re building a new application or modernizing an existing one, transitioning to microservices can be a complex undertaking.

vFunction simplifies the transition to microservices by automating architecture analysis,  identifying architectural issues, and enabling teams to build scalable, resilient applications. For those tackling aging frameworks, vFunction streamlines upgrades from legacy Java EE application servers and transitions older Spring versions to Spring Boot. After transforming your applications to microservices, vFunction continues to monitor architectural drift, enforce design patterns, and prevent sprawl, ensuring your microservices architecture remains efficient, scalable, and manageable over time.

Leveraging OpenRewrite, vFunction accelerates domain-specific framework upgrades, making monolith refactoring faster and more efficient for modern cloud-native environments.

Microservices architecture and governance

In addition to development work and the challenges of selecting a proper microservices framework, building new microservices presents significant architectural and deployment challenges. This can lead to unintended consequences like microservices sprawl or even a distributed monolith. While microservices are designed to promote modularity, poor architectural planning can result in tightly coupled services that share databases, create complex interdependencies, and violate the principles of loose coupling. This can make deployments increasingly difficult, as changes to one service may require synchronized updates across multiple others, negating the benefits of independent deployability.

Increasing number of services can overwhelm deployment pipelines, monitoring tools, and observability systems, making debugging and troubleshooting extremely difficult. Without clear boundaries and proper governance, teams risk building too many microservices and potentially creating a distributed monolith—an architecture where microservices are nominally independent but are so entangled that they behave like a single monolithic application, complete with all the scaling and reliability pitfalls of traditional monoliths. 

vFunction can help teams navigate the challenges of building or maintaining microservices architectures. Whether you’re designing new services or analyzing your existing microservices application, vFunction provides deep visibility into your architecture and ongoing governance to manage them.

Conclusion: building modern applications with the right framework

Microservices offer immense potential for building agile, scalable, and resilient applications. However, navigating the landscape of frameworks can be challenging. When it comes to choosing the right framework to develop microservices, the best choice is the one that matches your project’s needs (performance, scalability, etc.) and aligns with your team’s knowledge.

Learn how vFunction simplifies and accelerates the transition to microservices for existing applications while providing ongoing architecture governance to preserve their scalability and resilience.