In August 2024, vFunction was named a Gartner® Cool Vendor in AI-Augmented Development and Testing. The recognition validated our vision for modernizing application architecture—providing deep visibility into an application’s structure and proactively addressing architectural technical debt. This transforms monoliths into modular microservices-based apps that boost scalability and engineering velocity. One year later, with the rise of GenAI coding assistants like Amazon Q Developer and GitHub Copilot, this recognition feels even more relevant—cementing architectural modernization as a critical enabler of AI-augmented development.
AI is transforming how software gets built. It can generate code, suggest fixes, and even refactor functions. But AI works at the code level, not the system level. Without understanding runtime behavior, GenAI risks making complexity worse, unable to understand the logical domains and how to best modularize the code..
The brownfield reality
This challenge is especially acute for brownfield applications. Unlike greenfield projects, which represent a small fraction of what enterprises run today, the vast majority of critical systems are brownfield: large, complex applications with years of accumulated dependencies, complex flows, and architectural drift. Recent stats show that more than 60% of IT budgets is spent simply maintaining these systems. These are the critical apps that drive revenue, support operations, and enable customer experiences.
At the extreme end are the “megaliths”—applications with over 20,000 classes and more than 10 million lines of code. These applications are so large and tangled that even incremental change feels daunting. Some teams have already lifted and shifted them to the cloud, but their monolithic nature prevents them from realizing the cloud benefits. Others are still in the process of migrating and face the same challenge.
At that scale, small inefficiencies multiply quickly, slowing engineering velocity and triggering resiliency issues. Simple containerization—or relying on code assistants without architectural context—can’t address the complexity, modularize the code, or truly solve these problems.
Why architectural context matters
That’s why architectural context is essential. Without it, GenAI can’t solve the challenge of modularization—breaking monoliths into smaller, well-structured services. This is critical for taking full advantage of cloud-native services and reducing complexity and technical debt.
Without runtime-based architectural context, GenAI struggles to:
- Scale: ingest or analyze large code bases (e.g., 2M+ lines of code)
- Understand dependencies: It only reads code, missing runtime behavior
- Recognize system-wide structures like context, components, and business or logical domains.
With it, breaking monoliths into modular services or microservices becomes not only possible, but predictable and successful.
Analysts expect the global market for modernization services to grow from US$19.82 billion in 2024 to US$39.62 billion by 2030, expanding at a 15% CAGR. By bringing architectural context into the GenAI era, we ensure that coding assistants like Amazon Q Developer and GitHub Copilot aren’t just generating more code, but are helping teams refactor dependencies, modularize monoliths, and extract and transform services—the real work of sustainable modernization.
Making developers’ lives easier
Since our original recognition, we’ve continued to build toward this future—expanding our platform to focus on the architectural modernization of today’s megaliths. Architectural modernization transforms monoliths into modular domains or microservices, allowing workloads to leverage cloud-native services like Amazon EKS, Lambda, and Azure Functions—harnessing the scalability, elasticity, lower costs, and faster innovation of the cloud.
Architectural modernization transforms monoliths into modular domains or microservices, allowing workloads to leverage cloud-native services like Amazon EKS, Lambda, and Azure Functions—harnessing the scalability, elasticity, lower costs, and faster innovation of the cloud.
The opportunity is so massive and so critical to how enterprises and ISVs will evolve that we’ve made it a primary focus of our strategy. Since our original recognition, we’ve expanded our platform to focus on the architectural modernization of monoliths—extending to today’s massive megaliths—and bringing architectural context into the GenAI era.
Deepening our work with cloud partners
At the same time, we’ve broadened our collaboration with hyperscalers AWS and Microsoft, taking on new competencies and joining programs like AWS Workload Migration and ISV Accelerate. These partnerships not only validate our approach but also fund licenses for vFunction to support architectural modernization efforts, making the path forward faster and more cost-efficient for our customers.
GenAI-powered remediation
What does all this mean for developers at the heart of our Cool Vendor recognition for AI-augmented development? This means less time wrestling with legacy complexity and more time building features that matter.
Using our Visual Studio Code extension or MCP (Model Context Protocol) server, vFunction bridges the gap between architecture and code assistants, making architectural transformation fast, actionable, and fully embedded in the SDLC. The sweeping made over the past year allow vFunction to connect to modern developer environments, enabling teams to:
- Query architectural issues in plain language
- Generate GenAI-powered remediation prompts
- Trigger fixes—all from the command line or inside their IDE
By surfacing the architecture in real time and guiding AI code assistants with context, vFunction helps developers move faster, avoid rework, and keep technical debt in check. And through integrations with Jira and Azure DevOps, vFunction pushes prioritized architectural to-dos straight into the backlog, ensuring nothing gets lost between discovery and execution, and automatically validates that remediations are completed.
From Cool Vendor to critical capability
When we were recognized as a Cool Vendor in 2024, architecture in the SDLC was still emerging. In today’s AI-driven landscape, it has become essential. AI alone isn’t enough. But AI + architecture is a powerful combination; one that equips organizations to innovate faster, modernize with confidence, and build resilient, scalable applications for the cloud.
And it’s not just theory. We’re now helping enterprises and ISVs tackle some of the world’s largest applications in automotive, financial services, telecom, and manufacturing, to name a few—modernizing them into modular, cloud-ready systems. If a 10-million-line megalith can be transformed, any system can. With the right architectural context, no application is too complex to modernize.
At vFunction, that’s the future we’re helping our customers create every day.👉 Ready to see how architectural modernization empowers AI-augmented development? [Request a demo]
