Today, we’re thrilled to sit down with Vijay Raina, a renowned expert in enterprise SaaS technology and tools, and a thought leader in software design and architecture. With years of experience shaping innovative solutions for complex challenges, Vijay has been instrumental in guiding teams and organizations through the intricacies of API development and scalable software systems. In this interview, we dive into the evolution of API tools, the impact of AI on development workflows, strategies for scaling engineering teams, and the future of software interfaces. Our conversation explores how personal frustrations can spark groundbreaking tools, the power of customer feedback in driving product roadmaps, and the exciting intersection of APIs and agentic technologies.
How did early challenges in your career with API development influence your approach to designing tools for developers? Can you share a memorable moment from those initial struggles that shaped your vision?
I’m glad to share that story. Back in the late 2000s, while working on a project at a major tech company, I was tasked with building an application that relied heavily on APIs. The constant frustration of broken APIs, missing documentation, and the sheer stress of wondering if a build would even work was overwhelming. I remember one late night, staring at my screen, trying to debug an API call with no clear error message—just pure trial and error. It felt like I was lost in a maze with no map. That moment stuck with me and became the spark for designing tools that prioritize simplicity and collaboration. I wanted something that would cut through the clutter, something that developers like me could use intuitively without needing a PhD in deciphering enterprise software. From that frustration, I started sketching out ideas for a tool that would let teams share API knowledge seamlessly, ensuring no one else had to endure those sleepless nights of guesswork. It was about creating a space where developers could focus on building, not troubleshooting.
Scaling an engineering team from a small group to hundreds sounds daunting. What was one of the biggest hurdles you faced during such rapid growth, and how did you navigate it?
Scaling a team from just a handful of founders to over 350 engineers is no small feat, and the biggest hurdle for me was maintaining alignment as we grew. Early on, when we were just a tight-knit group, communication was effortless—we’d huddle in one room, sometimes even sleeping at the office, hammering out code together. But around the time we hit 50 people, I recall a specific project where miscommunication between the client-side and backend teams led to a major delay in a critical feature rollout. It was a gut punch; customers were waiting, and we were scrambling to fix something that could’ve been avoided with better coordination. We realized our informal “everyone knows everything” approach wouldn’t scale. To address it, we introduced structured communication layers, focusing on singular goals like customer scaling or revenue retention to unify efforts. We also started cross-functional units with product managers and designers to ensure clarity of purpose. It wasn’t perfect overnight, but over time, we iterated on these processes, learning to balance autonomy with alignment. That experience taught me that growth isn’t just about numbers—it’s about evolving how you connect as a team.
Collaboration seems to be a cornerstone of successful API tools. How did you recognize that focusing on team collaboration could transform a tool into a viable business model?
Recognizing collaboration as the key to a business model came from observing how developers actually used APIs in real life. Early on, I noticed that APIs aren’t just built for one person—they exist to connect teams, systems, and ideas. I recall running a beta program with around 600 to 700 companies, and the feedback was crystal clear: managers and businesses wanted a tool that didn’t just help individuals but unified their teams. There was this one conversation with a tech lead who said, “I love this tool, but I need my whole department to be on the same page—can you make that happen?” That was a lightbulb moment. We started experimenting with pricing models, initially trying open-source donations and in-app purchases, which barely paid the bills, let alone scaled. But when we shifted to a subscription model for businesses while offering more free features to individual developers, the response was overwhelming. People were willing to pay for something that solved a real team need. It felt like we’d cracked a code—build for the lone coder, but monetize through the power of the group. That balance became our foundation.
AI is reshaping so many aspects of software development. How have you leveraged AI to handle the overwhelming volume of developer feedback, and can you walk us through a specific instance where it made a difference?
AI has been a game-changer for us in managing feedback at scale. With over 2,000 open feature requests and 13,000 comments piling up, manually sifting through them was like trying to drink from a firehose—it took months to onboard new engineers or PMs just to grasp the landscape. We built AI agents on our platform to aggregate and summarize this feedback, pulling from support channels and customer conversations. I remember a particularly tricky issue where users were frustrated with a workaround for an API testing feature; the feedback was scattered across hundreds of comments, and we couldn’t pinpoint the root cause. Using the agent, integrated into our Slack workflow, we asked it to cluster related complaints and suggest solutions. It distilled the noise into a clear summary, linking back to original comments so we could verify its analysis, and even highlighted a workaround some users had tried outside our tool. This saved us weeks of manual digging, and within days, our team prototyped a fix that addressed the core pain point. It wasn’t just about speed—it was about empowering every engineer to tackle complex issues without drowning in data. We’re still refining how we use these agents, but they’ve brought a level of clarity I didn’t think was possible at this scale.
APIs are increasingly vital in the AI era, especially for connecting systems to live data. How did you identify this opportunity early, and what’s a standout reaction from a user that validated your direction?
Seeing APIs as the backbone of AI wasn’t a sudden revelation—it came from understanding that AI, especially large language models, needs real-world context to be actionable. Early this year, when we launched MCP server generation on our public network, allowing users to connect public APIs to LLM-based agents, I knew we were onto something big. The need was evident: AI can chat all day, but without APIs, it can’t execute tasks or interact with live systems. I’ll never forget a user at a developer meetup who approached me after trying the feature—he was almost giddy, saying, “I just turned a static API into something my AI tool can actually act on; this changes everything for my workflow.” His excitement was palpable, and it hit me how transformative this could be. Behind the scenes, we’d spent months ensuring the integration was seamless, from generating MCP servers to enabling connections with tools like Cursor. Now, as we extend this to private APIs, the demand is even louder. It’s been a complex journey of engineering work to balance context windows and coherence in LLMs, but moments like that user’s reaction keep us pushing forward.
With a company growing to over 850 people, communication must have been a challenge. Can you share a specific breakdown you encountered during this expansion and how you resolved it?
Growing to 850 people brought communication challenges I hadn’t anticipated. In the early days, when we were just a few dozen, we could literally fit in one room—ideas flowed freely, even if we were exhausted from coding marathons. But as we scaled, I recall a pivotal breakdown around the time we hit 200 employees. A critical product update got delayed because the engineering and sales teams were completely out of sync on customer priorities—one side thought we were focusing on scalability, while the other pushed for new features based on client promises. The disconnect cost us weeks and frustrated key accounts; it felt like we were rowing in opposite directions. To fix it, we introduced a unified goal framework, tying every team’s efforts to a single metric like customer retention. We also implemented regular cross-team syncs and brought in product managers to bridge gaps between technical and business needs. It was messy at first—there’s no overnight fix for trust issues—but over time, these structured layers rebuilt our rhythm. That breakdown taught me that communication isn’t just about talking; it’s about ensuring everyone sees the same horizon.
Listening to customers seems central to your product evolution. How do hands-on interactions like reverse demos shape your roadmap, and can you recall a surprising use case that led to a feature change?
Customer interactions are absolutely critical to shaping our roadmap, and reverse demos—where customers show us how they use our tools—are pure gold. These sessions often reveal insights no survey or ticket can capture. I remember meeting with an engineering team from a mid-sized enterprise who were using our platform in a way I’d never imagined: they were mocking out entire API workflows before writing a single line of code, essentially using it as a design sandbox. I was floored; we’d built the tool for testing, not pre-coding ideation, and watching them walk through their process was like seeing a whole new world open up. Their feedback during that demo—about needing better visualization for mocked responses—stuck with me. We took that back to the team, prioritized a feature to enhance mock server capabilities, and iterated on UI elements to make those visualizations intuitive. Within a few months, that feature became one of our most adopted updates. Moments like that remind me why we sit with customers—it’s not just about fixing bugs; it’s about discovering how they dream with our tools.
You’ve spoken about the future of software design moving toward less UI and more adaptive interfaces. What trends are driving this belief, and how do you envision this shift playing out in your own tools?
I’m convinced we’re heading toward less UI and more adaptive interfaces because of how user expectations and technology are converging. Today, users demand instant customization—think of how AI is already letting people tweak experiences on the fly without wading through menus. I’ve noticed in our own tools that overly complex UIs can frustrate developers; for instance, we had a feature interface that buried key API testing options under layers of clicks, and feedback showed users felt bogged down. It was a wake-up call that static designs won’t cut it anymore. My vision is to evolve our platform so users can simply instruct the interface to adapt—say, “rearrange this dashboard for my testing flow”—and have it happen in real time. We’re already experimenting with AI-driven prototyping to minimize static design docs, and the next step is embedding adaptability directly into the user experience. Over the next few years, I see us stripping away unnecessary UI elements, focusing on core interactions, and letting the software mold itself to the user’s intent. It’s about making technology feel like a conversation, not a puzzle.
As APIs evolve to support conversational agent interactions, how do you see your tools adapting to these new patterns? Can you share an experiment or direction your team is exploring?
The shift toward conversational agent interactions is reshaping APIs in fascinating ways, and we’re actively adapting to meet that wave. With agents becoming a new app form factor, APIs need to move beyond dumping massive JSON outputs to enabling more dynamic, dialogue-like exchanges. One pattern we’re exploring is making API responses more modular and context-aware, so agents can interact as if they’re in a back-and-forth conversation rather than a one-way data dump. Right now, our team is experimenting with MCP-based interactions, tweaking how APIs structure outputs to better suit agent-to-agent communication. We’ve set up sandbox environments where we simulate agent workflows—think of two AI systems negotiating data access—and test how our platform can streamline those exchanges with minimal human oversight. It’s early days, but the goal is to create APIs that feel less like rigid endpoints and more like flexible partners in a dialogue. Watching these experiments unfold, I’m reminded of the early internet days when no one quite knew what would stick—it’s thrilling to be building at the edge of what’s possible.
Looking ahead, what is your forecast for the role of APIs in the broader landscape of AI and agentic technologies over the next few years?
I’m incredibly optimistic about the role of APIs in the AI and agentic tech landscape over the next few years. I believe APIs will become the critical bridge that transforms AI from a conversational novelty into a powerhouse of actionable outcomes—think agents executing real-world tasks like payments or secure transactions through robust API connections. We’re already seeing early signs of this with tools like MCP, and I predict a surge in demand for new interaction patterns as every company either builds or integrates agents. My forecast is that within four years, the majority of API development will prioritize agent-to-agent communication, requiring standards and practices we’re only beginning to define. This will also bring challenges around trust and security, much like the early internet faced with open servers, but it’s an exciting problem to solve. I think we’ll look back and see this era as the moment APIs became the nervous system of a truly interconnected, intelligent digital world.
