Mudit Singh is the Co-Founder and Head of Growth at TestMu AI, an AI-native unified enterprise test execution cloud platform.
There is a strange contradiction in every engineering metric I have looked at this year.
Developers using AI coding assistants are merging 98% more pull requests, and epics completed per developer are up 66%. Software development has never been faster.
And yet, in the same datasets, something else is happening. Time in code review is up 441% according to the latest DORA telemetry.
Thirty-one percent of PRs are now being merged without any human review. Change failure rates are creeping up across nearly every team that adopted AI heavily in 2025.
The 2025 DORA report from Google Cloud put it bluntly: AI does not automatically improve software delivery performance. It amplifies what is already there. It magnifies the strengths of disciplined organizations and the dysfunctions of fragmented ones.
The bottleneck in software has quietly moved. It used to be writing the code. Now it is trusting the code.
The Build-Vs-Buy Conversation Every Leader Is Having
Walk into any engineering leadership meeting in 2026, and you will hear the same exchange. The developer team has been using AI coding assistants for six months. Tests are getting authored faster. Someone says: “We could just build our QA stack on these tools. Why pay another vendor?”
This is a fair question, and worth taking seriously. But build or buy in 2026 is now an architecture question.
It Is An Architecture Question, Not Just A Cost One
AI coding agents are designed primarily to generate code. Using the same tool category for both code generation and code verification raises a worthwhile question for engineering leaders: Where in the workflow does an independent check belong? A verification layer that shares assumptions with the system being verified is something teams should think carefully about as they design their testing strategy.
Yes, Your Team Can Build It. The Real Question Is Why
Your team is capable of building something. The real question is why, when proven AI testing platforms already exist, refined over years of working with thousands of QA teams. Building your own version means re-creating what is already solved, while your competitors ship a product.
The Cost Arc That Procurement Spreadsheets Miss
The cost looks deceptively cheap on a procurement spreadsheet. A few seats of an AI coding tool look like a rounding error compared to a dedicated testing platform. But the per-seat sticker price has almost nothing to do with what a production-grade AI testing capability actually costs by the end of year one, especially in regulated industries where compliance, audit posture and data governance add significant overhead.
To be fair, AI coding agents wired up with open-source browser automation frameworks do work for one specific job: unit-level code verification on a single developer’s machine. A few hundred lines of test code, run locally against a known branch. That setup is real and worth using.
The problem is what happens when you scale it. At the organizational scale, the same setup requires context retrieval across hundreds of services, a self-hosted execution grid that runs across browsers and real devices, a custom reporting layer, dashboards for non-engineer stakeholders and a senior engineer who maintains it all.
The $19 AI coding agent subscription was never the full bill. It was the down payment on a multiyear platform project.
When You Build A QA Stack, You Now Have Two Products
Here’s a dynamic that’s easy to underestimate at the outset. When a team builds its own AI testing platform, the maintenance burden tends to grow over time.
Engineers who set out to test the product can find themselves increasingly maintaining the very tool that tests it. You started with one thing to ship and now have two.
There’s a related effect worth weighing. A testing platform built by developers, on top of developer tooling, often stays usable mainly by developers. Triaging a flaky test means reading a custom reporting layer; updating a test means understanding the internal context engine.
The risk is that the broader QA function loses the ability to operate the system meant to support it.
This is the gap an independent verification layer is meant to address: something that sits above the coding-agent layer and validates rendered output the way a real user would, rather than the way the code’s author would.
Out-of-the-box AI-native testing platforms are designed around exactly that separation.
This matters specifically because of the failure modes a DIY stack runs into. AI-generated tests are brittle by default: They rely on the structural representation of a page rather than on what a user actually sees.
None of this means building is wrong for everyone. If testing infrastructure is genuinely core to your differentiation, or you have requirements no platform meets, owning the stack can make sense. For most teams, though, the honest question is whether that ongoing investment is better spent on the product itself.
How Leaders Should Think About This Decision
Three principles tend to clarify the build-or-buy testing decision better than any spreadsheet.
Build what differentiates you. Buy what doesn’t. Your product is your business. Your testing infrastructure is not. A custom-built QA platform does not create a competitive moat. It consumes engineering capacity that would otherwise go into the product.
Time-to-value matters more than ever. A DIY AI testing stack takes six to 12 months to reach feature parity with a mature platform, before maintenance enters the equation. Every week your team spends building the tester, your competitors spend improving the product.
Subscription costs scale linearly. Maintenance costs scale exponentially. Over a three-year horizon, maintenance always dominates.
The Bottom Line
Build or buy is more than a procurement decision. It has become a strategic one.
Ask yourself: When proven platforms already solve the problem, and when every quarter spent building is a quarter your competitors spend on the product, should you build a new solution?
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