What Is POC in Software Development?

A POC, or proof of concept, is a small technical exercise built to answer one question: can this specific approach actually work? As of August 2026, after 15 years shipping platform software in regulated industries like healthcare, where "can this even work" had to be answered before anyone signed off on a budget, I still see teams confuse a POC with a small version of the finished product. It isn't sold, shown to real users, or judged on polish. It's built to remove one specific kind of doubt: technical feasibility, not market demand. Get that distinction wrong, and you either burn a quarter proving something nobody was worried about, or you skip the proof entirely and find out the hard way, mid-build, that the approach doesn't scale.

What is a POC in software development?

A POC, or proof of concept, is a small technical exercise built to answer one question: can this specific approach actually work? It isn't sold, shown to real users, or judged on polish. It exists to remove technical doubt before anyone commits budget or a team to building the real thing.

Here's what I keep coming back to: a POC answers exactly one question, and the discipline is picking that question correctly. "Can we sync this legacy database to a modern API without corrupting records" is a POC question. "Will customers pay for this" is not.

I've watched engineers spend three weeks polishing a POC's error handling and logging. None of that mattered. The only thing that mattered was whether the core mechanism worked, and every hour spent past that answer was an hour taken from somewhere else.

Why do teams build a proof of concept?

Teams build a POC when the biggest risk isn't whether users want something, it's whether the technology can deliver it at all. A POC answers that question cheaply, with throwaway code, before an engineering team spends months building on an assumption that turns out to be false.

In regulated platform work, that risk shows up constantly. Can this integration meet the audit trail requirements. Can this model run inference fast enough on the hardware a clinic actually owns, not the hardware a vendor demo used. Those are expensive questions to get wrong after the real build starts.

A POC is deliberately disposable. I've written POC code I knew would never see a production repository, because keeping it clean would have cost time the exercise didn't need. The only deliverable is the answer: yes, this works, or no, it doesn't, and here's why.

How is a POC different from an MVP?

A POC proves an approach is technically possible. An MVP proves people want the resulting product. A POC can be ugly, single-purpose, and thrown away. An MVP has to be a real product real users can actually use, because it's answering a demand question, not a feasibility one.

I wrote about this distinction in more depth in what an MVP actually is, but the short version is that these two terms answer different risks, and treating them as interchangeable stages of the same ladder is how teams lose months. A POC that works doesn't mean users want it. An MVP that users like doesn't mean the underlying approach will hold up at scale.

I keep a version of this table in my head on every new platform effort. The moment a team can't say which column they're in, that's the moment the timeline starts slipping.

How is a POC different from a prototype?

A POC tests one narrow technical unknown in isolation, often just a script or a spike, with no interface at all. A prototype tests how a whole experience feels or flows, usually with a visual interface a stakeholder can click through. A POC proves capability; a prototype proves shape.

I use both, and I've seen teams reach for the wrong one more than once. A prototype in Figma or a clickable frontend answers "does this flow make sense to a human." It doesn't touch the hard backend question sitting underneath it.

A POC skips the interface entirely, most of the time. Nobody needs a polished screen to know whether a batch job can process a million records in the maintenance window. That's a terminal output and a stopwatch, not a design review.

Analysts like Gartner have written for years about how many AI proofs of concept never make it to production, and in my experience the gap usually traces back to exactly this confusion: a team proved a model could technically produce an answer, then mistook that for proof the whole product was ready.

When should you skip the POC?

Skip the POC when the technical approach is already well established, like building a CRUD app with a mainstream framework, because there's no real feasibility doubt to resolve. Also skip it when the honest risk is whether anyone wants the thing, not whether it can be built. That's an MVP question.

The Project Management Institute's guidance on feasibility work draws the same line: a proof step earns its cost only when a real technical unknown exists, not as a default ritual before every project. I've sat in kickoff meetings where a POC got added to the plan out of habit, for an integration the team had built the same way a dozen times before. That's not risk reduction. That's a stalling tactic wearing a process badge.

The honest question to ask before scoping a POC is simple: what specifically don't we know how to do yet? If nobody in the room can answer that in one sentence, you don't need a POC. You need to start building, or you need to go find out whether anyone wants what you're about to build, which is a different exercise entirely, closer to what I described in what an MVP actually is.

The same discipline that says "prove the risky part first" is the same judgment call I wrote about in what AI has and hasn't replaced in software engineering: the tools can help you write the throwaway code faster. They can't tell you which question is actually worth proving.

I write more about how I make these calls, and about the fifteen years of specific mistakes behind them, on the About page.