The difference between a PoC and an MVP (and why confusing them costs you)
Just as there’s a lot of terminology in the data + AI world that causes confusion (you don’t need to know the difference between a data warehouse and a data lake; that’s what we’re here for), the same happens with terms from the operations of modern companies trying to be agile. Among them, there are two in particular that tend to generate a lot of conflict: the PoC (proof of concept) and the MVP (minimum viable product).
A PoC answers a question / An MVP solves a problem.
The distinction is really simple, once you know what you’re looking at.
A proof of concept (PoC) exists to answer one question: could this be done? Pay attention to the verb tense: here we’re in the realm of the potential. The goal is to start from a bounded part of the problem, try to solve it with the real data available (or something close), and see if the idea works or not. Basically, it’s an experiment: it has no users, often not even a UI, it doesn’t run on its own, etc. What we’re looking for, to state the obvious, is to prove the concept: see if it’s feasible or makes sense.
A minimum viable product (MVP) answers a more complex question: does this work in the real world? Here we’re talking in present tense, about something much more solid and looking toward the immediate future. It’s a development that (at least partially) must run in production, has to integrate with existing systems, and must be given to someone who had nothing to do with the development process and be able to operate it. Put simply, it’s the smallest and most reduced possible version of something that actually solves a particular problem: maybe not with all the features it could have, but complete in its origin and use case.
At this point, it’s worth clarifying that one isn’t “better” than the other. It’s not a difference of quality, but of purpose and specific function: the PoC has to demonstrate that something is possible, and the MVP has to demonstrate that it’s useful.
The gap between PoC and MVP is where projects die
This is where the first sparks tend to appear: the PoC works, generates a lot of enthusiasm, and whoever received this development asks to put it in production. Not understanding the difference between both concepts is what generates a lot of discomfort when the stakeholder (understandably anxious to see results) assumes that the step between one and the other is just pressing a button (or worse, a matter of sheer will). But unfortunately, reality isn’t always that simple.
You might never have thought about it, but look at the concrete differences between one and the other:
- Automated data ingestion. In the PoC you downloaded a CSV, a JSON, some kind of data, and treated it manually. In production, that data has to arrive on its own, every day, without anyone doing anything. And it goes without saying it has to arrive clean, tidy, and ready to use: no ad-hoc fixes.
- Infrastructure. The PoC ran on someone’s machine. And the phrase “but this can’t be, it works on my computer” has been said by thousands or millions of devs in history for a reason: the MVP has to run somewhere other than the person’s machine who created it.
- Monitoring. In a PoC, there’s no such thing as “error,” assuming you’re in the validation phase of a solution. But the MVP is already doing real things, so when something inevitably errors, fails, or breaks, you need to find out as soon as possible.
- Retraining. The PoC model was trained once. But an MVP will likely require retraining or adjustments as the data changes.
Each of these points is real work, with real hours, that costs real money.
PoC of churn vs. MVP of churn
To make it even more concrete, let’s go with a very specific example. Let’s say someone asks us for a model to predict which customers will leave the company: nothing more, nothing less than a classic churn model.
If what you need is a PoC, that means taking historical data, training a model, and measuring if it predicts better than what you have today. You probably need two to three weeks of a data scientist’s time and some spare time from a data engineer to help with an initial data dump. At the end, you’ll have a prototype notebook, a couple of metrics, and an honest answer about whether the idea is feasible.
Now, if what you need is an MVP, that means connecting data sources, building a pipeline that runs automatically, handles data quality analysis, training the model, defining decision thresholds with the business team, building a UI where someone can see results, monitoring performance, and defining when and how to retrain. Here we’re talking about, depending on the case and scale, between two and six months of work from a team with data scientists, engineers, analysts, and probably someone with a more product-minded vision.
What is “a churn model”? Both. But one costs ten times more than the other, because one is a promise with many guarantees, but the other generates real, tangible impact.
And this is what you should take away from this article: if you don’t have a frank and honest conversation about which of the two you’re buying before starting, the chances of the project ending well drop considerably.
When does each make sense
There’s no one better than the other. It’s a matter of timing.
Do a PoC when:
- You don’t know if the problem can be solved with the data you have.
- You need to convince someone internally that it’s worth investing.
- You want to compare approaches before committing to one.
Do an MVP when:
- You already know the problem can be solved (ideally because you already did a PoC and have the decisions and findings well-documented).
- There’s a real user who’s going to use this every week.
- You have budget to build not only the model but everything around it.
- There’s someone internal who’s going to own this when it ends.
The question worth asking
Before starting any data or AI project, there’s a question that should be asked in the first meeting:
Is the goal of this project to know if something can be done, or that something works in production?
If it’s the first, it’s a PoC. It has a scope, a cost, and a timeline. If it’s the second, it’s an MVP. It has a different scope, different cost, and different timeline. And if you don’t know which one you need, that’s exactly the conversation to have before talking about budget.
