"How much does an AI MVP cost" doesn't have one honest answer, because the range is genuinely wide, and most of the variation comes down to a handful of decisions made early, not the AI itself.
What actually drives cost
The biggest cost driver usually isn't the AI model, it's the surrounding product: how many user flows need to be built, whether there's a real backend and database behind it, how much of the interface needs custom design versus a simpler functional layout, and how much integration work is needed with your existing tools and data.
Ongoing cost versus build cost
A separate budget line that's easy to underestimate is the ongoing cost of running the AI model itself once real users are on it. A prototype tested by one person costs almost nothing to run. The same product at a few hundred active users can look very different on the model bill, depending on how the calls are structured. Building in caching, sensible limits, and cheaper models for simpler tasks (reserving the most expensive model only for the steps that actually need it) makes a real difference here.
Timeline expectations
A narrowly scoped MVP, one clear core feature, built cleanly, is realistic in a matter of weeks rather than months. Trying to launch with five features at once instead of one is usually the single biggest reason timelines slip, more than any technical difficulty with the AI itself.
The honest advice
The MVPs that come in on budget and on time are almost always the ones scoped down to a single core job first. It's tempting to want the "full vision" in version one; it's rarely the version that actually ships on schedule.