What an AI MVP really costs
By Virgil Nilson, founder of Park23
“How much will it cost?” is the first question every founder asks, and the one most agencies dodge. Here’s a straight answer, based on what we see building AI products for founders.
The short version: a focused AI MVP built by a senior team usually costs somewhere between $15,000 and $50,000, and takes four to ten weeks. The rest of this article is about why yours might be at either end, or outside it entirely.
The four kinds of “AI MVP”
People mean very different things by an AI MVP, and the price follows.
| What you’re building | Typical cost | Typical timeline |
|---|---|---|
| A prototype to prove the idea | $5,000 – $15,000 | 1 – 3 weeks |
| A focused MVP for first paying users | $15,000 – $50,000 | 4 – 10 weeks |
| A rescue of an app built with Lovable, Replit or similar | $5,000 – $25,000 | 2 – 6 weeks |
| A full platform with several user types, billing and integrations | $50,000 and up | 3 months and up |
These are typical ranges, not quotes. Your scope decides the number.
A prototype can be surprisingly quick. With Explainr, we went from an idea to a working AI tutor, one that narrates any PDF as a lesson and answers spoken questions about it, in about a week. That’s the right first step when the big question is “will this work at all?”
What pushes the price up
- Real-time anything. Live updates, trading, collaboration and voice all need more engineering than a page that loads once.
- Money. Subscriptions, credits, marketplaces and payouts need careful work, because mistakes cost real money.
- Multiple user types. Every role (admin, customer, staff, partner) means more screens and more permission rules.
- Integrations. Each outside system, like a CRM, an accounting tool or an industry API, adds work, especially if its documentation is poor.
- Accuracy that matters. If the AI’s answers have legal, medical or financial weight, you need testing, guardrails and human review, and those take time.
- Your data. Clean, well-organized data is cheap to work with. Data scattered across PDFs, spreadsheets and inboxes is not.
What brings it down
- A narrow first version. One user type, one core workflow, done well. Most MVPs try to do three products at once.
- Using models, not training them. Almost every MVP should use existing models from OpenAI, Anthropic, Google or open-source providers. Training your own is rarely worth it before you have users.
- Starting from what you have. If you’ve already built a version in an AI builder, the screens and flows you’ve worked out are real progress. Rescuing it is usually cheaper than starting over.
The costs founders forget
The build is a one-off. These aren’t.
- AI model usage. Every question, summary or generated image costs money. For most early products it’s tens to hundreds of dollars a month, but it scales with usage, and a few heavy users can surprise you. This is worth modeling before you set your prices.
- Hosting and services. Database, hosting, email, file storage and monitoring. Usually modest at the start.
- Ongoing development. No product is finished at launch. Real users find bugs and ask for features. Budget for continued work after launch, either as a monthly plan or in milestones.
How to get a real number
Every estimate that matters starts with the same few questions: what exactly the first version must do, what data it runs on, what it connects to, and how good the AI’s answers need to be. Answering them properly is most of the work of quoting.
That’s what our Launch Blueprint does. In five business days, we test your idea on a sample of your real data and work out the models, running costs and architecture. You get a clear plan and a fixed quote, and the full price is credited toward your build. Or book a free strategy call and I’ll give you a ballpark on the spot.