First, a distinction that decides your entire budget: an AI agent is one workflow inside your business; an AI MVP is a product you're putting in front of users. The agent needs to work. The product needs to work, be signed into, be paid for, be supported, and survive people using it in ways you didn't plan. That's a different cost structure, and conflating the two is why so many AI product budgets are wrong by a factor of three. If you want the one-workflow number instead, that's what an AI agent costs.
For an AI product MVP, the mid-market range lands in the $50,000–$500,000 window — and where you fall inside it has almost nothing to do with the model you pick.
What "MVP" has to mean when there's a model in it
In conventional software, an MVP is the smallest thing that delivers value. With AI in the loop, that definition quietly breaks, because the smallest thing that *demos* value and the smallest thing that *survives contact with users* are far apart. A deterministic MVP either works or throws an error. A probabilistic one is wrong 4% of the time, silently, in front of a customer.
So an AI MVP has a floor that a normal MVP doesn't: it needs an eval suite, traces you can debug from, and guardrails around the worst case. Those aren't scope you can defer to v2 — they're what makes v1 shippable at all. Budget them in from the start or pay the retrofit price, which is materially higher.
The four cost centres
| Cost centre | Typical share | What drives it |
|---|---|---|
| Product engineering | 40–50% | Auth, billing, admin, the actual UI — the unglamorous majority |
| AI engineering | 20–30% | Prompts, retrieval, orchestration, evals, guardrails |
| Data & integration | 15–25% | Getting the knowledge into a queryable shape; third-party APIs |
| Inference | 1–3% | Token spend. Cents per transaction, and falling |
That last row is the one that surprises people. Teams spend weeks agonising over model pricing and then underfund the data work by a factor of ten. Processing ten thousand conversations on a fast model runs about $37 in tokens (Anthropic), and the cost of reaching any given capability level has been falling roughly 40× per year (Epoch AI, 2025). Whatever inference costs in your model today, it costs less next quarter. The engineering around it does not.
What actually moves the number
- How many users, and are they yours? An internal tool for 30 colleagues and a product for 3,000 strangers are different builds. Strangers need onboarding, self-service recovery, and support surfaces; colleagues can be trained and can ask you.
- How wrong is too wrong? A drafting assistant a human reviews is cheap. A system that acts without review needs the full reliability stack. This single decision can double a budget.
- Whose data, and where does it live? Clean data in a queryable system saves weeks. Data in inboxes, PDFs, and someone's head is a pipeline project wearing an AI project's clothes — and poor data readiness is the top obstacle data leaders name for moving pilots to production (Informatica, 2025).
- Regulated or not? Healthcare, finance, and anything touching PHI or payments add compliance surface that is real engineering, not paperwork.
The line most budgets forget
What it costs to keep running. An AI MVP is not a thing you finish. Models get deprecated and retuned on the vendor's schedule, your data distribution shifts, and edge cases accumulate — so a system with nobody operating it degrades quietly until someone notices a month of bad output. That's not a maintenance line item you can defer; it's the difference between a product and a demo with users attached.
Budget it the way you budget hosting. Ours runs $3,000–$20,000 a month for managed operations depending on how many systems are under management. Whatever number you use, put it in the model before you commit to the build, not after — we go into the failure modes in why AI systems break after launch.
Where AI MVPs overspend
Not on tokens. On surface area — building four features adequately instead of one properly, because it was hard to say no in the planning meeting. And on rebuilds: the demo-grade version that impressed everyone, collapsed under real usage, and got rebuilt from scratch. That's the dominant cost in the market right now — 95% of enterprise generative-AI pilots deliver no measurable P&L impact (MIT Project NANDA, 2025) — and it's paid in full, twice.
The cheapest insurance is scoping the thing properly before you fund it. A fixed-price two-week Sprint is $25,000 and ends with a deployed pilot and a real number, which is a considerably better way to discover a $400,000 build was actually a $120,000 one. Our prices are published if you want to benchmark whatever you're being quoted, and why we run this as a software factory explains the model underneath them.
