The honest answer to "what does an AI MVP cost" is a range, and the range depends as much on where you build as on what you build. Here is how to compare quotes across three regions companies often weigh against each other, and how to work out a real number for your own scope.
The three rate worlds
A senior full-stack or AI engineer bills very differently depending on geography. US tech hubs such as San Francisco, Seattle, Austin and New York sit at the top of the global rate scale. Western Europe (Paris, Milan, Munich) is generally lower than the US coasts but still premium. India typically offers the lowest rates of the three for comparable seniority, which is why a startup in California or a scale-up in Texas often builds its first AI version offshore and keeps a small onshore product lead.
Rates move with seniority, specialism and demand, so treat any published figure as a starting point. The only comparison that means anything is real quotes for the same written scope.
What each location offers
| Location | Strengths | Watch for |
|---|---|---|
| San Francisco and US hubs | Deep AI product experience, same time zones as US customers and investors, easy in-person workshops | Highest rates; strong competition for senior AI talent |
| Paris and Western Europe | Strong machine learning community, familiarity with GDPR and the EU AI Act, working hours that overlap with both the US and Asia | Rates still premium; a smaller agency pool for some specialisms |
| India | Large engineering talent pool, lower rates, good overlap with Europe and the Gulf | Needs a clear spec and review cadence; limited live overlap with US Pacific time |
What a real AI MVP includes (the part budgets miss)
An AI MVP is not just a chat box over an API. A production-ready version includes retrieval or data plumbing, an evaluation harness so you can prove it works, guardrails, observability and a deployment path. Teams that price only the happy-path UI discover that the evaluation and reliability work was the actual product. Budget for each of these explicitly:
- Data plumbing. Ingesting, cleaning and indexing the documents or records the model needs.
- Evaluation. A test set and scoring, so you can show quality and catch regressions.
- Guardrails. Input and output checks, refusal handling and rate limits.
- Observability. Logs and traces of every model call, with cost per request.
- Deployment. Environments, CI, secrets handling and a rollback path.
- Product surface. The web or mobile interface, authentication and admin tools.
How to estimate your own number
Instead of trusting a published band, build the estimate from scope:
- List the features and the six components above, and estimate each in engineer-weeks.
- Add a contingency for integration, rework and review, the work nobody itemises.
- Multiply the total by each vendor's blended weekly rate.
- Add model and hosting costs for the build period and the first months of use.
- Add your own team's time: the product owner, reviews and user testing.
A worked example
Say your scope comes to 24 engineer-weeks and you add 25% contingency, giving 30 engineer-weeks. Vendor A quotes a blended weekly rate of R; Vendor B quotes 0.6R. B's build costs 18R against A's 30R. That saving holds only if B delivers the same scope in the same time with the same amount of rework. If B needs 40 engineer-weeks because of rework, its cost rises to 24R and much of the gap closes, before you count the cost of a later launch. All figures here are hypothetical.
Where cheap gets expensive
The lowest hourly rate often produces the highest total cost: rework, no evaluation criteria, undocumented code and an MVP that cannot graduate to production. The right comparison is cost per shipped outcome, not cost per hour. Warning signs in a proposal include no mention of evaluation, no named senior engineers, and code held in the vendor's repository rather than yours. Our LLM cost checklist covers the runtime side once you are live.
Compliance changes the comparison
If you are regulated, pick a partner fluent in your compliance regime regardless of geography. For users in the EU, GDPR applies wherever the developer sits, with fines of up to EUR 20 million or 4% of worldwide annual turnover, whichever is higher. The EU AI Act entered into force on 1 August 2024, with its prohibitions applying from February 2025 and obligations for general-purpose AI models from August 2025; check which of its obligations apply to your use case. For US health data, HIPAA shapes hosting choices and vendor agreements. Compliance work does not shrink with a lower hourly rate, so budget it as its own line.
Tax can also move the numbers. R&D incentives such as France's crédit d'impôt recherche or the US research credit may change the effective cost of work done locally, so ask your accountant before you compare quotes on headline rates alone.
How to choose
If you are pre-revenue and speed matters, build offshore with a tight scope and an onshore product owner. If you need daily in-person collaboration with US customers, an onshore or hybrid team may justify the premium. In every case, lock the scope before comparing prices; at Velura Labs we start every engagement that way. An AI Strategy & Roadmap fixes the scope, then the build runs with our LLM applications and mobile / web teams. Read the 60-day scoping guide, then get a fixed-scope estimate.
FAQ
Is a no-code AI builder cheaper?
For internal tools and prototypes, often yes. It becomes limiting when you need custom evaluation, deeper integrations or control over where data is processed.
How much should we hold back for after launch?
Plan for at least one more iteration after the MVP. Your first real users will show you what to change, and a budget that ends at launch leaves no room to act on what they tell you.