Ask five agencies what an AI MVP costs and you'll get five numbers spread across an order of magnitude. That's not because anyone is lying. It's because "MVP" hides the real variables. Here's how to read a quote like someone who has shipped 50+ projects.
The three variables that actually move the price
First: how much of the product is the AI. A product with one LLM feature bolted onto a standard app costs very differently from a product where the AI is the product: agents, retrieval, evals, and all the reliability work that implies.
Second: integration surface. Every external system your MVP must read from or write to adds scoping, auth, and failure modes. Two integrations is a line item; ten is an architecture.
Third: who has to trust it on day one. An internal demo tolerates rough edges that paying users and regulated industries do not. Production-grade from day one costs more up front and dramatically less by month six.
Where cheap quotes hide the cost
Hourly billing moves the risk to you: the quote is low because the estimate is optimistic, and the overrun is your problem. Prototype-grade builds hide a rewrite in year one. And quotes without a measurement plan hide the biggest cost of all: shipping something nobody can prove is working.
The alternative is fixed-fee, milestone-based pricing scoped to an ROI target. You know the number before kickoff, and the vendor owns the estimate risk. That's how we price every project, and it's the first question we'd ask any competitor's quote: who pays for the overrun?
Questions to ask before signing
Who owns the code and prompts? Is the architecture meant to survive past the MVP? What ships every week so drift is caught early? What exactly is measured at launch? A good partner has crisp answers; a body shop has a rate card.
