The real cost of an AI MVP — and how to cut it
The sticker price of an AI MVP is rarely the model. It's the data plumbing, the evaluation harness, and the six weeks nobody budgeted for making the thing trustworthy. Here's where the money actually goes.
We've shipped a lot of first versions. The teams that overspend almost always make the same three mistakes: they buy capability they don't need yet, they skip evaluation, and they treat the demo as the finish line.
Where the budget really goes
- Data preparation and access — typically the single largest line item, and the one most often underestimated.
- Evaluation and guardrails — the work that turns a demo into something you can put in front of a customer.
- Integration — auth, permissions, and wiring the model into the systems people already use.
- The model itself — often the smallest and most predictable cost of the four.
Start with the thinnest slice that proves value
An MVP should answer one question: does this create enough value for someone to change their behaviour? Pick the single highest-leverage workflow, ship it end to end, and measure. A narrow feature that's genuinely used beats a broad platform that impresses in a demo and dies in a drawer.
The cheapest AI MVP is the one that answers the value question in six weeks — not the one that ships every feature in six months.
Where to save without cutting corners
Use a hosted model before you fine-tune. Use retrieval before you train. Buy evaluation tooling instead of building it. And instrument everything from day one — the usage data you collect in the MVP is what makes version two cheap.
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