How to Price an AI Automation Project

An AI project has three costs and only one of them behaves like the software work you have quoted before. There is your build time, which ends when the build ends. There is the API cost, which starts when the thing goes live and never stops. And there is the human review time in the first months, while somebody checks the output often enough to trust it. Most quotes price the first, forget the third, and guess at the second.

Size the running cost before you build anything

You do not need a working system to know what it will cost to run. Take one typical request and estimate two numbers: roughly how much text goes in, counting your instructions and any documents you attach, and roughly how much comes back. Price that pair on a mid-tier model, then multiply by the requests you expect in a month. That is the whole calculation. Output is where the money goes, because providers charge several times more for it than for input, so a job that writes long replies costs far more than one that reads long documents and answers briefly.

Quote the ceiling, not the estimate

Your estimate will be wrong in the client's favour or yours, and you only care about one of those. Price the same workload on a flagship model as well as a mid-tier one. The gap is usually large: the same support bot can run $5.04 a month on the cheapest model we price and $1,080 on the most expensive. Quote against the high figure and you have room to be wrong. Quote against the low one and every surprise comes out of your margin.

Put the recurring cost on its own line

Separate the build fee from the running cost in the document the client signs, because they behave differently and the client needs to understand that before they sign rather than after their first busy month. A fixed build fee plus a monthly API cost passed through at a capped amount is easy to explain and hard to lose money on. Fold the two together into one fixed number and you have quietly sold an unlimited resource for a fixed price.