# What an AI feature really costs to run

> A model call costs fractions of a cent — until you multiply it by real traffic. The costs that surprise teams are predictable if you look for them before launch, not after the first invoice.

*Source: https://www.lazlosoftwaresolution.com/resources/what-an-ai-feature-really-costs-to-run*

A single model call costs a fraction of a cent, which makes AI features feel almost free in a demo. Then the feature ships, traffic arrives, and the fraction gets multiplied by a number nobody estimated. The costs that surprise teams are not mysterious — they are just easy to skip when the prototype was cheap.

Here is where the money actually goes, and how to keep it sensible.

## Cost scales with usage, and usage grows quietly

Per-request pricing looks trivial per request. Multiplied by every user, every day, retried on failure, it becomes a real line item. The time to estimate this is before launch: how many calls, how large, how often, at the traffic you actually expect — not the traffic you have in testing.

## The size of the request matters as much as the number

Cost is driven largely by how much text goes in and comes out. Stuffing a huge amount of context into every call, or letting responses run long, multiplies the bill without necessarily improving the result. Sending only the context a request needs is both cheaper and usually better.

 - **Cache** what repeats — identical or near-identical requests do not need a fresh call each time.

 - **Right-size the model** — many tasks run fine on a smaller, cheaper model, with the largest one reserved for the cases that need it.

 - **Trim the context** — retrieve what is relevant, not everything you have.

## The hidden costs are not the model

The obvious cost is the model call. The quieter ones are the infrastructure around it — storing and indexing data for retrieval, the extra calls a single user action can trigger, and the engineering time to keep it all working. These rarely show up in an early estimate and often outweigh the raw model cost.

> Cost is a design input for AI features, not a bill you read afterwards. The choices that control it are made before launch.

## Decide the trade-offs on purpose

Cheaper, faster, and higher quality pull against each other, and the right balance depends on the feature. A background task can use a cheaper, slower path; a feature a user is waiting on may justify a more expensive one. Making these trade-offs deliberately, per feature, is what keeps an AI product both good and affordable — rather than discovering the balance from an invoice.