Understand the real cost of an AI chat API.

What drives cost

Model APIs commonly meter input and output tokens, with additional rules for cached tokens, tools or other features. Long conversation histories and retrieved documents increase input size, and verbose answers increase output size.

Cost controls that usually help

Send less irrelevant context. Keep only the turns and documents needed for the current task.

Use prompt caching when supported. Stable instructions and shared prefixes may qualify for cache pricing, depending on the provider's API and rules.

Set useful output limits. Short answers for simple questions save time and money, but limits must allow the task to complete.

Avoid unbounded retries. Apply backoff, limit attempts and report persistent failures.

Route selectively. Test whether a smaller or faster model meets your quality threshold for routine requests.

Estimate a request budget

A cost estimate can be modeled as:

estimated cost = input tokens × input unit price
               + output tokens × output unit price
               + tool and other applicable charges

Actual billing, caching and units are provider-specific. Read the provider's current pricing and account limits rather than copying prices into a long-lived static article.

Track the outcome

Measure cost per successful conversation, including retried requests, retrieval operations and required human review. Add hard spending ceilings and alerts where supported.

Next: model comparison or streaming design.