Rate Limit Testing for AI APIs
Rate limit testing for AI APIs verifies how a system behaves when it hits a provider's requests-per-minute or tokens-per-minute ceiling — whether it queues gracefully, retries with backoff, degrades to a smaller model, or fails the user outright. It matters more for AI features than typical APIs because LLM limits are often lower and errors less predictable.
Most third-party APIs return a clean 429 and a Retry-After header; some AI providers do too, but limits can also be enforced per-model, per-organization, or as a rolling token budget that's harder to predict from a single request. A feature that works fine in a demo with one user can start failing in production the moment concurrent traffic pushes several users' calls into the same rate window.
Testing it means deliberately simulating burst traffic against a staging key (never production) and confirming the retry/backoff logic actually engages instead of surfacing a raw provider error to the end user, and that a fallback path exists — a smaller/cheaper model, a cached response, or a clear "try again in a moment" message — rather than a blank screen.
A common gap is testing only the happy-path single request and never simulating what happens when ten users hit the same AI feature in the same minute, which is exactly the scenario that reveals whether the retry logic was ever actually built.
