Your first AI API error needs a decision tree, not another retry

#ai #api #debugging #devops

The first failed AI API request is usually treated as a retry problem.

That is often the wrong instinct.

A 401, 403, 404, 429, or model_not_found response is useful evidence. If you retry before classifying it, you can hide the real setup issue and make the next failure harder to explain.

Start with the boring checks

Before switching models or adding retry logic, ask:

Is the base URL correct for OpenAI-compatible calls?
Is the key present, server-side, and attached to the intended project?
Is the requested model visible for this account and key?
Does the key have a model limit, quota limit, or expiry date?
Is the account balance enough for this route?
Did the platform return a provider error, gateway error, or rate-limit error?
Did a fallback route change the model, cost, or final status?
Does the request log show model, status, tokens, route, and charge?

Classify before retrying

A useful first-call path should make the failure specific enough to act on:

401: inspect key format, auth header, and whether the key is server-side.
403: check account state, group access, balance, route permission, or policy limits.
404 / model_not_found: verify the exact model ID against current model pages or pricing.
429: separate concurrent users, concurrent requests, provider limits, and retry behavior.
HTTP 200: still confirm the log row, served model, token count, and charge.