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Every AI provider fails in its own way. I stopped checking status codes and built an error model instead.
TL;DR: The author built a gateway that normalizes provider errors into a single internal model to simplify retry decisions. They discovered that numeric status codes like 429 vary in meaning across providers, so a unified error class system helps consistency.
An API gateway fronts multiple providers (OpenAI, Anthropic, Gemini). Instead of passing through provider-specific status codes, it maps errors to a small set of internal classes (ratelimited, overloaded, quotaexceeded, invalidrequest, authenticationerror, servererror). This normalization clarifies error handling decisions and avoids provider-specific error shapes. The approach logs raw provider errors for debugging but surfaces only the internal category to callers. The author asks peers if they implemented a provider-agnostic error model and what mapping challenges they faced.
Question for the room: Have you implemented a provider-agnostic error model in your gateway or service, and what mapping challenges did you face?
— via dev.to
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