LLM API Error Handling: Timeouts, Rate Limits, Retries, and Fallback

LLM APIs fail in ways that ordinary APIs also fail: timeouts, rate limits, authentication errors, invalid requests, and provider outages. They also fail in model-specific ways, such as malformed structured output or unsupported tool calls.

Production AI apps need explicit error handling.

Classify errors first

Separate transient errors from deterministic errors. Timeouts and temporary rate limits may be retried. Invalid API keys, malformed requests, and unsupported parameters should not be retried.

Retry safely

Use exponential backoff, jitter, and retry caps. Unbounded retries increase cost and can make provider problems worse.

Add fallback

If the primary model fails, route to a backup model when quality and compatibility are acceptable. Fallback should be logged so you can measure how often it happens.

User experience

For user-facing features, return clear messages. If a long generation fails, consider partial output, cached content, or a graceful retry button.

Final thoughts

Good error handling turns AI provider failures into recoverable events. Classify errors, retry carefully, use fallback, and log every failure path.