Reliable AI API clients: timeouts, retries and fallbacks
Build resilient AI API integrations without retry storms, duplicate work or hidden failures. Learn which errors need a retry and which need a configuration fix.
Updated October 2, 2026 · G0I documentation team
Separate configuration errors from transient failures
An invalid key, unavailable account access or malformed request needs a different response from a temporary upstream error. Inspect the status and error message. Repeatedly sending an unchanged invalid request consumes resources and rarely improves the result.
Keep retries bounded
Use a small retry budget and backoff for suitable transient failures. Respect relevant retry guidance when supplied. A timeout does not prove that no work happened: take special care when a request triggers tool actions or asynchronous generation.
Treat streaming as a state machine
A connection can open successfully and still fail before the response is complete. Track completion signals, handle user cancellation and distinguish a partial response from a completed one. Choose client timeouts appropriate to your workload instead of using a single short timeout for every task.
Measure your own service level
Log request outcomes, model IDs and timings without recording API keys or unnecessary private content. Test any fallback model for compatibility and quality before enabling it. A gateway can simplify access, but your application still owns its user-visible recovery behavior.
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