Production AI reliability is broader than provider uptime. A request can return 200 and still be operationally poor because it was slow, unexpectedly expensive, routed to the wrong model or completed only after several hidden retries. Reliability means making the request path observable, bounded and recoverable.
Key takeaways
- Define timeouts, retry rules and fallback before an incident forces you to invent them.
- Monitor provider and model health at the workload level, not only global averages.
- Connect incidents to requests, releases and cost so recovery decisions are evidence-based.
1–3: identity, attribution and request evidence
1. Every application uses a scoped gateway credential. 2. Every request is attributable to a workload and environment. 3. Provider, model, status, latency and token usage are recorded consistently.
These controls create the evidence needed for every later reliability decision. If you cannot identify which workload sent a request or which route served it, incident response starts with guesswork.
4–6: timeouts, retries and fallback
4. Each model call has an explicit timeout. 5. Retryable errors are defined instead of retrying everything. 6. Fallback routes are documented and tested.
Retries and fallback consume inference and can change output behavior. Keep attempt counts bounded and record each attempt so reliability improvements do not hide a cost regression.
7–9: health, latency and error signals
7. Monitor success and failure rate by provider and model. 8. Track P50 and P95 latency, plus time to first token for streaming. 9. Detect provider-specific degradation before it disappears inside global averages.
The goal is to know whether an incident affects the entire platform, one provider, one model or only one workload.
10–12: cost, releases and policy
10. Cost per request or run is monitored. 11. Requests carry release or version context where possible. 12. Budget alerts and governance policy are connected to the workload.
A reliability incident can start as a release regression, a routing change or a cost anomaly. Shared context makes those relationships visible.
13–15: incident operations
13. There is a defined owner for AI incidents. 14. Operators can inspect representative requests or traces. 15. Recovery actions—reroute, disable fallback, roll back a release or change a timeout—are documented and reviewable.
After recovery, preserve the evidence. The post-incident question is not only what failed, but which signal should have made the issue obvious sooner.
FAQ
Common questions
What makes an LLM production-ready?
Production readiness requires more than a working model call: scoped credentials, request attribution, timeouts, bounded retries, tested fallback, latency/error monitoring, cost visibility and a clear incident response path.
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