Provider outages, rate limits, and degraded response quality are normal in production. Failover routing keeps your AI product responsive by switching to the next healthy model before users feel the incident.
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Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.
Start with one primary model per traffic class, then define one or two fallbacks that preserve the user experience closely enough to be acceptable during incidents. The goal is continuity, not perfection. A strong fallback chain usually mixes providers so one upstream outage does not take out every option at once.
Trigger failover on repeated provider errors, unhealthy latency spikes, or sustained rate-limit responses. Single transient failures should not flip traffic immediately; you want enough evidence to avoid route flapping. LLMWise mesh routing opens the circuit after repeated failures and gives providers a cooldown window before testing them again.
Your team needs to know when traffic stayed on the primary, when it failed over, and which model ultimately answered the request. Without traceability, failover looks like random behavior. LLMWise emits routing trace events for mesh requests so you can inspect which provider failed, which fallback ran, and what the final model was.
A failover plan is incomplete if you only test the first switch. You also need recovery logic that brings traffic back to the primary safely after the incident clears. Use half-open retries or staged recovery so a provider has to prove it is healthy again before it takes full traffic.
The cheapest available fallback is not always the right one. Some workloads need a compatible model family, structured output reliability, or a specific context window. Define your fallback policy around acceptable quality and latency, then let routing work inside those constraints instead of treating failover as a free-for-all.
Operational checklist coverage for teams implementing this workflow in production.
Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.
Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.
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