AI failover is the automatic process of switching from a failing LLM to a healthy backup model, ensuring your AI features stay online during provider outages.
AI failover is a reliability pattern that automatically redirects requests from a failing or degraded LLM provider to a healthy alternative. When the primary model returns errors, times out, or exceeds latency thresholds, the failover system detects the problem and routes subsequent requests to a pre-configured backup model. This happens transparently, with no user-visible interruption. Failover is essential for production AI systems because LLM providers experience outages, rate limiting, and performance degradation regularly.
A circuit breaker tracks consecutive failures for each model. When failures exceed a threshold, the circuit opens and the model is temporarily removed from rotation. After a cooldown period, the circuit enters a half-open state and sends a single test request. If the test succeeds, the circuit closes and normal traffic resumes. If it fails, the cooldown resets. LLMWise implements a three-strike circuit breaker with a 30-second open window, balancing fast failure detection with minimal false positives from transient errors.
A fallback chain defines the priority order of models to try when the primary fails. Effective chains cross provider boundaries: if GPT-5.2 is primary, the first fallback should be from a different provider like Claude Sonnet 4.5 or Gemini 3 Flash. This protects against full provider outages, not just individual model issues. Health checking complements failover by proactively monitoring provider status and detecting degradation before user traffic is affected, enabling preemptive rerouting.
LLMWise Mesh mode provides production-ready failover in a single API parameter. You specify a primary model and a fallback chain, and the platform handles circuit breaking, automatic rerouting, and recovery detection. Each failover event is logged with the failure reason, fallback model used, and added latency. The streaming protocol includes route events that tell your application exactly which model is handling the request and whether any failovers occurred, giving you full visibility without building custom monitoring.
LLMWise gives you five orchestration modes — Chat, Compare, Blend, Judge, and Mesh — with built-in optimization policy, failover routing, and replay lab. One API key, nine models, no separate subscriptions.
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