LLM Fallback and Retry Patterns
LLM fallback and retry patterns are resilience strategies that keep AI-powered applications running when a model provider fails, slows, or returns an error.
They include exponential backoff (progressively spacing retries), provider failover (switching to a backup model), circuit breakers (stopping retries when a service is down), and graceful degradation (returning a simpler response rather than failing completely). Also known as: LLM Resilience Patterns
What this topic covers
- Foundations — LLM fallback and retry patterns treat provider failures as expected infrastructure events, not exceptions — revealing that resilience in AI systems requires the same distributed systems discipline as any other external dependency.
- Implementation — These guides walk through implementing exponential backoff, provider failover chains, and circuit breakers — with honest trade-offs between simplicity, latency cost, and the complexity of managing multi-provider state.
- What's changing — The gateway-layer arms race is rewriting how retry logic gets implemented — with managed services abstracting fallback complexity that teams once built entirely by hand.
- Risks & limits — Silent model switching and opaque fallbacks create accountability gaps — when a system quietly routes to a different provider, it may change output quality, data residency, or compliance posture without anyone noticing.
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