Reliability & Fallbacks for AI Systems
Reliable AI systems define a fallback model or provider, retry policy, and timeout before a provider outage ever happens.
Prerequisites
Overview
Every model provider has outages and rate-limit spikes. A reliable AI feature has a plan for that in advance — a fallback provider or model, a retry policy with backoff, and a timeout — rather than discovering the gap during an incident.
Where It Fits
Request
Primary Provider
Timeout / Error
Fallback Provider
Key Points
- Fallback provider
- A pre-configured alternative model or provider a gateway routes to automatically when the primary fails.
- Retry with backoff
- Retrying a failed request immediately can worsen a rate-limit issue — exponential backoff spaces retries out.
- Graceful degradation
- When no provider is available, a system should fail in a visible, handled way — a clear error state, not a silent hang.
Interview Question
How would you design an AI feature to stay available during a model provider outage?
I’d configure a fallback provider or model through a gateway, so a failed request automatically retries elsewhere rather than failing outright, with exponential backoff to avoid worsening rate-limit issues. If no provider is available, the system should degrade visibly — a clear error state — rather than hanging silently.
Explain It in 30 Seconds
Reliable AI systems plan for provider failure in advance — a configured fallback provider, retries with backoff, and graceful degradation — rather than discovering the gap only when an outage actually happens.
Real-World Stack
Technologies commonly used to implement this in production.