AI model failover provides an alternate path when the selected model times out, returns a service error, reaches a capacity limit or becomes unavailable. The router chooses a fallback that still satisfies the task's minimum capability and policy requirements.
Failover improves availability but can change output style, quality, cost or tool behavior. Systems should record the model actually used, test fallback paths and avoid retrying actions in ways that create duplicate side effects.
ELI5
Model failover sends a request to another suitable AI model when the preferred one is unavailable or unhealthy. The fallback should still meet the minimum capability, safety and policy needs of the task.
For example, if a provider times out, a router can send a text-summary request to a tested backup model. The system should record which model answered and avoid repeating an action that could create duplicate emails, purchases or other side effects.
When does AI model failover occur?
It can occur after timeouts, provider errors, capacity limits, failed health checks or policy-defined quality failures.
Can a fallback model behave differently?
Yes. Models can differ in capability, style, tool support, latency and cost, so fallback behavior should be tested explicitly.
