Automatic LLM failover moves an eligible request from a failed or degraded route to a healthy standby. A safe implementation classifies failure types, respects retry budgets and idempotency, preserves the response contract, and makes every attempt visible in one trace.
The INFRO view: INFRO treats failover as a policy-controlled infrastructure behavior and keeps the complete attempt chain attached to one request.
Failures that should trigger failover
Timeouts, connection errors, rate limits, and many provider-side 5xx responses are good candidates. Authentication failures, invalid parameters, policy rejections, and malformed customer input usually are not: another provider will reject the same request and the retry only adds latency.
- Retry only errors that are plausibly route-specific.
- Cap attempts and total elapsed time at the request level.
- Use jittered backoff for short provider throttles.
- Open a circuit when repeated failures show a route is unhealthy.
Same-model and cross-model fallback
Same-model failover changes the serving provider while keeping model behavior stable. Cross-model fallback changes semantics as well as infrastructure. It can protect availability, but it should be opt-in and evaluation-backed because tool use, refusal behavior, output length, and structured-output reliability can differ.
For high-risk workflows, return a clear degraded result rather than silently substituting a model that has never passed the workload's tests.
Streaming changes the retry boundary
Before the first response byte, failover can be invisible to the client. After tokens have streamed, restarting on another route may duplicate or contradict output. Production systems therefore treat first-token delivery as a commit point unless the application protocol explicitly supports resumable generation.
Track time to first token separately from total duration. A route can look healthy on average while regularly stalling before it begins to stream.
The trace operators need
A useful trace shows the initial route, failure classification, time spent, fallback route, final outcome, and cost of every attempt. Without that chain, failover can hide provider problems and quietly double spend.
Alert on fallback rate, not just final failure rate. Rising recovery traffic is often the earliest signal of a provider incident.
Where INFRO fits
With INFRO, applications keep one client contract while the control plane can remove a degraded route and select an eligible standby. Request-level logs show what happened instead of presenting a mysteriously slow success.
Review INFRO's reliability model, request tracing, and security posture before setting production policies. Private early access onboarding is the right place to define which failures and fallback models are allowed for each workload.
Frequently asked questions
Should every LLM error be retried?
No. Retry route-specific transient failures such as timeouts, throttles, and many 5xx responses. Do not retry deterministic request or authentication errors on another route.
Can failover work during streaming?
It is safest before the first token. Once output is visible, switching models can produce duplicated or inconsistent text unless the application implements its own resume protocol.
Does INFRO expose fallback attempts?
INFRO's product design records routing and attempt information with the request so operators can inspect recovery rather than treating it as an invisible black box.
