Canary releases & staged rollout

How it works

A new model version is a behaviour change, and behaviour changes are best discovered on a few percent of traffic rather than all of it. A canary release exposes the new version to a small, monitored slice while the rest stay on the known-good one; error rates, latency, refusals, and quality signals are compared in real time, and the rollout only widens if the canary stays healthy — otherwise it rolls back automatically. It converts a risky cutover into a reversible, observable migration.

When to use it

Any production model or prompt upgrade; high-traffic systems where a silent regression would be expensive; alongside drift monitoring on the canary slice; and the standard way to de-risk adopting a hosted provider's model update on a timeline you don't fully control, since 'new version available' often isn't optional for long.

Limitations

Only catches problems that surface quickly and visibly in the canary window; slow-burn regressions, or rare-path failures that a small traffic slice just doesn't hit often enough to trigger an alert, can still pass through. It needs solid real-time monitoring on metrics that actually reflect quality — a canary that only watches latency and error rate will miss a quiet reasoning regression.

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