Canary rollouts: upgrade models in production without downtime
TL;DR
A hard model swap exposes every user at once, and rolling back means cold-starting the old deployment under pressure. Here's how staged traffic ramps, metric gates, and automatic rollback work on dedicated inference. A hard model swap exposes every user at once, and rolling back means cold-starting the old deployment under pressure. Here's how staged traffic ramps, metric gates, and automatic rollback work on dedicated inference.
Nauti's Take
Staged rollouts are a clear advantage for LLM upgrades, because new model versions often answer subtly differently and problems surface with a small slice of users first. The limit is the metrics: latency and error rates are easy to gate on, while gradual drops in output quality are hard to catch.
Teams running production workloads on dedicated inference benefit directly, provided they bring their own evals as gates.
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