Running a model canary: the operator's walkthrough
Slice the traffic, compare against cached baselines, promote or retreat — model upgrades as a controlled experiment with the cache as your measuring instrument.
Model upgrades without process are vibes-based engineering: switch the string, watch support tickets, hope. The canary workflow makes them an experiment. Register the new model as a backend, route a configured slice of traffic to it, and let the cache do something unique — compare the canary's answers against your corpus of cached, served, confidence-scored baselines from the incumbent.
That comparison is the underrated asset: your cache is a ground-truth library of what good answers looked like, per intent class. Quality regressions surface as divergence patterns in the dashboard before they surface as user complaints — visible per intent, so you can see that the new model improved reasoning but regressed terse factual answers, and decide with specifics.
The upgrade is a workflow, not a leap of faith.
Promotion is then mechanical rather than dramatic: migrate cache entries with leasing (old answers serve until replacements verify — no hit-rate cliff), shift routing fully, retire the incumbent. Retreat is equally cheap: collapse the slice to zero, nothing lost, corpus untouched.
The bottom line
Frequent-upgrade teams converge on this rhythm because it removes the courage requirement from model adoption. The best model ships quarterly now; your process should metabolize that without a war room.