HPA Audit

The HPA Audit page is the horizontal (autoscaling) analogue of the Resource Audit — a single table of every HPA in the cluster, with the signals operators tune on and the actions to tune them in bulk. Where Resource Audit cleans up sizing drift, HPA Audit applies a tuning policy.

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What it shows

Each row surfaces the facts you’d otherwise chase across kubectl:

• Saturation — an HPA that’s out of headroom (maxReplicas − currentReplicas ≤ 1). A fixed (min == max) HPA is never flagged saturated.
• Responsiveness posture + source — the resolved scale-up/down behavior and where it came from (original / override / policy / scheduled).
• CPU target + source, and config-drift facets: fixed-replicas, sensitivity override, scale-down disabled, “using default.”
• Over-provisioned min — an HPA pinned at an over-committed minReplicas with a recommended right-sized value and the replicas it would save.
• Auto status — whether Auto-Optimization is effective for this HPA, and its per-HPA override.

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Inert HPAs are demoted

An HPA that can’t scale on CPU — fixed-replicas, a disabled CPU target (≥ 100%, a value the pod can never reach), or a memory/custom-metric HPA — is marked inert (nothing to tune). The default “Tunable only” filter hides inert HPAs so the actionable ones aren’t drowned by a fleet of decorative single-replica ones. A fleet-summary strip (total / saturated / inert / using-default / overridden / scale-down-disabled) sits above the table, and facet filters narrow it.

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Bulk tuning

Select up to 500 HPAs and apply one action — every mutation reuses the exact per-HPA logic the single-workload controls use, so single and bulk never diverge:

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Where to find it

The HPA Audit page is in the Nodes navigation group. Recommendations (right-sized min, CPU target) render as clickable chips that open a pre-filled modal — you confirm, Autopilot never auto-applies them.