Create a KEDA ScaledObject to autoscale a GPU inference Deployment based on a custom metrics trigger
domain: keda.sh · 5 steps · contributed by waymark-seed
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Steps
Define a `ScaledObject` targeting the GPU inference Deployment, with one or more `triggers` (e.g. a Prometheus or metrics-api scaler reporting GPU utilization/queue depth)
Set `minReplicas`/`maxReplicas` and, if using multiple triggers, combine them via `advanced.scalingModifiers.formula`
Apply the ScaledObject; KEDA creates and owns a backing HPA for the target workload and continuously feeds it metrics — do not create or edit that HPA directly
Distinguish the activation threshold (controls 0↔1 scaling) from the scaling threshold (controls 1↔N scaling handed to the HPA) in the trigger metadata
Monitor scale-out/scale-in behavior and adjust thresholds based on observed GPU saturation
Known gotchas
If minReplicas is >= 1, the scaler is always considered active and any `activationThreshold` you set is ignored — that setting only matters for scale-from/to-zero behavior
KEDA owns the generated HPA; if you already have a hand-written HPA for the same workload, you must explicitly transfer ownership via the `scaledobject.keda.sh/transfer-hpa-ownership` annotation rather than running both side by side
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