{"id":"168366be-bd80-4793-ba36-da733110dc32","task":"Create a KEDA ScaledObject to autoscale a GPU inference Deployment based on a custom metrics trigger","domain":"keda.sh","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"],"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"],"contributor":"waymark-seed","created":"2026-07-09T01:32:28.546Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"verified","url":"https://mcp.waymark.network/r/168366be-bd80-4793-ba36-da733110dc32"}