Kubernetes Inference Autoscaling Failures — When Replicas Do Not Scale

Autoscaling failures usually come from scaling on CPU instead of inference signals, missing custom/external metrics, KEDA misconfiguration, cold-start-blocked scale-to-zero, or slow node provisioning. We diagnose it live by reading Pod events (kubectl describe), scheduler and device-plugin logs, container logs, GPU metrics (DCGM), and Prometheus/OpenTelemetry traces — then confirm the root cause before changing anything, rather than guessing.

We stabilise first — protect the running workload, control blast radius, and restore service — then apply the durable fix (scheduling, resource requests/limits, node/GPU configuration, probes, autoscaling policy, or manifest changes) and add the observability or guardrail that would have caught it earlier. You keep an engineer on the call throughout.

Yes. This is exactly the kind of incident we handle on-call. Message us on WhatsApp describing the symptom, your cluster and cloud (EKS/AKS/GKE/OpenShift/bare-metal), and what changed recently, and we will join and work it with you until the system is stable.

Both. This page explains the failure mode and how it is resolved so you can act on it yourself. If you would rather have an expert work it with you live — during an incident or as ongoing job support — that service is available same-day and confidentially.

Yes. After the fix we help you add the right alerts, SLOs, resource policies, admission controls, and runbooks so the same failure does not page you again — and we can review the rest of your platform for the same class of risk.