Best Practices
Use these guides when you already know which scaler or Kedify feature you want to use, and you want the workload and cluster behavior around it to be stable in production.
Guides
Section titled “Guides”- KEDA Best Practices covers ScaledObject fallback, HPA behavior, and scaling modifiers.
- Kubernetes & Autoscaling Best Practices covers pod shape, resource requests, probes, graceful shutdown, disruption handling, and horizontal and vertical scaling choices.
- Autoscaling Checks covers installation, dashboard visibility, and alerting for end-to-end autoscaling control-plane checks.
- Kedify High Availability Configuration covers replica and availability settings for Kedify and KEDA components.
- Resource Footprint covers rough CPU and memory sizing for the whole Kedify stack so you can plan cluster capacity before installing.
Start with the Kubernetes autoscaling guide for workload-level behavior, then tune the KEDA configuration that drives the scaling signal.
Validate Autoscaling Health
Section titled “Validate Autoscaling Health”Run autoscaling checks in production-like clusters to verify that the autoscaling control plane works end to end. The checks exercise HTTP, Prometheus, OTel, CPU, and memory scaling paths, then expose kedify_autoscaling_check_duration_seconds as the primary alerting metric for the latest check iteration latency.
Alert when the latest iteration takes longer than your operating threshold, and use the step status labels and step duration metrics to identify whether the delay is in signal generation, metric availability, pod-autoscaler activity, scale-up, or scale-down.