Kedify

Explore KEDA 2.21's new scalers, reduced duplicate polling, dynamic external-scaler targets, and the service-account-token security changes to plan for.

Deploy a JupyterLab workspace for a MetricPredictor, compare forecasting techniques on its metric history, and tune Prophet before retraining.

Karpenter v1.14 introduces capacity buffers for pre-provisioned warm nodes, but their size is a static number no autoscaler can drive. Kedify ScaleAdapter connects any KEDA trigger to a CapacityBuffer, so warm capacity follows schedules, metrics, and fore

A technical KPA vs. HPA benchmark at 5,000 Kubernetes applications: cold fan-out, warm decision latency, client request limits, and why sharding changes the result.

HPA is a strong default, but its shared controller can become a bottleneck at large scale. See how KPA shards autoscaling decisions while preserving the ScaledObject API.

Why observability metrics and autoscaling metrics are not the same thing, and how to build a faster Kubernetes scaling loop with real demand signals.

Use the Kedify Argo Rollouts plugin to drive canary traffic splits and HTTP autoscaling from a single ScaledObject. No second autoscaler, no hand-rolled Ingress weights.

Run multiple isolated KEDA operators in a single cluster, each scaling workloads for a different tenant.

Kedify extends KEDA with predictive autoscaling, fast vertical scaling, and multi-cluster coordination for modern AI-driven and distributed Kubernetes workloads.

Run workloads across several clusters, but scale them as if they lived in one.