Kubernetes Autoscaling Deep Dive: HPA, VPA, KEDA, and Cluster Autoscaler

Kubernetes Autoscaling Deep Dive: HPA, VPA, KEDA, and Cluster Autoscaler

Kubernetes autoscaling is not a single feature - it is a layered system where four components work together to match your infrastructure to actual demand. Getting autoscaling wrong means either wasting money on idle resources or dropping traffic during load spikes. Understanding how HPA, VPA, KEDA, and the Cluster Autoscaler interact is essential for any production Kubernetes deployment.

The Horizontal Pod Autoscaler (HPA) scales the number of pod replicas based on CPU, memory, or custom metrics. The Vertical Pod Autoscaler (VPA) adjusts resource requests and limits for individual containers based on observed usage - critical for right-sizing workloads you have not profiled yet. KEDA (Kubernetes Event-Driven Autoscaling) extends HPA with scalers for external event sources like SQS queue depth, Kafka consumer lag, or Prometheus queries, enabling scale-to-zero for workloads that do not need to run continuously. The Cluster Autoscaler adds or removes nodes when pods cannot be scheduled due to insufficient cluster capacity.

The key to effective autoscaling is layering these components correctly. Run VPA in recommendation mode first to establish baseline resource requests. Configure HPA with appropriate metrics and thresholds - CPU utilization of 60-70% is a good starting point. Use KEDA for event-driven workloads like queue processors. And ensure Cluster Autoscaler is configured with appropriate node groups and scale-down policies to avoid unnecessary churn.

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