HPA vs Cluster Autoscaler: Key Differences and Usage Guide

The Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a deployment based on CPU or custom metrics, while the Cluster Autoscaler adjusts the number of nodes in a cluster based on pending pods that cannot be scheduled. HPA manages pod-level scaling, and Cluster Autoscaler manages node-level scaling to support pod demands.

Quick Comparison

This table summarizes the main differences between HPA and Cluster Autoscaler.

Factor	Horizontal Pod Autoscaler (HPA)	Cluster Autoscaler
Scaling Level	Pods within deployments or replicasets	Nodes in the Kubernetes cluster
Trigger	CPU usage or custom metrics on pods	Unschedulable pods due to lack of node resources
Scope	Scales workload pods	Scales infrastructure nodes
Scaling Action	Increases or decreases pod count	Adds or removes nodes
Typical Use Case	Adjust app capacity dynamically	Ensure enough nodes for pod scheduling
Configuration	Defined in HPA resource with metrics	Runs as a controller monitoring cluster state

Key Differences

The Horizontal Pod Autoscaler (HPA) focuses on adjusting the number of pods in a deployment or replicaset based on observed metrics like CPU or memory usage. It reacts to workload demand changes by increasing or decreasing pod replicas to maintain performance and resource efficiency.

In contrast, the Cluster Autoscaler manages the number of nodes in the Kubernetes cluster. It watches for pods that cannot be scheduled due to insufficient resources and adds nodes to accommodate them. When nodes are underutilized and pods can be moved, it removes nodes to save costs.

While HPA scales the application layer by changing pod counts, Cluster Autoscaler scales the infrastructure layer by adjusting node counts. Both work together to ensure the cluster can handle workload demands efficiently.

Code Comparison

Example of an HPA configuration that scales pods based on CPU usage:

yaml

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: example-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: example-deployment
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

Output

Creates an HPA that keeps CPU usage around 50% by scaling pods between 2 and 10 replicas.

Cluster Autoscaler Equivalent

Cluster Autoscaler runs as a controller and does not require a YAML like HPA. Here is a typical command to deploy it on a cluster:

bash

kubectl apply -f https://github.com/kubernetes/autoscaler/releases/download/cluster-autoscaler-1.26.0/cluster-autoscaler-autodiscover.yaml

# Then edit the deployment to set your cloud provider and node group name
kubectl -n kube-system edit deployment cluster-autoscaler

Output

Deploys Cluster Autoscaler which monitors unschedulable pods and adjusts node count automatically.

When to Use Which

Choose HPA when you want to automatically adjust the number of pods in your application based on workload metrics like CPU or custom metrics.

Choose Cluster Autoscaler when your cluster needs to add or remove nodes to ensure pods can be scheduled and to optimize infrastructure costs.

In most cases, use both together: HPA scales pods for workload demand, and Cluster Autoscaler scales nodes to support those pods.

Key Takeaways

HPA scales pods based on workload metrics like CPU usage.

Cluster Autoscaler scales cluster nodes based on pod scheduling needs.

HPA manages application-level scaling; Cluster Autoscaler manages infrastructure-level scaling.

Use both together for efficient and responsive Kubernetes scaling.

Cluster Autoscaler requires cloud provider integration to add or remove nodes.