Kubernetes Autoscaling: Scale With Demand, Not Guesswork

The problem with fixed capacity

Applications rarely receive exactly the same traffic every hour.

Provisioning infrastructure permanently for peak traffic can leave large amounts of capacity unused during quieter periods.

Provisioning too little capacity creates the opposite problem: performance degradation during traffic spikes.

Horizontal Pod Autoscaler

Kubernetes Horizontal Pod Autoscaler, or HPA, automatically adjusts the number of Pods for supported workloads.

HPA can scale workloads based on metrics such as:

• CPU utilization;
• memory utilization;
• custom metrics.

When demand rises, Kubernetes can increase the replica count.

When demand decreases, it can reduce the number of replicas toward the configured minimum.

Cost and performance must be considered together

Autoscaling should not simply minimize the number of Pods.

Teams also need to define:

• resource requests;
• resource limits;
• minimum replicas;
• maximum replicas;
• readiness and startup probes;
• scale-up and scale-down behavior.

Poor resource requests can produce inefficient scaling decisions.