Kubernetes Scaling Strategies

Which one should you use?

Jun 24, 2025

Kubernetes has become the go-to platform for running containerized applications in production. The real power of Kubernetes lies in scalability.

As traffic to your application grows, Kubernetes offers several built-in and customizable scaling strategies to keep your system responsive, efficient, and cost-effective.

In this article, we’ll explore the most essential Kubernetes scaling strategies you should know.

1 - Horizontal Pod Autoscaling (HPA)

This is the most commonly used scaling method in Kubernetes.

The Horizontal Pod Autoscaler (HPA) automatically increases or decreases the number of pod replicas in a Deployment, StatefulSet, or ReplicaSet based on observed resource usage. Most often, it watches CPU utilization, memory usage, or even custom application-level metrics like request rates.

You can play around with the diagram on Eraser.io

Example:

If you set a target CPU utilization of 70% and your application crosses that threshold consistently, HPA might spin up more pod replicas to balance the load.

Key Benefits:

Keeps applications responsive during high traffic.
Scales down during idle periods to save resources and cost.
Works well for stateless workloads like web servers, APIs, etc.

Things to Watch:

Requires metrics server to be installed in the cluster.
Doesn’t handle increasing pod resources—only the number of pods.

2 - Vertical Pod Autoscaling (VPA)

Where HPA adds or removes pods, Vertical Pod Autoscaling (VPA) focuses on resizing the pods themselves.

VPA automatically adjusts the CPU and memory requests/limits for each pod based on its actual usage. This is especially useful for workloads where scaling out is either inefficient or impractical—such as batch jobs, data processing, or single-instance services.

Example:

If your application consistently uses more memory than allocated, VPA can increase the memory limits so the pod doesn’t get OOM-killed (Out of Memory error).

Key Benefits:

Prevents over-provisioning or under-provisioning of resources.
Helps optimize performance for memory-heavy or CPU-intensive tasks.

Things to Watch:

Restart of pods is often required for VPA to apply changes.
Doesn’t work well with HPA simultaneously unless specifically configured.
May lead to high memory usage if not monitored properly.

3 - Cluster Autoscaling

While HPA and VPA manage scaling at the pod level, Cluster Autoscaler takes care of scaling at the infrastructure level—specifically, the number of worker nodes in your Kubernetes cluster.

When pods can't be scheduled due to resource shortages (e.g., not enough CPU or memory across nodes), the Cluster Autoscaler works with your cloud provider (like AWS, GCP, Azure) to add more nodes. Similarly, if some nodes are underutilized and their pods can be moved elsewhere, it can safely scale the cluster down.