What is the main difference between HPA and VPA in Kubernetes?

The main difference is that the Horizontal Pod Autoscaler (HPA) scales the number of pods based on metrics like CPU usage or custom metrics, while the Vertical Pod Autoscaler (VPA) adjusts the resource requests of individual pods.

How does the Cluster Autoscaler work in Kubernetes?

The Cluster Autoscaler detects when pods cannot be placed in the cluster due to insufficient resources and automatically adds new nodes. It can also remove underutilized nodes to optimize efficiency.

When should I use the Vertical Pod Autoscaler (VPA)?

The VPA is particularly useful for workloads with unpredictable resource needs, as it automatically adjusts the resource requirements of pods. It is well-suited for situations where CPU and memory usage fluctuate significantly.

Can I use HPA and VPA simultaneously for the same pod?

It is not recommended to use HPA and VPA simultaneously for the same pod, as they can scale based on the same metrics. This could lead to conflicts that destabilize scaling decisions.

How can I reduce costs in cluster management with Kubernetes?

One way to save costs is to use the Cluster Autoscaler in conjunction with AWS Spot Instances, which can reduce costs by up to 90%. Additionally, you can optimize the scaling of your nodes by utilizing only the resources you need.

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Kubernetes & Container 8 min. read

Kubernetes Autoscaling: HPA, VPA, and Cluster Autoscaler Compared

Autoscaling is the backbone of any scalable Kubernetes architecture. We compare the three autoscaling mechanisms and show when each approach is the best fit.

devRocks Engineering · 20. March 2026 · Aktualisiert: 31. March 2026 ·

Kubernetes Autoscaling HPA VPA AWS EKS

Kubernetes Autoscaling: HPA, VPA, and Cluster Autoscaler Compared

Why Autoscaling Is Indispensable

In modern cloud environments, the load on your applications varies constantly. Manual scaling is not only inefficient — it is a risk. Kubernetes offers three complementary autoscaling mechanisms that together form a powerful system.

Horizontal Pod Autoscaler (HPA)

The HPA scales the number of pods based on metrics such as CPU utilization, memory consumption, or custom metrics. It is the most commonly used autoscaler and ideal for stateless workloads.

CPU-based: By default, the HPA scales based on the average CPU utilization across all pods.
Custom Metrics: Through the Metrics Server, you can use custom metrics such as requests per second or queue length.
Stabilization: The HPA uses a stabilization window to avoid flapping — no constant scaling up and down.

Vertical Pod Autoscaler (VPA)

The VPA adjusts the resource requests (CPU and memory requests/limits) of individual pods. It is particularly useful for workloads whose resource requirements are difficult to predict.

Recommender: Analyzes historical resource usage and provides recommendations.
Updater: Can automatically restart pods to apply the recommended values.
Caution: HPA and VPA should not scale on the same metric simultaneously.

Cluster Autoscaler

The Cluster Autoscaler scales the number of nodes in the cluster. It detects when pods cannot be scheduled (because not enough resources are available) and automatically adds new nodes.

Scale-Up: Detects pending pods and provisions new nodes from the node group.
Scale-Down: Removes underutilized nodes after a configurable waiting period.
Spot Instances: Can be combined with AWS Spot Instances to save up to 90% in costs.

Our Recommendation

In practice, at devRocks we deploy all three autoscalers in combination: HPA at the pod level, VPA in recommender mode for resource tuning, and the Cluster Autoscaler for the infrastructure level. This combination provides maximum flexibility at minimal cost.