Introduction
One of Kubernetes’ most powerful capabilities is its ability to automatically scale applications and infrastructure in response to demand. Instead of manually adjusting capacity, you can configure Kubernetes to scale your applications up when load increases and scale down when demand subsides, optimizing both performance and cost. This article explains the two primary Kubernetes autoscaling mechanisms — the Horizontal Pod Autoscaler and the Cluster Autoscaler — and how to configure them effectively.
Horizontal Pod Autoscaler
The Horizontal Pod Autoscaler, or HPA, automatically adjusts the number of Pod replicas in a Deployment or StatefulSet based on observed metrics. The most common metric is CPU utilization — when average CPU across all Pods in a Deployment exceeds a target threshold, the HPA adds more Pods. When CPU drops below the threshold, it removes Pods. Modern HPA configurations can also scale based on custom metrics exposed through the Kubernetes metrics API, enabling scaling based on application-specific signals like request queue depth.
Vertical Pod Autoscaler
The Vertical Pod Autoscaler, or VPA, adjusts the CPU and memory resource requests of individual Pods based on observed usage. Rather than adding more Pods, VPA right-sizes existing Pods. VPA is particularly useful for applications that are difficult to scale horizontally, such as stateful databases. VPA can run in recommendation mode, showing you suggested resource adjustments without applying them automatically, which is useful for capacity planning.
Cluster Autoscaler: Scaling the Infrastructure
The HPA scales the number of Pods, but if no nodes have capacity to schedule additional Pods, scaling will stall. The Cluster Autoscaler solves this by automatically adjusting the number of nodes in your cluster. When Pods cannot be scheduled due to insufficient resources, the Cluster Autoscaler triggers the addition of new nodes. When nodes are underutilized and their workloads can be consolidated, it removes nodes to reduce cost. The Cluster Autoscaler integrates with major cloud providers’ managed node groups.
Configuring Autoscaling Effectively
Effective autoscaling configuration requires understanding your application’s resource requirements and traffic patterns. Set accurate resource requests and limits for all your Pods — the autoscalers rely on this data. Configure HPA minimum and maximum replica counts to define safe scaling bounds. Set appropriate scale-down delay periods to prevent thrashing during brief load spikes. Monitor your scaling behavior and tune the configuration based on observed patterns.
Keda: Event-Driven Autoscaling
Kubernetes Event-Driven Autoscaling, or KEDA, extends Kubernetes autoscaling capabilities to support scaling based on external event sources like message queues, databases, and HTTP request counts. KEDA enables scaling to zero when there are no events to process, making it ideal for batch processing workloads and APIs that receive intermittent traffic. KEDA integrates with dozens of event sources and is a CNCF graduated project.
Conclusion
Kubernetes autoscaling is a powerful tool for managing application performance and infrastructure cost. Configuring it effectively requires understanding your application’s behavior and Kubernetes resource model. Our Kubernetes autoscaling and performance engineering services help organizations get this right. Explore more on our Kubernetes autoscaling and optimization blog.
