Guide to Kubernetes Autoscaling For Cloud Cost Optimization

Kubernetes is all about containerization and running more workloads on the same server instance can seem cost-effective. However, tracking which projects or teams generate Kubernetes costs is challenging, making it difficult to know if savings are being achieved from your cluster. One tactic that helps in this regard is autoscaling. The tighter your Kubernetes scaling mechanisms are configured, the lower the waste and costs of running your application. Kubernetes supports two types of autoscaling: horizontal and vertical. Horizontal autoscaling allows you to create rules for starting or stopping instances assigned to a resource when they breach upper or lower thresholds. Vertical autoscaling is based on rules that affect the amount of CPU or RAM allocated to an existing instance. There are three main Kubernetes autoscaling methods: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler. HPA scales pod replicas based on the mean of a per-pod metric value, while VPA increases or decreases CPU and memory resource requests for pod containers to match allocated cluster resources to actual usage better. Cluster Autoscaler changes the number of nodes in a cluster and can only manage nodes on supported platforms. To use these autoscaling methods effectively, it's essential to follow best practices such as ensuring HPA and VPA policies don't clash, using instance weighted scores, reducing costs with mixed instances, and automating Kubernetes autoscaling even more with tools like CAST AI.