Kubernetes on Vultr scales pods with HPA on CPU, memory or custom metrics, and nodes via autoscaling.
Kubernetes scales applications automatically using a combination of Horizontal Pod Autoscaling (HPA) and Cluster Autoscaling.
Horizontal Pod Autoscaling adjusts the number of pod replicas in a deployment or stateful set based on observed metrics such as CPU utilization, memory usage, or custom application metrics. This ensures that the application can handle increased load without manual intervention.
Cluster Autoscaling works at the node level. If the scheduler cannot place pods due to insufficient resources, the cluster can automatically provision additional nodes. Conversely, when nodes are underutilized, unused nodes can be removed to optimize costs. Together, these mechanisms allow Kubernetes to maintain application performance while efficiently using cluster resources.
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