HPA vs VPA: When to Use Each, and Can You Use Both?
Blog post from Cast AI
Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) are two distinct Kubernetes tools designed to optimize resource efficiency, each addressing different aspects of autoscaling. HPA adjusts the number of pod replicas based on metrics such as CPU utilization, making it suitable for stateless workloads like web APIs and microservices that benefit from parallel processing. Conversely, VPA fine-tunes individual pod resource requests, focusing on CPU and memory adjustments based on historical usage data, which is ideal for stateful applications and environments where resource allocation is critical, such as JVM services and machine learning inference servers. Both tools can be used together, provided their metrics are separate to avoid conflicts, with HPA typically managing CPU-based metrics and VPA handling memory adjustments. While HPA is built into Kubernetes, VPA requires an external installation and works through components like the Recommender and Updater. An alternative solution, Cast AI Workload Optimization, offers in-place rightsizing without pod eviction and can handle both pod and node-level optimizations, potentially simplifying the management of resource allocations across Kubernetes clusters.
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