Your Java apps have unique architectural demands, and so should your JVM workload optimization. Get tailored recommendations for heap size with machine learning-based analysis of your Java metrics, along with recommendations for Kubernetes resource requests and limits.
By knowing the appropriate heap size, containers can be sized accordingly at scale — no manual tuning involved. Other Kubernetes rightsizing recommendations are based solely on memory usage at the container level, so they cannot rightsize your heap.
Flexibility is key when rightsizing any Kubernetes workload. With various configuration settings, you can review and apply recommendations your way through an intuitive UI, or set policies for automatic deployment to achieve continuous performance optimization.
If you’re running Java applications in Kubernetes, and you’d like to stop manually rightsizing, sign up to request limited availability access to Java Workload Optimization.
