AnyScale outlines KubeRay as the operator for running Ray clusters, jobs and Serve apps on Kubernetes.

AnyScale says KubeRay is the standard way to run Ray on Kubernetes, positioning the open-source operator as the bridge between Ray workloads and Kubernetes-native infrastructure. In a blog post published on 15 Oct. 2026, the company said KubeRay reduces the hand-built deployment, submission, scaling and recovery logic teams otherwise need when they try to run Ray directly on Kubernetes.

According to AnyScale, Ray is an open-source framework for scaling Python and AI workloads across many machines, while Kubernetes already manages infrastructure such as GPUs, quotas, access control, networking and monitoring. The problem, the company said, is that Kubernetes does not natively understand Ray clusters, Ray jobs or Ray Serve applications. KubeRay is designed to fill that gap by adding Kubernetes-native lifecycle management, autoscaling, scheduling, reliability and observability for Ray workloads.

AnyScale said KubeRay introduces three custom resources: RayCluster for long-running clusters, RayJob for single jobs that use ephemeral clusters, and RayService for Ray Serve applications. In the article, the company described RayCluster as the basic building block, with RayJob adding job submission and cleanup, and RayService adding Serve applications and a Serve service. The post also said KubeRay is the foundation of managed Ray offerings from major cloud providers.

The company drew a clear division of responsibilities between Kubernetes and Ray. Kubernetes schedules Pods onto nodes, while Ray schedules tasks and actors onto CPUs and GPUs inside those Pods. KubeRay sits between the two, translating a RayCluster spec into Pods and reporting Ray state back to Kubernetes through status fields and events.

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