Running Ray on AWS, GCP and Azure Without Locking Into One Cloud's Managed Service

Anyscale’s general availability on Azure AKS expands its managed Ray service across AWS, GCP, and Azure, but the discussion argues that Ray itself remains portable because it is open source and can run on Kubernetes through KubeRay. It distinguishes Ray as the distributed computing framework, KubeRay as its Kubernetes operator, and Anyscale as a commercial managed platform, noting that lock-in is more likely to arise from proprietary job APIs, workspaces, identity integrations, storage conventions, and data location than from Ray or a particular cloud. A portable approach uses shared KubeRay RayCluster, RayJob, and RayService manifests, common container images, cloud-specific configuration overlays for GPU infrastructure and identity, and storage URIs supplied through configuration rather than application code. GPU availability, drivers, quotas, interruption behavior, and accelerator types remain cloud-specific concerns, while moving large datasets and checkpoints across clouds can create substantial egress costs, making data locality important. The piece contrasts managed Anyscale, self-managed KubeRay, cloud-native ML platforms such as SageMaker and Vertex AI, and an internal developer platform, concluding that the appropriate choice depends on Kubernetes expertise, operational needs, cloud commitments, compliance requirements, and the real need for multi-cloud deployment.

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