Ray is an open source distributed computing framework built for the deployment and orchestration of scalable distributed computing environments for a variety of large scale AI workloads. Ray Serve provides a robust infrastructure for training complex machine learning models and running reinforcement learning algorithms at scale. Leveraging Kubernetes orchestration capabilities, Ray Serve simplifies the deployment process, allowing users to efficiently allocate resources and manage workloads across clusters. With support for distributed execution and parallelism, Ray Serve optimizes resource utilization and accelerates model training, enabling faster iteration and experimentation in AI research and development.
Deploy Ray Serve on Kubernetes via Helm.
Serve Python and ML inference workloads with Ray-native primitives.
Manage deployment updates and traffic handling for services.
Scale serving replicas with workload demand and cluster capacity.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy Ray Serve on Kubernetes.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by Ray. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
