Anyscale Platform
From the creators of Ray, Anyscale helps teams build and run AI workloads at production-scale with speed, reliability, and cost-efficiency
runs 80% cheaper embedding generation.
runs 80% cheaper embedding generation.
achieves 3x faster batch inference on videos.
achieves 3x faster batch inference on videos.
processes 10x larger robotics datasets.
processes 10x larger robotics datasets.
12x faster runs while cutting cloud costs by 50%.
12x faster runs while cutting cloud costs by 50%.
trains fraud models with TB-sized datasets.
trains fraud models with TB-sized datasets.
runs 5x faster training with 12x more data.
runs 5x faster training with 12x more data.
achieves 20% lower latency for multimodal search
achieves 20% lower latency for multimodal search
runs real-time image processing at 25% lower cost
runs real-time image processing at 25% lower cost runs 80% cheaper embedding generation.
achieves 3x faster batch inference on videos.
processes 10x larger robotics datasets.
Scalable multimodal data processing with CPUs and GPUs working as a unified pipeline
Build and deploy AI workloads at scale
Feels Local. Runs distributed.
Build, debug, and ship AI workloads without changing how you write code, only how much it scales.
Deploy Anywhere. Scale Reliably.
Deploy fault-tolerant Ray clusters across any cloud. Built-in resilience and autoscaling, no manual ops.
Keep GPUs Busy. Budgets Lean.
Use advanced workload scheduling to maximize GPU utilization, and use budgets keep costs under control.
Developer Experience
Build and iterate on AI workloads of any size on any cloud.
Cluster-backed VS Code/Jupyter and managed dashboards for building and debugging scalable AI from dev to prod.
Workspaces. Build, debug and deploy AI on scalable, coding agent-ready Ray clusters with <1 min startup and fast uv syncs.
Observability. Workload-specific dashboards backed with persistent logs make debugging Ray Data, Train, and Serve workloads fast and simple.
Jobs & Services. Production-grade, managed Ray clusters for data, training, and serving with head node resilience, autoscaling, A/B rollouts & more.
Lineage Tracking. Visual traceability across datasets and models that provide pipeline transparency for faster reproduction and audits.
Anyscale Runtime. Fully managed, Ray-compatible runtime, supported by the leading Ray experts-so you can build faster and run with confidence in production, without vendor lock-in.
Platform Orchestration
Deploy and govern Ray clusters reliably on any cloud
Manage Ray across teams, and unlock capacity across regions and clouds while keeping costs under control.
Multi Cloud. Deploy and manage AI workloads on any region or cloud on Kubernetes or VMs with a single control plane. Find GPU capacity wherever it exists.
Orchestration. Cross-cloud, priority-aware scheduling ensures critical workloads run first and queues are efficiently managed to keep GPUs busy.
Monitoring. Real-time and persisted visibility into Ray cluster health, CPU/GPU utilization, memory, and more in our UI or in your monitoring tool of choice.
Governance. Access controls and authentication including SSO, SAML, SCIM, and audit logs for secure multi-team security and governance.
Budgets. Usage attribution and spend quotas to allocate and control GPU cost as your teams expand and workloads scale.
Ray runs faster on Anyscale.
Ray is the core distributed compute engine. Anyscale provides the production-grade platform around it, including developer tooling, workload-aware observability, and cluster orchestration.
