Anyscale
Free tierProduction-scale AI infrastructure powered by Ray — built for foundation model workloads
Free tier available·Technical·API available
Key strengths
Built on Ray, the world's most widely adopted AI compute engine (500M+ downloads, 41K+ GitHub stars)Elastic distributed training across thousands of GPU nodes with PyTorch, vLLM, and SGLang supportMulti-cloud orchestration across AWS, GCP, Azure, Nebius, and CoreWeave without code rewritesFine-grained hardware allocation enabling per-task CPU/GPU/TPU assignmentAdvanced observability, access controls (SSO, SAML, SCIM), and audit logs for enterprise governance
Free tier + paid plans
San Francisco, USA
Founded 2019
Self-hostable
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Recent changes
- Open source: now available
- Self-hosting: now available
- Plans updated: Pay-as-you-go with $100 free credit to start. Hosted and BYOC options. Compute billed per hour (e.g. CPU-only AC $0.0135/hr, NVIDIA T4 AC $0.5682/hr, NVIDIA L4 AC $0.9542/hr, NVIDIA A10G AC $1.3635/hr, NVIDIA A100 AC $4.9591/hr). Committed contracts available for volume discounts.
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Anyscale — Production-Scale AI with Ray
Anyscale is a managed platform built on Ray, the open-source distributed compute framework with 43,970+ GitHub stars and 500M+ all-time downloads. It gives AI/ML engineers a unified layer for distributed training, batch inference, data curation, and serving — across GPU clusters of any scale.
Key technical capabilities:
- Elastic GPU/CPU/TPU orchestration — fine-grained hardware allocation across heterogeneous clusters with auto-scaling
- Multi-cloud & BYOC — deploy on AWS, GCP, Azure, Nebius, or CoreWeave; bring your own cloud account or run on-prem via Kubernetes
- Framework integrations — native support for PyTorch, vLLM, SGLang, XGBoost, SkyRL, and veRL
- Enterprise security — SSO, SAML, SCIM, and audit logs
- Observability — advanced GPU monitoring and cluster-level telemetry
- Open source — Ray is fully open source and self-hostable; Anyscale adds managed infrastructure, scheduling, and enterprise controls on top
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