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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Developer Docs & Integration
Anyscale is built on Ray, which is open source and self-hostable. The managed Anyscale platform layers production scheduling, observability, and enterprise controls on top of the Ray API surface developers already know.
Integration surface:
- Training frameworks: PyTorch, XGBoost, SkyRL, veRL
- Inference engines: vLLM, SGLang
- Infrastructure: AWS, GCP, Azure, Nebius, CoreWeave, Kubernetes (for BYOC/on-prem)
- Auth & compliance: SSO, SAML, SCIM, audit logs
Deployment models:
- Hosted — fully managed by Anyscale
- BYOC (Bring Your Own Cloud) — Anyscale control plane, your cloud account and VPC
- Self-hosted / on-prem — deploy Ray directly on Kubernetes
Compute pricing reference (AC = Anyscale Compute):
| Hardware | Price/hr |
|---|---|
| CPU-only | $0.0135 |
| NVIDIA T4 | $0.5682 |
| NVIDIA L4 | $0.9542 |
| NVIDIA A10G | $1.3635 |
| NVIDIA A100 | $4.9591 |
Committed contracts are available for volume discounts.
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