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Anyscale

Free tier

Production-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):

HardwarePrice/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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