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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Technical Use Cases
Anyscale is purpose-built for AI/ML engineers and foundation model builders working at scale. Core workload patterns the platform is designed for:
- Distributed model training — multi-node, multi-GPU training jobs with elastic scaling across GPU clusters using PyTorch, SkyRL, or veRL
- Batch inference at scale — high-throughput offline inference pipelines using vLLM or SGLang across heterogeneous GPU fleets
- Data curation & preprocessing — large-scale data pipelines for foundation model pre-training and fine-tuning datasets
- Fine-tuning — parameter-efficient and full fine-tuning workflows on A10G, A100, and other GPU tiers
- Multi-cloud orchestration — workload scheduling across AWS, GCP, Azure, Nebius, and CoreWeave from a single control plane
- Production AI serving — deploying and scaling model endpoints with advanced GPU monitoring and observability
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