Anyscale

Overview

Anyscale is a production-scale AI compute platform powered by Ray, the world’s most widely adopted AI compute engine. Created and maintained by the original Ray team, Anyscale is designed for foundation model builders and enterprises that need to run data-intensive AI workloads reliably and efficiently. It enables teams to build, run, and optimize distributed training, multimodal data curation, batch embedding generation, and post-training pipelines on their own GPUs, across any cloud, or on-premises. With more than 500 million all-time downloads of Ray, 41,000+ GitHub stars, and 1,200+ contributors, the Ray ecosystem provides a robust foundation for production AI, and Anyscale operationalizes it with a managed, secure platform.

The core of Anyscale is its ability to scale existing AI libraries—such as PyTorch, vLLM, SGLang, and XGBoost—using simple Python APIs. Developers can annotate functions or classes with Ray decorators and execute them on distributed clusters with fine-grained hardware allocation, specifying CPUs, GPUs, TPUs, or specialized accelerator racks like NVL72. Ray’s native libraries, including Ray Data for data processing and Ray Train for distributed training, combine with a rapidly expanding ecosystem of third-party libraries to support a wide variety of AI workloads. Efficient distributed communication is achieved through Ray’s in-memory distributed object store or direct transport over RDMA, enabling high-throughput data exchange between workers. Anyscale also streamlines multimodal data curation for videos, images, text, and audio, and supports post-training frameworks like SkyRL and veRL natively on Ray for LLM inference and alignment workloads.

Anyscale delivers multi-cloud orchestration and a seamless agent-first experience. Teams can pool GPUs across AWS, GCP, Azure, Nebius, or CoreWeave and dynamically reallocate capacity to maximize utilization. The same code runs on any cloud without cloud-specific rewrites, enabling maximum GPU access across regions. The platform includes fine-grained machine control, advanced observability, and price-performance optimized Ray workloads. Enterprise security is built-in with access controls, SSO, SAML, SCIM, and audit logging, ensuring secure and governed multi-team collaboration. This allows organizations to unify and govern every team’s AI workloads across clouds, regions, and Kubernetes clusters.

Anyscale is trusted by AI builders to accelerate innovation and remove infrastructure bottlenecks. Customers report 13x faster model loading and 4x faster experimentation for visual language models (VLAs). By abstracting distributed systems complexity, Anyscale lets teams focus on modeling and product development rather than cluster management. The platform offers a free tier with $100 credit, dozens of code templates, and access to self-service courses, webinars, and technical events for developers to get started quickly. It is well-suited for teams working on foundation models, large-scale media processing, RAG pipelines, and post-training optimization across cloud and on-prem environments.

Core capabilities

Distributed Model Training

Orchestrate model training across GPU clusters with elastic scaling, last-mile data preprocessing, and GPU observability.

Multimodal Data Curation

Run large-scale pipelines to curate and prepare multimodal data across videos, images, text, and audio.

Batch Embedding Generation

Process and generate embeddings at scale for downstream search, retrieval, or training use cases.

Post-training on Ray

Run LLM inference and training on native Ray frameworks like SkyRL and veRL for post-training workloads.

Simple Python APIs

Execute Python functions and classes on a distributed cluster with a single decorator.

Fine-grained Hardware Allocation

Compose workloads with distributed functions and classes running on different CPUs, GPUs, TPUs, or accelerator racks such as NVL72.

Multi-cloud Execution

Run the same code across AWS, GCP, Azure, Nebius, or CoreWeave without cloud-specific rewrites.

Pooled GPU Resources

Dynamically reallocate capacity across a shared resource pool to maximize GPU utilization.

Enterprise Governance

Secure multi-team collaboration with SSO, SAML, SCIM, access controls, and audit logs.

Use cases

Multimodal data curation at scale

Distributed training of large foundation models

Batch embedding generation for search and retrieval

Post-training LLM alignment with frameworks like SkyRL and veRL

Batch inference on vLLM and SGLang

Pooling GPU capacity across clouds and regions

Accelerating experimentation for visual language models