Anyscale is an AI application platform built by the creators of Ray, based on Ray, a widely adopted AI compute engine. It is designed to help AI builders run data-intensive workloads and build and deploy foundation models and AI applications at scale on any cloud.
In terms of key capabilities, Anyscale supports foundation model builders in scaling distributed training, multimodal data curation, embedding generation, and post-training workloads. Specifically, this includes: multimodal data curation for large-scale pipeline data curation and preparation across video, images, text, and audio; distributed model training, supporting orchestration of model training across GPU clusters, with elastic scaling, last-mile data preprocessing, and GPU observability; batch embedding generation, processing and generating embeddings at scale for downstream search, retrieval, or training scenarios; and running LLM inference and training on post-training frameworks such as SkyRL and veRL, which are natively built on Ray.
In terms of usage, the platform can build, run, and optimize data-intensive training and inference pipelines on your own GPUs. It can scale data-intensive AI workloads, optimizing distributed training, data curation, and batch inference pipelines on Anyscale through Ray. With Python APIs, existing AI libraries such as PyTorch, vLLM, SGLang, and XGBoost can be scaled to thousands of nodes. The platform provides fine-grained machine control, a seamless agent-first experience, multi-cloud orchestration, performance-optimized Ray workloads, and advanced observability.
Ray itself supports executing Python functions and classes on distributed clusters through a single decorator; workloads can be composed using distributed functions and classes, each of which can run on different CPUs, GPUs, TPUs, or accelerator racks such as NVL72; high-throughput communication can be achieved using Ray's in-memory distributed object store or direct transfer via RDMA; Ray provides native libraries such as Ray Data and Ray Train, as well as a rapidly expanding ecosystem of third-party libraries such as vLLM and SkyRL.
In terms of unification and governance, Anyscale can unify and govern each team's AI workloads across clouds: pooling GPUs across clouds, regions, and K8s clusters so AI builders can move quickly; running training and inference on shared resource pools, dynamically reallocating capacity based on workload demands to maximize utilization; running the same code on AWS, GCP, Azure, Nebius, or CoreWeave, maximizing cross-region GPU access without rewriting for a specific cloud; providing access control and authentication, including SSO, SAML, SCIM, and audit logs, for secure multi-team security and governance.
Applicable scenarios include foundation model building, multimodal data curation, distributed model training, batch embedding generation, LLM inference and post-training processing, as well as cross-cloud GPU resource pooling and multi-team governance. Users can register for a free Anyscale account, access dozens of code templates on the Anyscale Platform, and learn to use Anyscale and Ray through self-paced courses, webinars, events, and more.
In terms of limitations, the materials do not provide specific pricing, costs, platform support details, or performance data; these facts are not currently provided.
