Anyscale

Anyscale is the company behind Ray, the open-source distributed computing framework that a large share of large-scale machine learning training and inference runs on under the hood. The Anyscale platform is a managed, hosted version of Ray: it handles cluster provisioning, autoscaling, and head node resilience so teams can run distributed training jobs, batch data processing, and model serving without operating Ray clusters themselves. It also runs on major clouds, including a first-party offering on Azure, and offers workspaces, jobs, and services as the core building blocks for developing and deploying Ray applications.

The platform is aimed at ML engineering teams working at a scale where a single machine or simple cloud VM setup no longer cuts it: think multi-node LLM fine-tuning, high-throughput inference serving, or large distributed data pipelines feeding model training. Beyond managed infrastructure, Anyscale has added the Anyscale Runtime, an API-compatible engine that speeds up data, training, and serving workloads without requiring code changes, along with lineage tracking that ties datasets and models back to the workspaces, jobs, and services that produced them, integrating with tools like MLflow, Weights & Biases, and Unity Catalog.

What sets Anyscale apart is that it's built and maintained by the team that created Ray itself, so it tends to get first access to new Ray capabilities and optimizations rather than layering support on top of someone else's framework. That makes it a natural choice for teams already committed to Ray who want to skip the operational burden of running and tuning clusters by hand, though it's a more specialized tool than general-purpose ML platforms since its value is really tied to using Ray as the underlying compute layer.