Ray

Ray is an open source framework developed by Anyscale for managing, executing and optimising compute across AI workloads. It is Python native, which means existing Python code can be scaled and distributed rather than rewritten, and it handles both structured and unstructured data including images, video and audio. The libraries cover distributed training, model serving, batch inference, reinforcement learning through Ray RLlib, and end to end generative AI pipelines. It uses heterogeneous GPUs and CPUs with fine grained scaling and runs from a laptop up to clusters of thousands of GPUs.

- Scale and distribute ordinary Python code across a cluster.

- Train large models, including generative AI base models and traditional machine learning models.

- Serve models and business logic with independent scaling.

- Run offline batch inference across CPUs and GPUs in one pipeline.

- Run reinforcement learning workloads through Ray RLlib.

- Serve and fine-tune large language models for online or batch use.

- AI and machine learning developers building production pipelines.

- Enterprise teams with heavy compute workloads and utilisation problems.

- Researchers processing large datasets and complex models.

- Platform teams standardising how workloads are scheduled and scaled.

Distributed systems are genuinely hard, and Ray does not remove that: concepts like task scheduling, actor lifecycle and cluster configuration take time to learn, so budget for it. The framework is open source, but cluster compute is a separate cost, and a free trial credit on Anyscale is limited before paid platform use begins. The performance and cost improvements quoted in the listing are vendor examples from specific workloads and will not transfer automatically to yours, so benchmark on your own data before believing them. Finally, operating a cluster brings its own reliability and cost management work, and misconfigured jobs can waste serious money.