What is Ray used for?

Ray is an open source framework for managing, executing, and optimizing compute needs, designed to support any AI or ML workload. It is a good fit for distributed training of AI and ML models, model serving and deployment, and batch inference workflows.

Yes. Ray is open source under the Apache-2.0 licence. Its source code is on GitHub at ray-project/ray and is written mainly in Python.

Yes. Ray is open source, so the software itself is free to use. A managed cloud version is also available.

Yes. Ray can be self-hosted on your own server or infrastructure.

What is Ray an alternative to?

Ray is an open-source alternative to OpenRouter, Together AI, Amazon SageMaker and Fireworks AI. Other open-source alternatives to OpenRouter include vLLM and LocalAI.

Is Ray actively maintained?

Yes. The most recent commit to Ray was on 14 October 2026. The project has 44k stars on GitHub.

Ray: open-source alternative to OpenRouter, Together AI

About Ray

Ray is an AI compute engine that consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. It is Python-native and built by developers for developers, designed to orchestrate infrastructure for distributed workloads on any accelerator at any scale.

The platform aims to address the AI Complexity Wall by supporting any AI or ML workload, any data types and model architectures, and heterogeneous GPUs and CPUs with fine-grained scaling. It can scale from a laptop to thousands of GPUs.

Key features

Supports any AI or ML workload
Handles multi-modal data processing including images, videos, audio
Runs distributed training for Gen AI foundation models, time series models, and traditional AI/ML models
Offers model serving with independent scaling and fractional resources via Ray Serve
Supports batch inference with heterogeneous compute using CPUs and GPUs

Good fit for

Distributed training of AI and ML models
Model serving and deployment
Batch inference workflows

Built with: Python tools
Tags: ai compute engine; distributed computing; machine learning; python; open source

Ray: questions and answers

What is Ray used for?: Ray is an open source framework for managing, executing, and optimizing compute needs, designed to support any AI or ML workload. It is a good fit for distributed training of AI and ML models, model serving and deployment, and batch inference workflows.
Is Ray open source?: Yes. Ray is open source under the Apache-2.0 licence. Its source code is on GitHub at ray-project/ray and is written mainly in Python.
Is Ray free?: Yes. Ray is open source, so the software itself is free to use. A managed cloud version is also available.
Can I self-host Ray?: Yes. Ray can be self-hosted on your own server or infrastructure.
What is Ray an alternative to?: Ray is an open-source alternative to OpenRouter, Together AI, Amazon SageMaker and Fireworks AI. Other open-source alternatives to OpenRouter include vLLM and LocalAI.
Is Ray actively maintained?: Yes. The most recent commit to Ray was on 14 October 2026. The project has 44k stars on GitHub.