--- title: "dstack vs ray" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dstackai-dstack-vs-ray-project-ray" tools: ["dstackai-dstack", "ray-project-ray"] --- # dstack vs ray *GraphCanon updated Sep 20, 2026* ## Verdict Pick dstack if dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments; pick ray if ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. [dstack](https://dstack.ai/docs) reports 2.3k GitHub stars, 262 forks, and 64 open issues, last pushed Sep 17, 2026. [ray](https://ray.io) has 44k stars, 8.1k forks, and 3.6k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [dstack's repository](https://github.com/dstackai/dstack) and [ray's repository](https://github.com/ray-project/ray). | | [dstack](/tools/dstackai-dstack.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Tagline | Vendor-agnostic orchestration for training, inference and agentic workloads | Unified framework for scaling AI and Python applications | | Stars | 2,253 | 43,863 | | Forks | 262 | 8,052 | | Open issues | 64 | 3,590 | | Language | Python | Python | | Adopt for | dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments. | Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. | | Persona | - | - | | Runtime | - | - | | License | dstack is licensed under the Mozilla Public License 2.0, which is an open-source license. | Apache-2.0 | | Categories | AI Agents, Inference & Serving, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dstack](/tools/dstackai-dstack.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Open issues (now) | 64 | 3.6k | | Stars delta | +61 (30d) | +337 (30d) | | Open issues delta | +3 (30d) | +107 (30d) | | Full report | [trust report](/tools/dstackai-dstack/trust.md) | [trust report](/tools/ray-project-ray/trust.md) | ## Decision facts: dstack - **Pricing:** freemium - The core functionality of dstack is free and open-source under the Mozilla Public License 2.0. Additional services or support may be available for a fee. - **Requirements:** Requires Docker; Requires Git and OpenSSH for CLI installation and configuration. - **Adopt for:** dstack is a vendor-agnostic orchestration tool for AI workloads, including training, inference, and agentic tasks, across various hardware and environments. - **License detail:** dstack is licensed under the Mozilla Public License 2.0, which is an open-source license. ## Decision facts: ray - **Adopt for:** Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads. ## Choose when ### Choose dstack if… - License: dstack is MPL-2.0, ray is Apache-2.0. - Pricing: The core functionality of dstack is free and open-source under the Mozilla Public License 2.0. Additional services or support may be available for a fee.. - Requirements: Requires Docker; Requires Git and OpenSSH for CLI installation and configuration.. - Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud. - Also covers AI Agents. - When you need orchestration for AI workloads that can run on a variety of hardware like NVIDIA, AMD, TPU, and Tenstorrent, and across different environments such as clouds, Kubernetes, and bare metal. ### Choose ray if… - License: ray is Apache-2.0, dstack is MPL-2.0. - Tags unique to ray: data-science, deep-learning, deployment, distributed. - Also covers Developer Tools, Evaluation & Observability. - When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ## When NOT to use dstack - If you are looking for a tool that is tightly integrated with a specific cloud provider or hardware vendor, as dstack is designed to be vendor-agnostic. - When your project strictly requires proprietary licensing, as dstack is licensed under the Mozilla Public License 2.0, which is open source. - If your team does not have the capability to install and configure the dstack CLI, which requires Git and OpenSSH, and involves setting up a server and configuring it with a user token and project URL ## When NOT to use ray - If your project is limited to single-node development and does not require scaling across multiple machines or clusters. - When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU - If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ## Common questions ### What is the difference between dstack and ray? dstack: Vendor-agnostic orchestration for training, inference and agentic workloads. ray: Unified framework for scaling AI and Python applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose dstack over ray? Choose dstack over ray when License: dstack is MPL-2.0, ray is Apache-2.0; Pricing: The core functionality of dstack is free and open-source under the Mozilla Public License 2.0. Additional services or support may be available for a fee.; Requirements: Requires Docker; Requires Git and OpenSSH for CLI installation and configuration.; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents; When you need orchestration for AI workloads that can run on a variety of hardware like NVIDIA, AMD, TPU, and Tenstorrent, and across different environments such as clouds, Kubernetes, and bare metal. ### When should I choose ray over dstack? Choose ray over dstack when License: ray is Apache-2.0, dstack is MPL-2.0; Tags unique to ray: data-science, deep-learning, deployment, distributed; Also covers Developer Tools, Evaluation & Observability; When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs. ### When should I avoid dstack? If you are looking for a tool that is tightly integrated with a specific cloud provider or hardware vendor, as dstack is designed to be vendor-agnostic. When your project strictly requires proprietary licensing, as dstack is licensed under the Mozilla Public License 2.0, which is open source. If your team does not have the capability to install and configure the dstack CLI, which requires Git and OpenSSH, and involves setting up a server and configuring it with a user token and project URL ### When should I avoid ray? If your project is limited to single-node development and does not require scaling across multiple machines or clusters. When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU If you are working in a non-Python environment, as Ray is primarily designed for Python applications. ### Is dstack or ray more popular on GitHub? ray has more GitHub stars (43,863 vs 2,253). Stars measure visibility, not whether either tool fits your constraints. ### Are dstack and ray open source? Yes - both are open-source projects on GitHub (dstack: MPL-2.0, ray: Apache-2.0). ### Where can I find alternatives to dstack or ray? GraphCanon lists graph-backed alternatives at [dstack alternatives](/tools/dstackai-dstack/alternatives) and [ray alternatives](/tools/ray-project-ray/alternatives) ([dstack markdown twin](/tools/dstackai-dstack/alternatives.md), [ray markdown twin](/tools/ray-project-ray/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dstackai-dstack-vs-ray-project-ray.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dstack or ray? dstack: Very active. ray: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for dstack and ray? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dstack trust report](/tools/dstackai-dstack/trust); [ray trust report](/tools/ray-project-ray/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dstackai-dstack`](/api/graphcanon/graph?tool=dstackai-dstack) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_