--- title: "pai vs ray" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-pai-vs-ray-project-ray" tools: ["microsoft-pai", "ray-project-ray"] --- # pai vs ray *GraphCanon updated Aug 16, 2026* ## Verdict Pick pai if pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer; pick ray if ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. [pai](https://openpai.readthedocs.io) reports 2.7k GitHub stars, 549 forks, and 282 open issues, last pushed Jun 6, 2024. [ray](https://ray.io) has 44k stars, 7.9k forks, and 3.5k open issues, last pushed Aug 16, 2026. Figures are from public GitHub metadata via [pai's repository](https://github.com/microsoft/pai) and [ray's repository](https://github.com/ray-project/ray). | | [pai](/tools/microsoft-pai.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Tagline | Resource scheduling and cluster management for AI | Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads. | | Stars | 2,686 | 43,526 | | Forks | 549 | 7,929 | | Open issues | 282 | 3,483 | | Language | JavaScript | Python | | Adopt for | pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer. | Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project. | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pai](/tools/microsoft-pai.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 788d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 282 | 3.5k | | Stars delta | Unknown | +270 (30d) | | Open issues delta | Unknown | +14 (30d) | | Full report | [trust report](/tools/microsoft-pai/trust.md) | [trust report](/tools/ray-project-ray/trust.md) | ## Decision facts: pai - **Adopt for:** pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer. ## Decision facts: ray - **Adopt for:** Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. - **License detail:** Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project. ## Choose when ### Choose pai if… - pai is primarily JavaScript; ray is Python. - License: pai is MIT, ray is Apache-2.0. - Tags unique to pai: ai, artificial-intelligence, gpu, kubernetes. - When you are working with JavaScript-based projects and need to integrate model training or serving operations within your tech stack seamlessly ### Choose ray if… - ray is primarily Python; pai is JavaScript. - License: ray is Apache-2.0, pai is MIT. - Tags unique to ray: data-science, deep-learning, deployment, distributed. - When you need to develop applications that require the distribution of tasks across multiple machines. ## When NOT to use pai - For organizations that prefer a more comprehensive suite tailored for specific languages other than JavaScript, as the tool's focus is clearly on this language environment - When looking for solutions strictly hosted in cloud environments, as pai also supports deployment in on-premise settings which could complicate decisions if cloud dependency is critical ## When NOT to use ray - For simplistic projects or single-machine use cases, as Ray's distributed architecture may introduce unnecessary complexity. - If your project strictly adheres to languages other than Python, since most of the ecosystem and support revolves around Python. - When an environment already heavily utilizes another distributed computing framework that integrates deeply with specific needs, moving to Ray might not offer additional advantages over sticking with, - for example, an existing, well-integrated solution like Apache Spark for data processing. ## Common questions ### What is the difference between pai and ray? pai: Resource scheduling and cluster management for AI. ray: Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads.. See the comparison table for live GitHub stats and shared categories. ### When should I choose pai over ray? Choose pai over ray when pai is primarily JavaScript; ray is Python; License: pai is MIT, ray is Apache-2.0; Tags unique to pai: ai, artificial-intelligence, gpu, kubernetes; When you are working with JavaScript-based projects and need to integrate model training or serving operations within your tech stack seamlessly. ### When should I choose ray over pai? Choose ray over pai when ray is primarily Python; pai is JavaScript; License: ray is Apache-2.0, pai is MIT; Tags unique to ray: data-science, deep-learning, deployment, distributed; When you need to develop applications that require the distribution of tasks across multiple machines. ### When should I avoid pai? For organizations that prefer a more comprehensive suite tailored for specific languages other than JavaScript, as the tool's focus is clearly on this language environment When looking for solutions strictly hosted in cloud environments, as pai also supports deployment in on-premise settings which could complicate decisions if cloud dependency is critical ### When should I avoid ray? For simplistic projects or single-machine use cases, as Ray's distributed architecture may introduce unnecessary complexity. If your project strictly adheres to languages other than Python, since most of the ecosystem and support revolves around Python. When an environment already heavily utilizes another distributed computing framework that integrates deeply with specific needs, moving to Ray might not offer additional advantages over sticking with, for example, an existing, well-integrated solution like Apache Spark for data processing. ### Is pai or ray more popular on GitHub? ray has more GitHub stars (43,526 vs 2,686). Stars measure visibility, not whether either tool fits your constraints. ### Are pai and ray open source? Yes - both are open-source projects on GitHub (pai: MIT, ray: Apache-2.0). ### Where can I find alternatives to pai or ray? GraphCanon lists graph-backed alternatives at [pai alternatives](/tools/microsoft-pai/alternatives) and [ray alternatives](/tools/ray-project-ray/alternatives) ([pai markdown twin](/tools/microsoft-pai/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/microsoft-pai-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, pai or ray? pai: Archived. 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 pai and ray? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pai trust report](/tools/microsoft-pai/trust); [ray trust report](/tools/ray-project-ray/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-pai`](/api/graphcanon/graph?tool=microsoft-pai) - 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/_