--- title: "ray vs ray-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ray-project-ray-vs-ray-project-ray-llm" tools: ["ray-project-ray", "ray-project-ray-llm"] --- # ray vs ray-llm *GraphCanon updated Aug 16, 2026* ## Verdict Pick ray if ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python; pick ray-llm if archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). [ray](https://ray.io) reports 44k GitHub stars, 7.9k forks, and 3.5k open issues, last pushed Aug 16, 2026. [ray-llm](https://docs.ray.io/en/latest/) has 1.3k stars, 90 forks, and 0 open issues, last pushed Mar 13, 2025. Figures are from public GitHub metadata via [ray's repository](https://github.com/ray-project/ray) and [ray-llm's repository](https://github.com/ray-project/ray-llm). | | [ray](/tools/ray-project-ray.md) | [ray-llm](/tools/ray-project-ray-llm.md) | | --- | --- | --- | | Tagline | Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads. | Archived repository; LLM serving APIs integrated into the Ray project | | Stars | 43,526 | 1,261 | | Forks | 7,929 | 90 | | Open issues | 3,483 | 0 | | Language | Python | - | | Adopt for | Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. | Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). | | Persona | - | - | | Runtime | - | - | | License | 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._ | | [ray](/tools/ray-project-ray.md) | [ray-llm](/tools/ray-project-ray-llm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 0d | 507d | | Archived on GitHub | No | Yes | | Open issues (now) | 3.5k | 0 | | Stars delta | +270 (30d) | Unknown | | Open issues delta | +14 (30d) | Unknown | | Full report | [trust report](/tools/ray-project-ray/trust.md) | [trust report](/tools/ray-project-ray-llm/trust.md) | ## 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. ## Decision facts: ray-llm - **Adopt for:** Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). ## Choose when ### Choose ray if… - 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. - More GitHub stars (44k vs 1.3k) - visibility, not fit. ### Choose ray-llm if… - Tags unique to ray-llm: llm-serving, ray. - For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team. - Leaner open-issue backlog (0). ## 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. ## When NOT to use ray-llm - If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. - For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects. ## Common questions ### What is the difference between ray and ray-llm? ray: Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads.. ray-llm: Archived repository; LLM serving APIs integrated into the Ray project. See the comparison table for live GitHub stats and shared categories. ### When should I choose ray over ray-llm? Choose ray over ray-llm when 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; More GitHub stars (44k vs 1.3k) - visibility, not fit. ### When should I choose ray-llm over ray? Choose ray-llm over ray when Tags unique to ray-llm: llm-serving, ray; For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team; Leaner open-issue backlog (0). ### 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. ### When should I avoid ray-llm? If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects. ### Is ray or ray-llm more popular on GitHub? ray has more GitHub stars (43,526 vs 1,261). Stars measure visibility, not whether either tool fits your constraints. ### Are ray and ray-llm open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to ray or ray-llm? GraphCanon lists graph-backed alternatives at [ray alternatives](/tools/ray-project-ray/alternatives) and [ray-llm alternatives](/tools/ray-project-ray-llm/alternatives) ([ray markdown twin](/tools/ray-project-ray/alternatives.md), [ray-llm markdown twin](/tools/ray-project-ray-llm/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/ray-project-ray-vs-ray-project-ray-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ray or ray-llm? ray: Very active. ray-llm: Archived. 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 ray and ray-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ray trust report](/tools/ray-project-ray/trust); [ray-llm trust report](/tools/ray-project-ray-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ray-project-ray`](/api/graphcanon/graph?tool=ray-project-ray) - 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/_