--- title: "ray-llm vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ray-project-ray-llm-vs-tensorchord-awesome-llmops" tools: ["ray-project-ray-llm", "tensorchord-awesome-llmops"] --- # ray-llm vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick ray-llm if archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`); pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [ray-llm](https://docs.ray.io/en/latest/) reports 1.3k GitHub stars, 90 forks, and 0 open issues, last pushed Mar 13, 2025. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [ray-llm's repository](https://github.com/ray-project/ray-llm) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [ray-llm](/tools/ray-project-ray-llm.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Archived repository; LLM serving APIs integrated into the Ray project | An awesome & curated list of best LLMOps tools for developers | | Stars | 1,261 | 5,915 | | Forks | 90 | 993 | | Open issues | 0 | 247 | | Language | - | Shell | | Adopt for | Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | - | CC0-1.0 | | Categories | Inference & Serving, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ray-llm](/tools/ray-project-ray-llm.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Slowing (36%) | | Days since push | 507d | 91d | | Archived on GitHub | Yes | No | | Open issues (now) | 0 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/ray-project-ray-llm/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## 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`). ## Decision facts: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### 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). ### Choose Awesome-LLMOps if… - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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. ## When NOT to use Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between ray-llm and Awesome-LLMOps? ray-llm: Archived repository; LLM serving APIs integrated into the Ray project. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose ray-llm over Awesome-LLMOps? Choose ray-llm over Awesome-LLMOps 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 choose Awesome-LLMOps over ray-llm? Choose Awesome-LLMOps over ray-llm when Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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. ### When should I avoid Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is ray-llm or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 1,261). Stars measure visibility, not whether either tool fits your constraints. ### Are ray-llm and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to ray-llm or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [ray-llm alternatives](/tools/ray-project-ray-llm/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([ray-llm markdown twin](/tools/ray-project-ray-llm/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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-llm-vs-tensorchord-awesome-llmops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ray-llm or Awesome-LLMOps? ray-llm: Archived. Awesome-LLMOps: Slowing. 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-llm and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ray-llm trust report](/tools/ray-project-ray-llm/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ray-project-ray-llm`](/api/graphcanon/graph?tool=ray-project-ray-llm) - 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/_