Comparison
ray-llm vs Awesome-LLMOps
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.
Markdown twin · ray-llm alternatives · Awesome-LLMOps alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | ray-llm | Awesome-LLMOps |
|---|---|---|
| Maintenance | Archived (507d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 5d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 5d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- ray-llm
- Archived repository; LLM serving APIs integrated into the Ray project
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- ray-llm
- 1.3k
- Awesome-LLMOps
- 5.9k
Forks
- ray-llm
- 90
- Awesome-LLMOps
- 993
Open issues
- ray-llm
- 0
- Awesome-LLMOps
- 247
Language
- ray-llm
- -
- Awesome-LLMOps
- Shell
Adopt for
- ray-llm
- Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`).
- Awesome-LLMOps
- 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
- ray-llm
- -
- Awesome-LLMOps
- -
Runtime
- ray-llm
- -
- Awesome-LLMOps
- -
License
- ray-llm
- -
- Awesome-LLMOps
- CC0-1.0
Last pushed
- ray-llm
- Mar 13, 2025
- Awesome-LLMOps
- May 21, 2026
Categories
- ray-llm
- Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- ray-llm
- Archived (8%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- ray-llm
- 507d
- Awesome-LLMOps
- 91d
Archived on GitHub
- ray-llm
- Yes
- Awesome-LLMOps
- No
Open issues (now)
- ray-llm
- 0
- Awesome-LLMOps
- 247
Stars delta
- ray-llm
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- ray-llm
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- ray-llm
- Trust report
- Awesome-LLMOps
- Trust report
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-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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ray-project/ray-llm) · observed Aug 2, 2026
- GitHub forks (ray-project/ray-llm) · observed Aug 2, 2026
- Last push (ray-project/ray-llm) · observed Mar 13, 2025
- License file (unknown) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ray-llm 1.3k · Awesome-LLMOps 5.9k (synced Aug 2, 2026).
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 and Awesome-LLMOps alternatives (ray-llm markdown twin, Awesome-LLMOps markdown twin), 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 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; Awesome-LLMOps trust report.