Comparison
ray vs ray-llm
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`).
Markdown twin · ray alternatives · ray-llm alternatives
GraphCanon updated 5d
Trust & integrity
| Signal | ray | ray-llm |
|---|---|---|
| Maintenance | Very active (0d since push) As of 5d · github_public_v1 | Archived (507d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 5d · github_public_v1 | Not a fork · Organization account As of 2w · 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
- 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
Stars
- ray
- 44k
- ray-llm
- 1.3k
Forks
- ray
- 7.9k
- ray-llm
- 90
Open issues
- ray
- 3.5k
- ray-llm
- 0
Language
- ray
- Python
- ray-llm
- -
Adopt for
- ray
- Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python.
- ray-llm
- Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`).
Persona
- ray
- -
- ray-llm
- -
Runtime
- ray
- -
- ray-llm
- -
License
- ray
- Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project.
- ray-llm
- -
Last pushed
- ray
- Aug 16, 2026
- ray-llm
- Mar 13, 2025
Categories
- ray
- Inference & Serving, Model Training
- ray-llm
- Inference & Serving, Model Training
Trust and health
Maintenance
- ray
- Very active (96%)
- ray-llm
- Archived (8%)
Days since push
- ray
- 0d
- ray-llm
- 507d
Archived on GitHub
- ray
- No
- ray-llm
- Yes
Open issues (now)
- ray
- 3.5k
- ray-llm
- 0
Stars delta
- ray
- +270 (30d)
- ray-llm
- Unknown
Open issues delta
- ray
- +14 (30d)
- ray-llm
- Unknown
Full report
- ray
- Trust report
- ray-llm
- Trust report
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.
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.
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.
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) · observed Aug 16, 2026
- GitHub forks (ray-project/ray) · observed Aug 16, 2026
- Last push (ray-project/ray) · observed Aug 16, 2026
- License file (Apache-2.0) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: ray 44k · ray-llm 1.3k (synced Aug 16, 2026).
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 and ray-llm alternatives (ray markdown twin, ray-llm 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 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; ray-llm trust report.