Home/Compare/ray vs ray-llm

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

ray logo

ray

ray-project/ray

44kpushed Aug 16, 2026
vs
ray-llm logo

ray-llm

ray-project/ray-llm

1.3kpushed Mar 13, 2025

Trust & integrity

Signalrayray-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

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 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.

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