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Comparison

keras vs ray

Verdict

Pick keras when tags unique to keras: neural-networks, python, pytorch, tensorflow; pick ray when tags unique to ray: distributed, deployment, large-language-models, llm-inference.

Markdown twin · keras alternatives · ray alternatives

GraphCanon updated today

keras logo

keras

keras-team/keras

★ 64kpushed Jul 7, 2026
vs
ray logo

ray

ray-project/ray

★ 43kpushed Jul 11, 2026

Trust & integrity

Signalkerasray
Maintenance
Very active (4d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No criticals
As of today · osv@v1
No lockfile
As of today · none
keras trust reportray trust reportTrust methodology

Tagline

keras
Deep Learning for humans
ray
Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads.

Stars

keras
64k
ray
43k

Forks

keras
20k
ray
7.8k

Open issues

keras
228
ray
3.5k

Language

keras
Python
ray
Python

Adopt for

keras
-
ray
Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python.

Persona

keras
-
ray
-

Runtime

keras
-
ray
-

License

keras
Apache-2.0
ray
Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project.

Last pushed

keras
Jul 7, 2026
ray
Jul 11, 2026

Categories

keras
Model Training
ray
Model Training, Inference & Serving

Trust and health

Days since push

keras
4d
ray
0d

Open issues (now)

keras
228
ray
3.5k

Security scan

keras
No criticals
ray
No lockfile

Full report

keras
Trust report
ray
Trust report

Choose keras if…

  • Tags unique to keras: neural-networks, python, pytorch, tensorflow.
  • More GitHub stars (64k vs 43k) - visibility, not fit.

When NOT to use keras

  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
GitHubDetails

Choose ray if…

  • Tags unique to ray: distributed, deployment, large-language-models, llm-inference.
  • Also covers Inference & Serving.
  • When you need to develop applications that require the distribution of tasks across multiple machines.

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.
GitHubDetails

Explore

keras trust report →ray trust report →Model Training category →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: keras 64k · ray 43k (synced Jul 11, 2026).

Common questions

What is the difference between keras and ray?
keras: Deep Learning for humans. ray: Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads.. See the comparison table for live GitHub stats and shared categories.
When should I choose keras over ray?
Choose keras over ray when Tags unique to keras: neural-networks, python, pytorch, tensorflow; More GitHub stars (64k vs 43k) - visibility, not fit.
When should I choose ray over keras?
Choose ray over keras when Tags unique to ray: distributed, deployment, large-language-models, llm-inference; Also covers Inference & Serving; When you need to develop applications that require the distribution of tasks across multiple machines.
When should I avoid keras?
Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
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.
Is keras or ray more popular on GitHub?
keras has more GitHub stars (64,191 vs 43,190). Stars measure visibility, not whether either tool fits your constraints.
Are keras and ray open source?
Yes - both are open-source projects on GitHub (keras: Apache-2.0, ray: Apache-2.0).
Where can I find alternatives to keras or ray?
GraphCanon lists graph-backed alternatives at keras alternatives and ray alternatives (keras markdown twin, ray 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, keras or ray?
keras: Very active. ray: Very active. 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 keras and ray?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: keras trust report; ray trust report.