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Comparison

keras vs serve

Verdict

Pick keras when keras is primarily Python; serve is Java; pick serve when serve is primarily Java; keras is Python.

Markdown twin · keras alternatives · serve alternatives

GraphCanon updated today

keras logo

keras

keras-team/keras

★ 64kpushed Jul 7, 2026
vs
serve logo

serve

pytorch/serve

★ 4.3kpushed Aug 6, 2025

Trust & integrity

Signalkerasserve
Maintenance
Very active (4d since push)
As of today · github_public_v1
Archived (339d 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 reportserve trust reportTrust methodology

Tagline

keras
Deep Learning for humans
serve
Serve, optimize and scale PyTorch models in production

Stars

keras
64k
serve
4.3k

Forks

keras
20k
serve
883

Open issues

keras
228
serve
443

Language

keras
Python
serve
Java

Adopt for

keras
-
serve
-

Persona

keras
-
serve
-

Runtime

keras
-
serve
-

License

keras
Apache-2.0
serve
Apache-2.0

Last pushed

keras
Jul 7, 2026
serve
Aug 6, 2025

Categories

keras
Model Training
serve
Inference & Serving, LLM Frameworks, Model Training

Trust and health

Maintenance

keras
Very active (96%)
serve
Archived (8%)

Days since push

keras
4d
serve
339d

Archived on GitHub

keras
No
serve
Yes

Open issues (now)

keras
228
serve
443

Security scan

keras
No criticals
serve
No lockfile

Full report

keras
Trust report
serve
Trust report

Shared compatibility

  • Python · keras: Python runtime · serve: Python runtime

Choose keras if…

  • keras is primarily Python; serve is Java.
  • Tags unique to keras: data-science, jax, neural-networks, python.
  • More GitHub stars (64k vs 4.3k) - 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 serve if…

  • serve is primarily Java; keras is Python.
  • Tags unique to serve: cpu, docker, gpu, kubernetes.
  • Also covers Inference & Serving, LLM Frameworks.

When NOT to use serve

  • serve is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
GitHubDetails

Explore

keras trust report →serve trust report →Model Training category →Inference & Serving category →LLM Frameworks 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 · serve 4.3k (synced Jul 11, 2026).

Common questions

What is the difference between keras and serve?
keras: Deep Learning for humans. serve: Serve, optimize and scale PyTorch models in production. See the comparison table for live GitHub stats and shared categories.
When should I choose keras over serve?
Choose keras over serve when keras is primarily Python; serve is Java; Tags unique to keras: data-science, jax, neural-networks, python; More GitHub stars (64k vs 4.3k) - visibility, not fit.
When should I choose serve over keras?
Choose serve over keras when serve is primarily Java; keras is Python; Tags unique to serve: cpu, docker, gpu, kubernetes; Also covers Inference & Serving, LLM Frameworks.
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 serve?
serve is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is keras or serve more popular on GitHub?
keras has more GitHub stars (64,191 vs 4,350). Stars measure visibility, not whether either tool fits your constraints.
Are keras and serve open source?
Yes - both are open-source projects on GitHub (keras: Apache-2.0, serve: Apache-2.0).
Where can I find alternatives to keras or serve?
GraphCanon lists graph-backed alternatives at keras alternatives and serve alternatives (keras markdown twin, serve 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 serve?
keras: Very active. serve: 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 keras and serve?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: keras trust report; serve trust report.