--- title: "keras vs serve" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/keras-team-keras-vs-pytorch-serve" tools: ["keras-team-keras", "pytorch-serve"] --- # keras vs serve *GraphCanon updated Jul 12, 2026* ## Verdict Pick keras when keras is primarily Python; serve is Java; pick serve when serve is primarily Java; keras is Python. [keras](http://keras.io/) reports 64k GitHub stars, 20k forks, and 228 open issues, last pushed Jul 7, 2026. [serve](https://pytorch.org/serve/) has 4.3k stars, 883 forks, and 443 open issues, last pushed Aug 6, 2025. Figures are from public GitHub metadata via [keras's repository](https://github.com/keras-team/keras) and [serve's repository](https://github.com/pytorch/serve). | | [keras](/tools/keras-team-keras.md) | [serve](/tools/pytorch-serve.md) | | --- | --- | --- | | Tagline | Deep Learning for humans | Serve, optimize and scale PyTorch models in production | | Stars | 64,191 | 4,350 | | Forks | 19,752 | 883 | | Open issues | 228 | 443 | | Language | Python | Java | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [keras](/tools/keras-team-keras.md) | [serve](/tools/pytorch-serve.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 4d | 339d | | Archived on GitHub | No | Yes | | Open issues (now) | 228 | 443 | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/keras-team-keras/trust.md) | [trust report](/tools/pytorch-serve/trust.md) | ## Shared compatibility - **Python**: [keras](/tools/keras-team-keras.md) - Python runtime; [serve](/tools/pytorch-serve.md) - Python runtime ## Choose when ### 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. ### 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 keras - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## 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. ## 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](/tools/keras-team-keras/alternatives) and [serve alternatives](/tools/pytorch-serve/alternatives) ([keras markdown twin](/tools/keras-team-keras/alternatives.md), [serve markdown twin](/tools/pytorch-serve/alternatives.md)), 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](/compare/keras-team-keras-vs-pytorch-serve.md) 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](/tools/keras-team-keras/trust); [serve trust report](/tools/pytorch-serve/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=keras-team-keras`](/api/graphcanon/graph?tool=keras-team-keras) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_