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Comparison

BodhiApp vs serve

Verdict

Pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods; pick serve if serve is a toolkit for deploying PyTorch models in production, focusing on optimization and scaling with deep learning frameworks and Kubernetes. It supports deployment via TorchServe, Docker, and conda environments.

Markdown twin · BodhiApp alternatives · serve alternatives

GraphCanon updated Sep 20, 2026

13views this month

BodhiApp logo

BodhiApp

BodhiSearch/BodhiApp

★ 139pushed Sep 20, 2026
vs
serve logo

serve

pytorch/serve

★ 4.3kpushed Aug 6, 2025

Trust & integrity

SignalBodhiAppserve
Maintenance
Very active (0d since push)
As of Sep 20, 2026 · github_public_v1
Archived (407d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 15, 2026 · osv@v1
No lockfile (source not queried)
As of Sep 18, 2026 · 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
BodhiApp trust reportserve trust reportTrust methodology

Tagline

BodhiApp
Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs
serve
Serve, optimize and scale PyTorch models in production

Stars

BodhiApp
139
serve
4.3k

Forks

BodhiApp
11
serve
880

Open issues

BodhiApp
12
serve
443

Language

BodhiApp
TypeScript
serve
Java

Adopt for

BodhiApp
BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods.
serve
Serve is a toolkit for deploying PyTorch models in production, focusing on optimization and scaling with deep learning frameworks and Kubernetes. It supports deployment via TorchServe, Docker, and conda environments.

Persona

BodhiApp
-
serve
-

Runtime

BodhiApp
-
serve
-

License

BodhiApp
The license information for BodhiApp has not been provided.
serve
Apache-2.0

Last pushed

BodhiApp
Sep 20, 2026
serve
Aug 6, 2025

Categories

BodhiApp
Inference & Serving, LLM Frameworks
serve
Inference & Serving

Trust and health

Maintenance

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

Days since push

BodhiApp
0d
serve
407d

Archived on GitHub

BodhiApp
No
serve
Yes

Open issues (now)

BodhiApp
12
serve
443

Stars delta

BodhiApp
+3 (30d)
serve
-6 (30d)

Open issues delta

BodhiApp
+2 (30d)
serve
0 (30d)

Full report

BodhiApp
Trust report
serve
Trust report

Choose BodhiApp if…

  • BodhiApp is primarily TypeScript; serve is Java.
  • Pricing: Pricing details are not mentioned in the repository data..
  • Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen..
  • Tags unique to BodhiApp: gemma, generative-ai, llama, llm.
  • Also covers LLM Frameworks.
  • You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs.

When NOT to use BodhiApp

  • Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models.
  • If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods.
  • You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems.
GitHubDetails

Choose serve if…

  • serve is primarily Java; BodhiApp is TypeScript.
  • Tags unique to serve: cpu, deep-learning, docker, gpu.
  • When you need to deploy PyTorch models and require support for optimization and scaling with deep learning frameworks and Kubernetes.

When NOT to use serve

  • If your project does not involve PyTorch models or if you do not require deep learning framework and Kubernetes support for scaling and optimization.
  • If you are working with models that are not supported by the VLLM engine or TRT-LLM engine, such as models that are not listed in the repository's quick start examples.
  • If you are not working with Java or do not want to use an Apache-2.0 licensed toolkit, or if you prefer a different deployment method not covered by Serve, such as a different containerization or ML-ö
GitHubDetails

Explore

BodhiApp trust report →serve trust report →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: BodhiApp 139 · serve 4.3k (synced Sep 20, 2026).

Common questions

What is the difference between BodhiApp and serve?
BodhiApp: Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs. serve: Serve, optimize and scale PyTorch models in production. See the comparison table for live GitHub stats and shared categories.
When should I choose BodhiApp over serve?
Choose BodhiApp over serve when BodhiApp is primarily TypeScript; serve is Java; Pricing: Pricing details are not mentioned in the repository data.; Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.; Tags unique to BodhiApp: gemma, generative-ai, llama, llm; Also covers LLM Frameworks; You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs.
When should I choose serve over BodhiApp?
Choose serve over BodhiApp when serve is primarily Java; BodhiApp is TypeScript; Tags unique to serve: cpu, deep-learning, docker, gpu; When you need to deploy PyTorch models and require support for optimization and scaling with deep learning frameworks and Kubernetes.
When should I avoid BodhiApp?
Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems.
When should I avoid serve?
If your project does not involve PyTorch models or if you do not require deep learning framework and Kubernetes support for scaling and optimization. If you are working with models that are not supported by the VLLM engine or TRT-LLM engine, such as models that are not listed in the repository's quick start examples. If you are not working with Java or do not want to use an Apache-2.0 licensed toolkit, or if you prefer a different deployment method not covered by Serve, such as a different containerization or ML-ö
Is BodhiApp or serve more popular on GitHub?
serve has more GitHub stars (4,344 vs 139). Stars measure visibility, not whether either tool fits your constraints.
Are BodhiApp and serve open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to BodhiApp or serve?
GraphCanon lists graph-backed alternatives at BodhiApp alternatives and serve alternatives (BodhiApp 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, BodhiApp or serve?
BodhiApp: 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 BodhiApp and serve?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: BodhiApp trust report; serve trust report.

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