Comparison
ai-serving vs serve
Verdict
Pick ai-serving if ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker; 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 · ai-serving alternatives · serve alternatives
GraphCanon updated Sep 20, 2026
14views this month
vs
Trust & integrity
| Signal | ai-serving | serve |
|---|---|---|
| Maintenance | Slowing (208d 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 |
Tagline
- ai-serving
- Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints
- serve
- Serve, optimize and scale PyTorch models in production
Stars
- ai-serving
- 166
- serve
- 4.3k
Forks
- ai-serving
- 31
- serve
- 880
Open issues
- ai-serving
- 3
- serve
- 443
Language
- ai-serving
- Scala
- serve
- Java
Adopt for
- ai-serving
- Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker.
- 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
- ai-serving
- -
- serve
- -
Runtime
- ai-serving
- -
- serve
- -
License
- ai-serving
- Apache-2.0
- serve
- Apache-2.0
Last pushed
- ai-serving
- Feb 24, 2026
- serve
- Aug 6, 2025
Categories
- ai-serving
- Inference & Serving
- serve
- Inference & Serving
Trust and health
Maintenance
- ai-serving
- Slowing (36%)
- serve
- Archived (8%)
Days since push
- ai-serving
- 208d
- serve
- 407d
Archived on GitHub
- ai-serving
- No
- serve
- Yes
Open issues (now)
- ai-serving
- 3
- serve
- 443
Stars delta
- ai-serving
- 0 (30d)
- serve
- -6 (30d)
Full report
- ai-serving
- Trust report
- serve
- Trust report
Choose ai-serving if…
- ai-serving is primarily Scala; serve is Java.
- Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx.
- When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats.
When NOT to use ai-serving
- Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs.
- Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice.
- If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images.
Choose serve if…
- serve is primarily Java; ai-serving is Scala.
- 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (autodeployai/ai-serving) · observed Sep 20, 2026
- GitHub forks (autodeployai/ai-serving) · observed Sep 20, 2026
- Last push (autodeployai/ai-serving) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (pytorch/serve) · observed Sep 20, 2026
- GitHub forks (pytorch/serve) · observed Sep 20, 2026
- Last push (pytorch/serve) · observed Aug 6, 2025
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: ai-serving 166 · serve 4.3k (synced Sep 20, 2026).
Common questions
- What is the difference between ai-serving and serve?
- ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. serve: Serve, optimize and scale PyTorch models in production. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-serving over serve?
- Choose ai-serving over serve when ai-serving is primarily Scala; serve is Java; Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats.
- When should I choose serve over ai-serving?
- Choose serve over ai-serving when serve is primarily Java; ai-serving is Scala; 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 ai-serving?
- Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images.
- 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 ai-serving or serve more popular on GitHub?
- serve has more GitHub stars (4,344 vs 166). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-serving and serve open source?
- Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, serve: Apache-2.0).
- Where can I find alternatives to ai-serving or serve?
- GraphCanon lists graph-backed alternatives at ai-serving alternatives and serve alternatives (ai-serving 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, ai-serving or serve?
- ai-serving: Slowing. 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 ai-serving and serve?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; serve trust report.