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 offers dedicated support for deploying and scaling PyTorch models with features tailored towards large language model deployment, such as integration with Hugging Face.
Markdown twin · ai-serving alternatives · serve alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | ai-serving | serve |
|---|---|---|
| Maintenance | Slowing (171d since push) As of 1w · github_public_v1 | Archived (360d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- 882
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 offers dedicated support for deploying and scaling PyTorch models with features tailored towards large language model deployment, such as integration with Hugging Face.
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
- 171d
- serve
- 360d
Archived on GitHub
- ai-serving
- No
- serve
- Yes
Open issues (now)
- ai-serving
- 3
- serve
- 443
Stars delta
- ai-serving
- 0 (30d)
- serve
- Unknown
Open issues delta
- ai-serving
- 0 (30d)
- serve
- Unknown
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.
- If you are working primarily with PyTorch-based machine-learning projects that require production-grade deployments.
When NOT to use serve
- Avoid if your primary model development is not in PyTorch or requires deployment using a language other than Java.
- Not suitable if you do not require the fine-grained control and optimization provided by tools such as VLLM or TensorRT-LLM.
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 Aug 14, 2026
- GitHub forks (autodeployai/ai-serving) · observed Aug 14, 2026
- Last push (autodeployai/ai-serving) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (pytorch/serve) · observed Aug 2, 2026
- GitHub forks (pytorch/serve) · observed Aug 2, 2026
- Last push (pytorch/serve) · observed Aug 6, 2025
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ai-serving 166 · serve 4.3k (synced Aug 14, 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; If you are working primarily with PyTorch-based machine-learning projects that require production-grade deployments.
- 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?
- Avoid if your primary model development is not in PyTorch or requires deployment using a language other than Java. Not suitable if you do not require the fine-grained control and optimization provided by tools such as VLLM or TensorRT-LLM.
- Is ai-serving or serve more popular on GitHub?
- serve has more GitHub stars (4,350 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.