Comparison
ai-serving vs serving
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 serving if tensorFlow Serving is a high-performance system for deploying machine learning models in production, written in C++.
Markdown twin · ai-serving alternatives · serving alternatives
GraphCanon updated Sep 20, 2026
10views this month
vs
Trust & integrity
| Signal | ai-serving | serving |
|---|---|---|
| Maintenance | Slowing (208d since push) As of Sep 20, 2026 · github_public_v1 | Very active (1d 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
- serving
- Flexible, high-performance serving system for machine learning models
Stars
- ai-serving
- 166
- serving
- 6.4k
Forks
- ai-serving
- 31
- serving
- 2.2k
Open issues
- ai-serving
- 3
- serving
- 79
Language
- ai-serving
- Scala
- serving
- C++
Adopt for
- ai-serving
- Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker.
- serving
- TensorFlow Serving is a high-performance system for deploying machine learning models in production, written in C++.
Persona
- ai-serving
- -
- serving
- -
Runtime
- ai-serving
- -
- serving
- -
License
- ai-serving
- Apache-2.0
- serving
- Apache-2.0
Last pushed
- ai-serving
- Feb 24, 2026
- serving
- Sep 16, 2026
Categories
- ai-serving
- Inference & Serving
- serving
- Inference & Serving
Trust and health
Maintenance
- ai-serving
- Slowing (36%)
- serving
- Very active (96%)
Days since push
- ai-serving
- 208d
- serving
- 1d
Open issues (now)
- ai-serving
- 3
- serving
- 79
Stars delta
- ai-serving
- 0 (30d)
- serving
- +3 (30d)
Open issues delta
- ai-serving
- 0 (30d)
- serving
- -16 (30d)
Full report
- ai-serving
- Trust report
- serving
- Trust report
Choose ai-serving if…
- ai-serving is primarily Scala; serving is C++.
- 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 serving if…
- serving is primarily C++; ai-serving is Scala.
- Requirements: Requires Docker; Docker is required for easy deployment of TensorFlow Serving..
- Tags unique to serving: cpp, deep-learning, deep-neural-networks, machine-learning.
- When you need a system that supports multiple models, versions, and platforms, including Docker for easy deployment.
When NOT to use serving
- If your project is not aligned with TensorFlow's ecosystem and you prefer a different framework for model serving.
- When you require a solution that is not written in C++ and you prefer a different language for your deployment environment.
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 (tensorflow/serving) · observed Sep 20, 2026
- GitHub forks (tensorflow/serving) · observed Sep 20, 2026
- Last push (tensorflow/serving) · observed Sep 16, 2026
- 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 · serving 6.4k (synced Sep 20, 2026).
Common questions
- What is the difference between ai-serving and serving?
- ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. serving: Flexible, high-performance serving system for machine learning models. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-serving over serving?
- Choose ai-serving over serving when ai-serving is primarily Scala; serving is C++; 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 serving over ai-serving?
- Choose serving over ai-serving when serving is primarily C++; ai-serving is Scala; Requirements: Requires Docker; Docker is required for easy deployment of TensorFlow Serving.; Tags unique to serving: cpp, deep-learning, deep-neural-networks, machine-learning; When you need a system that supports multiple models, versions, and platforms, including Docker for easy deployment.
- 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 serving?
- If your project is not aligned with TensorFlow's ecosystem and you prefer a different framework for model serving. When you require a solution that is not written in C++ and you prefer a different language for your deployment environment.
- Is ai-serving or serving more popular on GitHub?
- serving has more GitHub stars (6,362 vs 166). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-serving and serving open source?
- Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, serving: Apache-2.0).
- Where can I find alternatives to ai-serving or serving?
- GraphCanon lists graph-backed alternatives at ai-serving alternatives and serving alternatives (ai-serving markdown twin, serving 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 serving?
- ai-serving: Slowing. serving: Very active. 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 serving?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; serving trust report.