Comparison
ai-serving vs onnx-mlir
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 onnx-mlir if oNNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Markdown twin · ai-serving alternatives · onnx-mlir alternatives
GraphCanon updated 6d
vs
Trust & integrity
| Signal | ai-serving | onnx-mlir |
|---|---|---|
| Maintenance | Slowing (171d since push) As of 6d · github_public_v1 | Very active (3d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 6d · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- onnx-mlir
- ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes
Stars
- ai-serving
- 166
- onnx-mlir
- 1.0k
Forks
- ai-serving
- 31
- onnx-mlir
- 447
Open issues
- ai-serving
- 3
- onnx-mlir
- 352
Language
- ai-serving
- Scala
- onnx-mlir
- 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.
- onnx-mlir
- ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments.
Persona
- ai-serving
- -
- onnx-mlir
- -
Runtime
- ai-serving
- -
- onnx-mlir
- -
License
- ai-serving
- Apache-2.0
- onnx-mlir
- Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms.
Last pushed
- ai-serving
- Feb 24, 2026
- onnx-mlir
- Jul 31, 2026
Categories
- ai-serving
- Inference & Serving
- onnx-mlir
- Inference & Serving, Model Training
Trust and health
Maintenance
- ai-serving
- Slowing (36%)
- onnx-mlir
- Very active (96%)
Days since push
- ai-serving
- 171d
- onnx-mlir
- 3d
Open issues (now)
- ai-serving
- 3
- onnx-mlir
- 352
Stars delta
- ai-serving
- 0 (30d)
- onnx-mlir
- Unknown
Open issues delta
- ai-serving
- 0 (30d)
- onnx-mlir
- Unknown
OSV dependency advisories
- ai-serving
- No lockfile (source not queried)
- onnx-mlir
- Published findings
Full report
- ai-serving
- Trust report
- onnx-mlir
- Trust report
Choose ai-serving if…
- ai-serving is primarily Scala; onnx-mlir is C++.
- Tags unique to ai-serving: ai-serving, grpc, inference-server, pmml-deployment.
- 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 onnx-mlir if…
- onnx-mlir is primarily C++; ai-serving is Scala.
- Tags unique to onnx-mlir: compiler, llvm, mlir, runtime environments.
- Also covers Model Training.
- For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python
When NOT to use onnx-mlir
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging
- For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
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 (onnx/onnx-mlir) · observed Aug 4, 2026
- GitHub forks (onnx/onnx-mlir) · observed Aug 4, 2026
- Last push (onnx/onnx-mlir) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ai-serving 166 · onnx-mlir 1.0k (synced Aug 14, 2026).
Common questions
- What is the difference between ai-serving and onnx-mlir?
- ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. onnx-mlir: ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes. See the comparison table for live GitHub stats and shared categories.
- When should I choose ai-serving over onnx-mlir?
- Choose ai-serving over onnx-mlir when ai-serving is primarily Scala; onnx-mlir is C++; Tags unique to ai-serving: ai-serving, grpc, inference-server, pmml-deployment; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats.
- When should I choose onnx-mlir over ai-serving?
- Choose onnx-mlir over ai-serving when onnx-mlir is primarily C++; ai-serving is Scala; Tags unique to onnx-mlir: compiler, llvm, mlir, runtime environments; Also covers Model Training; For users needing compile-time optimization of ONNX models to improve inference performance in a variety of language runtimes such as C++, Java, and Python.
- 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 onnx-mlir?
- When quick setup and environment management are desired without using prebuilt containers, as setting up prerequisites manually may be challenging For teams primarily focused on real-time inference serving with dedicated AI hardware that requires specialized frameworks not covered by ONNX-MLIR's support matrix
- Is ai-serving or onnx-mlir more popular on GitHub?
- onnx-mlir has more GitHub stars (1,039 vs 166). Stars measure visibility, not whether either tool fits your constraints.
- Are ai-serving and onnx-mlir open source?
- Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, onnx-mlir: Apache-2.0).
- Where can I find alternatives to ai-serving or onnx-mlir?
- GraphCanon lists graph-backed alternatives at ai-serving alternatives and onnx-mlir alternatives (ai-serving markdown twin, onnx-mlir 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 onnx-mlir?
- ai-serving: Slowing. onnx-mlir: 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 onnx-mlir?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ai-serving trust report; onnx-mlir trust report.