--- title: "ai-serving vs onnx-mlir" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-onnx-onnx-mlir" tools: ["autodeployai-ai-serving", "onnx-onnx-mlir"] --- # ai-serving vs onnx-mlir *GraphCanon updated Aug 14, 2026* ## 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. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [onnx-mlir](https://github.com/onnx/onnx-mlir) has 1.0k stars, 447 forks, and 352 open issues, last pushed Jul 31, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [onnx-mlir's repository](https://github.com/onnx/onnx-mlir). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [onnx-mlir](/tools/onnx-onnx-mlir.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | ONNX model compiler technology lowering ONNX graphs to MLIR and LLVM bytecodes | | Stars | 166 | 1,039 | | Forks | 31 | 447 | | Open issues | 3 | 352 | | Language | Scala | C++ | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms. | | Categories | Inference & Serving | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [onnx-mlir](/tools/onnx-onnx-mlir.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 171d | 3d | | Open issues (now) | 3 | 352 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/onnx-onnx-mlir/trust.md) | ## Decision facts: ai-serving - **Adopt for:** Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. ## Decision facts: onnx-mlir - **Adopt for:** ONNX-MLIR is optimised for compiling ONNX models to MLIR and LLVM bytecodes, offering cross-platform support and multiple runtime environments. - **License detail:** Available under the Apache License Version 2.0 (Apache-2.0). Permissions granted for reproduction, distribution, etc., as per license terms. ## Choose when ### 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. ### 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 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 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 ## 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](/tools/autodeployai-ai-serving/alternatives) and [onnx-mlir alternatives](/tools/onnx-onnx-mlir/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [onnx-mlir markdown twin](/tools/onnx-onnx-mlir/alternatives.md)), 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](/compare/autodeployai-ai-serving-vs-onnx-onnx-mlir.md) 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](/tools/autodeployai-ai-serving/trust); [onnx-mlir trust report](/tools/onnx-onnx-mlir/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autodeployai-ai-serving`](/api/graphcanon/graph?tool=autodeployai-ai-serving) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_