--- title: "ai-serving vs seldon-core" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-seldonio-seldon-core" tools: ["autodeployai-ai-serving", "seldonio-seldon-core"] --- # ai-serving vs seldon-core *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 seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [seldon-core](https://www.seldon.io/solutions/core/) has 4.8k stars, 867 forks, and 396 open issues, last pushed Mar 23, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [seldon-core's repository](https://github.com/SeldonIO/seldon-core). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | | Stars | 166 | 4,765 | | Forks | 31 | 867 | | Open issues | 3 | 396 | | Language | Scala | Go | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | SeldonIO/seldon-core uses The Business Source License for distribution | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Days since push | 171d | 133d | | Open issues (now) | 3 | 396 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/seldonio-seldon-core/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: seldon-core - **Requirements:** Requires Docker; Requires Docker for deployment environments - **Adopt for:** seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. - **License detail:** SeldonIO/seldon-core uses The Business Source License for distribution ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; seldon-core is Go. - License: ai-serving is Apache-2.0, seldon-core is Other. - 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. ### Choose seldon-core if… - seldon-core is primarily Go; ai-serving is Scala. - License: seldon-core is Other, ai-serving is Apache-2.0. - Requirements: Requires Docker; Requires Docker for deployment environments. - Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning-operations. - If you are deploying and serving ML models on Kubernetes clusters, seldon-core provides specialized capabilities within its MLOps framework to facilitate this. ## 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 seldon-core - Consider alternatives if you are not running your infrastructure on Kubernetes, since seldon-core is optimized for Kubernetes environments. - If compatibility or licensing concerns arise due to the Business Source License under which Seldon is distributed, explore other frameworks with more permissive licenses. ## Common questions ### What is the difference between ai-serving and seldon-core? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over seldon-core? Choose ai-serving over seldon-core when ai-serving is primarily Scala; seldon-core is Go; License: ai-serving is Apache-2.0, seldon-core is Other; 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 seldon-core over ai-serving? Choose seldon-core over ai-serving when seldon-core is primarily Go; ai-serving is Scala; License: seldon-core is Other, ai-serving is Apache-2.0; Requirements: Requires Docker; Requires Docker for deployment environments; Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning-operations; If you are deploying and serving ML models on Kubernetes clusters, seldon-core provides specialized capabilities within its MLOps framework to facilitate this. ### 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 seldon-core? Consider alternatives if you are not running your infrastructure on Kubernetes, since seldon-core is optimized for Kubernetes environments. If compatibility or licensing concerns arise due to the Business Source License under which Seldon is distributed, explore other frameworks with more permissive licenses. ### Is ai-serving or seldon-core more popular on GitHub? seldon-core has more GitHub stars (4,765 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and seldon-core open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, seldon-core: Other). ### Where can I find alternatives to ai-serving or seldon-core? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [seldon-core markdown twin](/tools/seldonio-seldon-core/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-seldonio-seldon-core.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or seldon-core? ai-serving: Slowing. seldon-core: Slowing. 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 seldon-core? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [seldon-core trust report](/tools/seldonio-seldon-core/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/_