--- title: "seldon-core vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/seldonio-seldon-core-vs-visenger-awesome-mlops" tools: ["seldonio-seldon-core", "visenger-awesome-mlops"] --- # seldon-core vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [seldon-core](https://www.seldon.io/solutions/core/) reports 4.8k GitHub stars, 867 forks, and 396 open issues, last pushed Mar 23, 2026. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 44 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [seldon-core's repository](https://github.com/SeldonIO/seldon-core) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [seldon-core](/tools/seldonio-seldon-core.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | A curated list of references for MLOps | | Stars | 4,765 | 14,127 | | Forks | 867 | 2,101 | | Open issues | 396 | 44 | | Language | Go | - | | Adopt for | seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | SeldonIO/seldon-core uses The Business Source License for distribution | - | | Categories | Inference & Serving | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [seldon-core](/tools/seldonio-seldon-core.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 133d | 621d | | Open issues (now) | 396 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/seldonio-seldon-core/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## 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 ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose seldon-core if… - 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. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - Also covers Model Training. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ## 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. ## When NOT to use awesome-mlops - Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. - Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ## Common questions ### What is the difference between seldon-core and awesome-mlops? seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose seldon-core over awesome-mlops? Choose seldon-core over awesome-mlops when 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 choose awesome-mlops over seldon-core? Choose awesome-mlops over seldon-core when Tags unique to awesome-mlops: ai, data-science, devops, engineering; Also covers Model Training; If you need references covering online training and inference service architecture patterns, consider awesome-mlops. ### 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. ### When should I avoid awesome-mlops? Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ### Is seldon-core or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 4,765). Stars measure visibility, not whether either tool fits your constraints. ### Are seldon-core and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to seldon-core or awesome-mlops? GraphCanon lists graph-backed alternatives at [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([seldon-core markdown twin](/tools/seldonio-seldon-core/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/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/seldonio-seldon-core-vs-visenger-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, seldon-core or awesome-mlops? seldon-core: Slowing. awesome-mlops: Dormant. 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 seldon-core and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [seldon-core trust report](/tools/seldonio-seldon-core/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=seldonio-seldon-core`](/api/graphcanon/graph?tool=seldonio-seldon-core) - 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/_