--- title: "awesome-mlops vs seldon-core" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-seldonio-seldon-core" tools: ["kelvins-awesome-mlops", "seldonio-seldon-core"] --- # awesome-mlops vs seldon-core *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 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 [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [seldon-core's repository](https://github.com/SeldonIO/seldon-core). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | | Stars | 5,229 | 4,765 | | Forks | 762 | 867 | | Open issues | 71 | 396 | | Language | Python | Go | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. | | Persona | - | - | | Runtime | - | - | | License | - | SeldonIO/seldon-core uses The Business Source License for distribution | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Days since push | 97d | 133d | | Open issues (now) | 71 | 396 | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/seldonio-seldon-core/trust.md) | ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## 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 awesome-mlops if… - awesome-mlops is primarily Python; seldon-core is Go. - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools, Evaluation & Observability, Model Training. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose seldon-core if… - seldon-core is primarily Go; awesome-mlops is Python. - 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 awesome-mlops - In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. - Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ## 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 awesome-mlops and seldon-core? awesome-mlops: A curated list of awesome MLOps tools.. 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 awesome-mlops over seldon-core? Choose awesome-mlops over seldon-core when awesome-mlops is primarily Python; seldon-core is Go; Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Evaluation & Observability, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose seldon-core over awesome-mlops? Choose seldon-core over awesome-mlops when seldon-core is primarily Go; awesome-mlops is Python; 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 awesome-mlops? In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ### 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 awesome-mlops or seldon-core more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 4,765). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and seldon-core open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or seldon-core? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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/kelvins-awesome-mlops-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, awesome-mlops or seldon-core? awesome-mlops: 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 awesome-mlops and seldon-core? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [seldon-core trust report](/tools/seldonio-seldon-core/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelvins-awesome-mlops`](/api/graphcanon/graph?tool=kelvins-awesome-mlops) - 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/_