--- title: "awesome-open-mlops vs seldon-core" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fuzzylabs-awesome-open-mlops-vs-seldonio-seldon-core" tools: ["fuzzylabs-awesome-open-mlops", "seldonio-seldon-core"] --- # awesome-open-mlops vs seldon-core *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-open-mlops if awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs; pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. [awesome-open-mlops](https://github.com/fuzzylabs/awesome-open-mlops) reports 482 GitHub stars, 54 forks, and 6 open issues, last pushed May 19, 2025. [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-open-mlops's repository](https://github.com/fuzzylabs/awesome-open-mlops) and [seldon-core's repository](https://github.com/SeldonIO/seldon-core). | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Tagline | Model deployment and serving guide with open-source MLOps tools | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | | Stars | 482 | 4,765 | | Forks | 54 | 867 | | Open issues | 6 | 396 | | Language | - | Go | | Adopt for | awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. | seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. | | Persona | - | - | | Runtime | - | - | | License | Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. | 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._ | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [seldon-core](/tools/seldonio-seldon-core.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 442d | 133d | | Open issues (now) | 6 | 396 | | Full report | [trust report](/tools/fuzzylabs-awesome-open-mlops/trust.md) | [trust report](/tools/seldonio-seldon-core/trust.md) | ## Decision facts: awesome-open-mlops - **Hosting:** unknown - No specific details available. - **Pricing:** freemium - `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource. - **Adopt for:** awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. - **License detail:** Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. ## 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-open-mlops if… - License: awesome-open-mlops is Apache-2.0, seldon-core is Other. - No specific details available. - Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.. - Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning. - When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases ### Choose seldon-core if… - License: seldon-core is Other, awesome-open-mlops 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 awesome-open-mlops - Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects - Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required ## 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-open-mlops and seldon-core? awesome-open-mlops: Model deployment and serving guide with open-source 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-open-mlops over seldon-core? Choose awesome-open-mlops over seldon-core when License: awesome-open-mlops is Apache-2.0, seldon-core is Other; No specific details available; Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.; Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning; When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases. ### When should I choose seldon-core over awesome-open-mlops? Choose seldon-core over awesome-open-mlops when License: seldon-core is Other, awesome-open-mlops 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 awesome-open-mlops? Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required ### 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-open-mlops or seldon-core more popular on GitHub? seldon-core has more GitHub stars (4,765 vs 482). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-open-mlops and seldon-core open source? Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, seldon-core: Other). ### Where can I find alternatives to awesome-open-mlops or seldon-core? GraphCanon lists graph-backed alternatives at [awesome-open-mlops alternatives](/tools/fuzzylabs-awesome-open-mlops/alternatives) and [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) ([awesome-open-mlops markdown twin](/tools/fuzzylabs-awesome-open-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/fuzzylabs-awesome-open-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-open-mlops or seldon-core? awesome-open-mlops: Dormant. 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-open-mlops and seldon-core? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-open-mlops trust report](/tools/fuzzylabs-awesome-open-mlops/trust); [seldon-core trust report](/tools/seldonio-seldon-core/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops`](/api/graphcanon/graph?tool=fuzzylabs-awesome-open-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/_