--- title: "seldon-core vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/seldonio-seldon-core-vs-tensorchord-awesome-llmops" tools: ["seldonio-seldon-core", "tensorchord-awesome-llmops"] --- # seldon-core vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [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-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [seldon-core's repository](https://github.com/SeldonIO/seldon-core) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [seldon-core](/tools/seldonio-seldon-core.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models | An awesome & curated list of best LLMOps tools for developers | | Stars | 4,765 | 5,915 | | Forks | 867 | 993 | | Open issues | 396 | 247 | | Language | Go | Shell | | Adopt for | seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | SeldonIO/seldon-core uses The Business Source License for distribution | CC0-1.0 | | Categories | Inference & Serving | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [seldon-core](/tools/seldonio-seldon-core.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Days since push | 133d | 91d | | Open issues (now) | 396 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/seldonio-seldon-core/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/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-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose seldon-core if… - seldon-core is primarily Go; Awesome-LLMOps is Shell. - License: seldon-core is Other, Awesome-LLMOps is CC0-1.0. - 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. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; seldon-core is Go. - License: Awesome-LLMOps is CC0-1.0, seldon-core is Other. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between seldon-core and Awesome-LLMOps? seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose seldon-core over Awesome-LLMOps? Choose seldon-core over Awesome-LLMOps when seldon-core is primarily Go; Awesome-LLMOps is Shell; License: seldon-core is Other, Awesome-LLMOps is CC0-1.0; 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 choose Awesome-LLMOps over seldon-core? Choose Awesome-LLMOps over seldon-core when Awesome-LLMOps is primarily Shell; seldon-core is Go; License: Awesome-LLMOps is CC0-1.0, seldon-core is Other; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is seldon-core or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 4,765). Stars measure visibility, not whether either tool fits your constraints. ### Are seldon-core and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (seldon-core: Other, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to seldon-core or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [seldon-core alternatives](/tools/seldonio-seldon-core/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([seldon-core markdown twin](/tools/seldonio-seldon-core/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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-tensorchord-awesome-llmops.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-LLMOps? seldon-core: Slowing. Awesome-LLMOps: 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 seldon-core and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [seldon-core trust report](/tools/seldonio-seldon-core/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/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/_