--- title: "awesome-mlops vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-tensorchord-awesome-llmops" tools: ["kelvins-awesome-mlops", "tensorchord-awesome-llmops"] --- # awesome-mlops vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; 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. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 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 [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | An awesome & curated list of best LLMOps tools for developers | | Stars | 5,229 | 5,915 | | Forks | 762 | 993 | | Open issues | 71 | 247 | | Language | Python | Shell | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | 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 | - | CC0-1.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | 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._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Days since push | 97d | 91d | | Open issues (now) | 71 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/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: 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 awesome-mlops if… - awesome-mlops is primarily Python; Awesome-LLMOps is Shell. - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; awesome-mlops is Python. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops. - Also covers Computer Vision, Data & Retrieval, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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 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 awesome-mlops and Awesome-LLMOps? awesome-mlops: A curated list of awesome MLOps tools.. 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 awesome-mlops over Awesome-LLMOps? Choose awesome-mlops over Awesome-LLMOps when awesome-mlops is primarily Python; Awesome-LLMOps is Shell; Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose Awesome-LLMOps over awesome-mlops? Choose Awesome-LLMOps over awesome-mlops when Awesome-LLMOps is primarily Shell; awesome-mlops is Python; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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 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 awesome-mlops or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 5,229). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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/kelvins-awesome-mlops-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, awesome-mlops or Awesome-LLMOps? awesome-mlops: 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 awesome-mlops and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/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/_