--- title: "Awesome-LLMOps vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-visenger-awesome-mlops" tools: ["tensorchord-awesome-llmops", "visenger-awesome-mlops"] --- # Awesome-LLMOps vs awesome-mlops *GraphCanon updated Aug 20, 2026* ## Verdict 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; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 993 forks, and 247 open issues, last pushed May 21, 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 [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | A curated list of references for MLOps | | Stars | 5,915 | 14,127 | | Forks | 993 | 2,101 | | Open issues | 247 | 44 | | Language | Shell | - | | 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. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | - | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 91d | 621d | | Open issues (now) | 247 | 44 | | Stars delta | +28 (30d) | Unknown | | Open issues delta | +66 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## 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. ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose Awesome-LLMOps if… - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops. - Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. - More GitHub stars (14k vs 5.9k) - visibility, not fit. ## 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. ## 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 Awesome-LLMOps and awesome-mlops? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over awesome-mlops? Choose Awesome-LLMOps over awesome-mlops when Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, 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 choose awesome-mlops over Awesome-LLMOps? Choose awesome-mlops over Awesome-LLMOps when Tags unique to awesome-mlops: ai, data-science, devops, engineering; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 5.9k) - visibility, not fit. ### 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. ### 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 Awesome-LLMOps or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-LLMOps or awesome-mlops? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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/tensorchord-awesome-llmops-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, Awesome-LLMOps or awesome-mlops? Awesome-LLMOps: 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 Awesome-LLMOps and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tensorchord-awesome-llmops`](/api/graphcanon/graph?tool=tensorchord-awesome-llmops) - 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/_