--- title: "clearml vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-tensorchord-awesome-llmops" tools: ["clearml-clearml", "tensorchord-awesome-llmops"] --- # clearml vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick clearml if clearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform; 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. [clearml](https://clear.ml/docs) reports 6.8k GitHub stars, 785 forks, and 573 open issues, last pushed Jul 27, 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 [clearml's repository](https://github.com/clearml/clearml) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [clearml](/tools/clearml-clearml.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | MLOps/LLMOps solution for CI/CD in AI workloads | An awesome & curated list of best LLMOps tools for developers | | Stars | 6,805 | 5,915 | | Forks | 785 | 993 | | Open issues | 573 | 247 | | Language | Python | Shell | | Adopt for | ClearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform. | 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 | Apache-2.0 | CC0-1.0 | | Categories | 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._ | | [clearml](/tools/clearml-clearml.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 7d | 91d | | Open issues (now) | 573 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: clearml - **Adopt for:** ClearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform. ## 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 clearml if… - clearml is primarily Python; Awesome-LLMOps is Shell. - License: clearml is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to clearml: ai, clearml, control, deep-learning. - When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; clearml is Python. - License: Awesome-LLMOps is CC0-1.0, clearml is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - 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 NOT to use clearml - Avoid if you need deep support for languages other than Python since ClearML is primarily built around Python - Consider alternatives if your MLOps needs do not include a centralized orchestration platform, as ClearML emphasizes integrated solutions ## 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 clearml and Awesome-LLMOps? clearml: MLOps/LLMOps solution for CI/CD in AI workloads. 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 clearml over Awesome-LLMOps? Choose clearml over Awesome-LLMOps when clearml is primarily Python; Awesome-LLMOps is Shell; License: clearml is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to clearml: ai, clearml, control, deep-learning; When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects. ### When should I choose Awesome-LLMOps over clearml? Choose Awesome-LLMOps over clearml when Awesome-LLMOps is primarily Shell; clearml is Python; License: Awesome-LLMOps is CC0-1.0, clearml is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; 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 avoid clearml? Avoid if you need deep support for languages other than Python since ClearML is primarily built around Python Consider alternatives if your MLOps needs do not include a centralized orchestration platform, as ClearML emphasizes integrated solutions ### 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 clearml or Awesome-LLMOps more popular on GitHub? clearml has more GitHub stars (6,805 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are clearml and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (clearml: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to clearml or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [clearml alternatives](/tools/clearml-clearml/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([clearml markdown twin](/tools/clearml-clearml/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/clearml-clearml-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, clearml or Awesome-LLMOps? clearml: Active. 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 clearml and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [clearml trust report](/tools/clearml-clearml/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=clearml-clearml`](/api/graphcanon/graph?tool=clearml-clearml) - 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/_