--- title: "clearml vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-visenger-awesome-mlops" tools: ["clearml-clearml", "visenger-awesome-mlops"] --- # clearml vs awesome-mlops *GraphCanon updated Aug 4, 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-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [clearml](https://clear.ml/docs) reports 6.8k GitHub stars, 785 forks, and 573 open issues, last pushed Jul 27, 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 [clearml's repository](https://github.com/clearml/clearml) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [clearml](/tools/clearml-clearml.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | MLOps/LLMOps solution for CI/CD in AI workloads | A curated list of references for MLOps | | Stars | 6,805 | 14,127 | | Forks | 785 | 2,101 | | Open issues | 573 | 44 | | Language | Python | - | | 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-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [clearml](/tools/clearml-clearml.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 7d | 621d | | Open issues (now) | 573 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/visenger-awesome-mlops/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-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose clearml if… - Tags unique to clearml: clearml, control, deep-learning, deeplearning. - When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects - More recently updated (last pushed Jul 27, 2026). ### Choose awesome-mlops if… - Tags unique to awesome-mlops: data-science, engineering, federated-learning, machine-learning. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. - More GitHub stars (14k vs 6.8k) - visibility, not fit. ## 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-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 clearml and awesome-mlops? clearml: MLOps/LLMOps solution for CI/CD in AI workloads. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose clearml over awesome-mlops? Choose clearml over awesome-mlops when Tags unique to clearml: clearml, control, deep-learning, deeplearning; When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects; More recently updated (last pushed Jul 27, 2026). ### When should I choose awesome-mlops over clearml? Choose awesome-mlops over clearml when Tags unique to awesome-mlops: data-science, engineering, federated-learning, machine-learning; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 6.8k) - visibility, not fit. ### 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-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 clearml or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 6,805). Stars measure visibility, not whether either tool fits your constraints. ### Are clearml and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to clearml or awesome-mlops? GraphCanon lists graph-backed alternatives at [clearml alternatives](/tools/clearml-clearml/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([clearml markdown twin](/tools/clearml-clearml/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/clearml-clearml-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, clearml or awesome-mlops? clearml: Active. 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 clearml and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [clearml trust report](/tools/clearml-clearml/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_