--- title: "clearml vs mlem" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-iterative-mlem" tools: ["clearml-clearml", "iterative-mlem"] --- # clearml vs mlem *GraphCanon updated Jul 12, 2026* ## Verdict Pick clearml if ^Auto-Magical CI/CD for streamlined AI workload management$; pick mlem if mLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI. [clearml](https://clear.ml/docs) reports 6.8k GitHub stars, 782 forks, and 565 open issues, last pushed Jul 7, 2026. [mlem](https://mlem.ai) has 719 stars, 42 forks, and 131 open issues, last pushed Sep 13, 2023. Figures are from public GitHub metadata via [clearml's repository](https://github.com/clearml/clearml) and [mlem's repository](https://github.com/iterative/mlem). | | [clearml](/tools/clearml-clearml.md) | [mlem](/tools/iterative-mlem.md) | | --- | --- | --- | | Tagline | ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution | A tool to package, serve, and deploy any ML model on any platform. | | Stars | 6,770 | 719 | | Forks | 782 | 42 | | Open issues | 565 | 131 | | Language | Python | Python | | Adopt for | ^Auto-Magical CI/CD for streamlined AI workload management$ | MLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Inference & Serving, LLM Frameworks | Developer Tools, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [clearml](/tools/clearml-clearml.md) | [mlem](/tools/iterative-mlem.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 4d | 1032d | | Archived on GitHub | No | Yes | | Open issues (now) | 565 | 131 | | Security scan | 119 low (119 low) | No lockfile | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/iterative-mlem/trust.md) | ## Decision facts: clearml - **Adopt for:** ^Auto-Magical CI/CD for streamlined AI workload management$ ## Decision facts: mlem - **Adopt for:** MLEM is a Python-based tool that streamlines packaging, serving, and deploying machine learning models across different platforms via CLI. ## Choose when ### Choose clearml if… - Tags unique to clearml: ai, clearml, control, deep-learning. - Also covers AI Agents, LLM Frameworks. - - When you require a comprehensive MLOps solution that integrates experiment management, data management, pipeline orchestration, and serving in one tool. ### Choose mlem if… - Tags unique to mlem: cli, data-science, deployment, git. - Also covers Developer Tools. - Use MLEM if you are looking to deploy ML models quickly using a command-line interface (CLI), making it ideal for teams preferring script-driven integration. ## When NOT to use clearml - - If your team is already deeply committed to another MLOps platform that has extensive custom integrations built into it, moving to ClearML could involve substantial rework. - - When working in a non-Python environment since ClearML primarily supports Python. ## When NOT to use mlem - Avoid MLEM if you are working in environments where strict package dependency management is required outside Python, as it might complicate integration with non-Python native services. - If detailed manual configuration of deployment settings is a necessity for your application, consider alternatives that offer more granular control over model serving parameters and configurations. ## Common questions ### What is the difference between clearml and mlem? clearml: ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution. mlem: A tool to package, serve, and deploy any ML model on any platform.. See the comparison table for live GitHub stats and shared categories. ### When should I choose clearml over mlem? Choose clearml over mlem when Tags unique to clearml: ai, clearml, control, deep-learning; Also covers AI Agents, LLM Frameworks; - When you require a comprehensive MLOps solution that integrates experiment management, data management, pipeline orchestration, and serving in one tool. ### When should I choose mlem over clearml? Choose mlem over clearml when Tags unique to mlem: cli, data-science, deployment, git; Also covers Developer Tools; Use MLEM if you are looking to deploy ML models quickly using a command-line interface (CLI), making it ideal for teams preferring script-driven integration. ### When should I avoid clearml? - If your team is already deeply committed to another MLOps platform that has extensive custom integrations built into it, moving to ClearML could involve substantial rework. - When working in a non-Python environment since ClearML primarily supports Python. ### When should I avoid mlem? Avoid MLEM if you are working in environments where strict package dependency management is required outside Python, as it might complicate integration with non-Python native services. If detailed manual configuration of deployment settings is a necessity for your application, consider alternatives that offer more granular control over model serving parameters and configurations. ### Is clearml or mlem more popular on GitHub? clearml has more GitHub stars (6,770 vs 719). Stars measure visibility, not whether either tool fits your constraints. ### Are clearml and mlem open source? Yes - both are open-source projects on GitHub (clearml: Apache-2.0, mlem: Apache-2.0). ### Where can I find alternatives to clearml or mlem? GraphCanon lists graph-backed alternatives at [clearml alternatives](/tools/clearml-clearml/alternatives) and [mlem alternatives](/tools/iterative-mlem/alternatives) ([clearml markdown twin](/tools/clearml-clearml/alternatives.md), [mlem markdown twin](/tools/iterative-mlem/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-iterative-mlem.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, clearml or mlem? clearml: Very active. mlem: Archived. 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 mlem? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [clearml trust report](/tools/clearml-clearml/trust); [mlem trust report](/tools/iterative-mlem/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/_