--- title: "clearml vs pai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-microsoft-pai" tools: ["clearml-clearml", "microsoft-pai"] --- # clearml vs pai *GraphCanon updated Sep 29, 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 pai if pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer. [clearml](https://clear.ml/docs) reports 6.9k GitHub stars, 802 forks, and 526 open issues, last pushed Sep 23, 2026. [pai](https://openpai.readthedocs.io) has 2.7k stars, 554 forks, and 260 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [clearml's repository](https://github.com/clearml/clearml) and [pai's repository](https://github.com/microsoft/pai). | | [clearml](/tools/clearml-clearml.md) | [pai](/tools/microsoft-pai.md) | | --- | --- | --- | | Tagline | MLOps/LLMOps solution for CI/CD in AI workloads | Resource scheduling and cluster management for AI | | Stars | 6,891 | 2,694 | | Forks | 802 | 554 | | Open issues | 526 | 260 | | Language | Python | JavaScript | | 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. | pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | 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) | [pai](/tools/microsoft-pai.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 3d | 11d | | Open issues (now) | 526 | 260 | | Stars delta | +86 (30d) | +8 (30d) | | Open issues delta | -47 (30d) | -22 (30d) | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/microsoft-pai/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: pai - **Adopt for:** pai is an open-source solution focused on resource scheduling and cluster management that supports deep learning frameworks including TensorFlow, PyTorch, and Chainer. ## Choose when ### Choose clearml if… - clearml is primarily Python; pai is JavaScript. - License: clearml is Apache-2.0, pai is MIT. - 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 ### Choose pai if… - pai is primarily JavaScript; clearml is Python. - License: pai is MIT, clearml is Apache-2.0. - Tags unique to pai: artificial-intelligence, gpu, kubernetes, machine-learning. - When you are working with JavaScript-based projects and need to integrate model training or serving operations within your tech stack seamlessly ## 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 pai - For organizations that prefer a more comprehensive suite tailored for specific languages other than JavaScript, as the tool's focus is clearly on this language environment - When looking for solutions strictly hosted in cloud environments, as pai also supports deployment in on-premise settings which could complicate decisions if cloud dependency is critical ## Common questions ### What is the difference between clearml and pai? clearml: MLOps/LLMOps solution for CI/CD in AI workloads. pai: Resource scheduling and cluster management for AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose clearml over pai? Choose clearml over pai when clearml is primarily Python; pai is JavaScript; License: clearml is Apache-2.0, pai is MIT; 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. ### When should I choose pai over clearml? Choose pai over clearml when pai is primarily JavaScript; clearml is Python; License: pai is MIT, clearml is Apache-2.0; Tags unique to pai: artificial-intelligence, gpu, kubernetes, machine-learning; When you are working with JavaScript-based projects and need to integrate model training or serving operations within your tech stack seamlessly. ### 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 pai? For organizations that prefer a more comprehensive suite tailored for specific languages other than JavaScript, as the tool's focus is clearly on this language environment When looking for solutions strictly hosted in cloud environments, as pai also supports deployment in on-premise settings which could complicate decisions if cloud dependency is critical ### Is clearml or pai more popular on GitHub? clearml has more GitHub stars (6,891 vs 2,694). Stars measure visibility, not whether either tool fits your constraints. ### Are clearml and pai open source? Yes - both are open-source projects on GitHub (clearml: Apache-2.0, pai: MIT). ### Where can I find alternatives to clearml or pai? GraphCanon lists graph-backed alternatives at [clearml alternatives](/tools/clearml-clearml/alternatives) and [pai alternatives](/tools/microsoft-pai/alternatives) ([clearml markdown twin](/tools/clearml-clearml/alternatives.md), [pai markdown twin](/tools/microsoft-pai/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-microsoft-pai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, clearml or pai? clearml: Very active. pai: Active. 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 pai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [clearml trust report](/tools/clearml-clearml/trust); [pai trust report](/tools/microsoft-pai/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/_