--- title: "clearml vs dstack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-dstackai-dstack" tools: ["clearml-clearml", "dstackai-dstack"] --- # clearml vs dstack *GraphCanon updated Aug 3, 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 dstack if vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. [clearml](https://clear.ml/docs) reports 6.8k GitHub stars, 785 forks, and 573 open issues, last pushed Jul 27, 2026. [dstack](https://dstack.ai/docs) has 2.2k stars, 240 forks, and 61 open issues, last pushed Jul 24, 2026. Figures are from public GitHub metadata via [clearml's repository](https://github.com/clearml/clearml) and [dstack's repository](https://github.com/dstackai/dstack). | | [clearml](/tools/clearml-clearml.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Tagline | MLOps/LLMOps solution for CI/CD in AI workloads | Vendor-agnostic orchestration for AI workloads | | Stars | 6,805 | 2,192 | | Forks | 785 | 240 | | Open issues | 573 | 61 | | Language | Python | 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. | Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MPL-2.0 | | Categories | Inference & Serving, Model Training | AI Agents, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [clearml](/tools/clearml-clearml.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 7d | 0d | | Open issues (now) | 573 | 61 | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/dstackai-dstack/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: dstack - **Adopt for:** Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal. ## Choose when ### Choose clearml if… - License: clearml is Apache-2.0, dstack is MPL-2.0. - 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 dstack if… - License: dstack is MPL-2.0, clearml is Apache-2.0. - Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud. - Also covers AI Agents. - If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent ## 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 dstack - When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred - If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0) ## Common questions ### What is the difference between clearml and dstack? clearml: MLOps/LLMOps solution for CI/CD in AI workloads. dstack: Vendor-agnostic orchestration for AI workloads. See the comparison table for live GitHub stats and shared categories. ### When should I choose clearml over dstack? Choose clearml over dstack when License: clearml is Apache-2.0, dstack is MPL-2.0; 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 dstack over clearml? Choose dstack over clearml when License: dstack is MPL-2.0, clearml is Apache-2.0; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers AI Agents; If your project requires support for multiple hardware vendors such as NVIDIA, AMD, TPU, or Tenstorrent. ### 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 dstack? When sticking to single-vendor solutions where tightly integrated proprietary tools are preferred If the project strictly avoids open-source components with Mozilla Public License (MPL-2.0) ### Is clearml or dstack more popular on GitHub? clearml has more GitHub stars (6,805 vs 2,192). Stars measure visibility, not whether either tool fits your constraints. ### Are clearml and dstack open source? Yes - both are open-source projects on GitHub (clearml: Apache-2.0, dstack: MPL-2.0). ### Where can I find alternatives to clearml or dstack? GraphCanon lists graph-backed alternatives at [clearml alternatives](/tools/clearml-clearml/alternatives) and [dstack alternatives](/tools/dstackai-dstack/alternatives) ([clearml markdown twin](/tools/clearml-clearml/alternatives.md), [dstack markdown twin](/tools/dstackai-dstack/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-dstackai-dstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, clearml or dstack? clearml: Active. dstack: Very 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 dstack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [clearml trust report](/tools/clearml-clearml/trust); [dstack trust report](/tools/dstackai-dstack/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/_