Home/Compare/clearml vs dstack

Comparison

clearml vs dstack

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.

Markdown twin · clearml alternatives · dstack alternatives

GraphCanon updated 2w

clearml logo

clearml

clearml/clearml

6.8kpushed Jul 27, 2026
vs
dstack logo

dstack

dstackai/dstack

2.2kpushed Jul 24, 2026

Trust & integrity

Signalclearmldstack
Maintenance
Active (7d since push)
As of 2w · github_public_v1
Very active (0d since push)
As of 4w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 4w · github_public_v1
OSV dependency advisories
Published findings
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

clearml
MLOps/LLMOps solution for CI/CD in AI workloads
dstack
Vendor-agnostic orchestration for AI workloads

Stars

clearml
6.8k
dstack
2.2k

Forks

clearml
785
dstack
240

Open issues

clearml
573
dstack
61

Language

clearml
Python
dstack
Python

Adopt for

clearml
ClearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform.
dstack
Vendor-agnostic AI workload orchestration tool supports GPU providers like NVIDIA and AMD across cloud, Kubernetes, and bare metal.

Persona

clearml
-
dstack
-

Runtime

clearml
-
dstack
-

License

clearml
Apache-2.0
dstack
MPL-2.0

Last pushed

clearml
Jul 27, 2026
dstack
Jul 24, 2026

Categories

clearml
Inference & Serving, Model Training
dstack
AI Agents, Inference & Serving, Model Training

Trust and health

Maintenance

clearml
Active (82%)
dstack
Very active (96%)

Days since push

clearml
7d
dstack
0d

Open issues (now)

clearml
573
dstack
61

OSV dependency advisories

clearml
Published findings
dstack
No lockfile (source not queried)

Full report

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

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

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 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)

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: clearml 6.8k · dstack 2.2k (synced Aug 3, 2026).

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 and dstack alternatives (clearml markdown twin, dstack markdown twin), 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 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; dstack trust report.

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