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Comparison

clearml vs mlflow

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 mlflow if mLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various.

Markdown twin · clearml alternatives · mlflow alternatives

GraphCanon updated 2d

clearml logo

clearml

clearml/clearml

★ 6.8kpushed Jul 27, 2026
vs
mlflow logo

mlflow

mlflow/mlflow

★ 28kpushed Aug 20, 2026

Trust & integrity

Signalclearmlmlflow
Maintenance
Active (7d since push)
As of 2w · github_public_v1
Very active (0d since push)
As of 2d · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 2d · 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
clearml trust reportmlflow trust reportTrust methodology

Tagline

clearml
MLOps/LLMOps solution for CI/CD in AI workloads
mlflow
AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications

Stars

clearml
6.8k
mlflow
28k

Forks

clearml
785
mlflow
6.2k

Open issues

clearml
573
mlflow
2.1k

Language

clearml
Python
mlflow
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.
mlflow
MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use,

Persona

clearml
-
mlflow
-

Runtime

clearml
-
mlflow
-

License

clearml
Apache-2.0
mlflow
Apache-2.0

Last pushed

clearml
Jul 27, 2026
mlflow
Aug 20, 2026

Categories

clearml
Inference & Serving, Model Training
mlflow
Evaluation & Observability, Inference & Serving, Model Training

Trust and health

Maintenance

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

Days since push

clearml
7d
mlflow
0d

Open issues (now)

clearml
573
mlflow
2.1k

Stars delta

clearml
Unknown
mlflow
+476 (30d)

Open issues delta

clearml
Unknown
mlflow
-22 (30d)

OSV dependency advisories

clearml
Published findings
mlflow
No lockfile (source not queried)

Full report

clearml
Trust report
mlflow
Trust report

Typed relationship

clearml alternative mlflowClearML and mlflow both provide comprehensive tools for managing machine learning experiments, including tracking, orchestration, data management, and model serving. They solve similar problems in MLOps with different approaches.

Choose clearml if…

  • ClearML and mlflow both provide comprehensive tools for managing machine learning experiments, including tracking, orchestration, data management, and model serving. They solve similar problems in MLOps with different approaches.
  • 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
GitHubDetails

Choose mlflow if…

  • ClearML and mlflow both provide comprehensive tools for managing machine learning experiments, including tracking, orchestration, data management, and model serving. They solve similar problems in MLOps with different approaches.
  • Tags unique to mlflow: agentops, agents, ai-governance, evaluation.
  • Also covers Evaluation & Observability.
  • - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**.

When NOT to use mlflow

  • - Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain.
  • - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments.
GitHubDetails

Explore

clearml trust report →mlflow trust report →Inference & Serving category →Model Training category →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

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 · mlflow 28k (synced Aug 3, 2026).

Common questions

What is the difference between clearml and mlflow?
clearml: MLOps/LLMOps solution for CI/CD in AI workloads. mlflow: AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications. See the comparison table for live GitHub stats and shared categories.
When should I choose clearml over mlflow?
Choose clearml over mlflow when ClearML and mlflow both provide comprehensive tools for managing machine learning experiments, including tracking, orchestration, data management, and model serving. They solve similar problems in MLOps with different approaches; 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 mlflow over clearml?
Choose mlflow over clearml when ClearML and mlflow both provide comprehensive tools for managing machine learning experiments, including tracking, orchestration, data management, and model serving. They solve similar problems in MLOps with different approaches; Tags unique to mlflow: agentops, agents, ai-governance, evaluation; Also covers Evaluation & Observability; - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**.
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 mlflow?
- Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain. - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments.
Is clearml or mlflow more popular on GitHub?
mlflow has more GitHub stars (27,591 vs 6,805). Stars measure visibility, not whether either tool fits your constraints.
Are clearml and mlflow open source?
Yes - both are open-source projects on GitHub (clearml: Apache-2.0, mlflow: Apache-2.0).
Where can I find alternatives to clearml or mlflow?
GraphCanon lists graph-backed alternatives at clearml alternatives and mlflow alternatives (clearml markdown twin, mlflow 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 mlflow?
clearml: Active. mlflow: 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 mlflow?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: clearml trust report; mlflow trust report.

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