Comparison
clearml vs awesome-mlops
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 awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · clearml alternatives · awesome-mlops alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | clearml | awesome-mlops |
|---|---|---|
| Maintenance | Active (7d since push) As of 2w · github_public_v1 | Dormant (621d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- awesome-mlops
- A curated list of references for MLOps
Stars
- clearml
- 6.8k
- awesome-mlops
- 14k
Forks
- clearml
- 785
- awesome-mlops
- 2.1k
Open issues
- clearml
- 573
- awesome-mlops
- 44
Language
- clearml
- Python
- awesome-mlops
- -
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.
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- clearml
- -
- awesome-mlops
- -
Runtime
- clearml
- -
- awesome-mlops
- -
License
- clearml
- Apache-2.0
- awesome-mlops
- -
Last pushed
- clearml
- Jul 27, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- clearml
- Inference & Serving, Model Training
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Maintenance
- clearml
- Active (82%)
- awesome-mlops
- Dormant (18%)
Days since push
- clearml
- 7d
- awesome-mlops
- 621d
Open issues (now)
- clearml
- 573
- awesome-mlops
- 44
Owner type
- clearml
- Organization
- awesome-mlops
- User
OSV dependency advisories
- clearml
- Published findings
- awesome-mlops
- No lockfile (source not queried)
Full report
- clearml
- Trust report
- awesome-mlops
- Trust report
Choose clearml if…
- 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
- More recently updated (last pushed Jul 27, 2026).
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 awesome-mlops if…
- Tags unique to awesome-mlops: data-science, engineering, federated-learning, machine-learning.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- More GitHub stars (14k vs 6.8k) - visibility, not fit.
When NOT to use awesome-mlops
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
- Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (clearml/clearml) · observed Aug 3, 2026
- GitHub forks (clearml/clearml) · observed Aug 3, 2026
- Last push (clearml/clearml) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (visenger/awesome-mlops) · observed Aug 4, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: clearml 6.8k · awesome-mlops 14k (synced Aug 3, 2026).
Common questions
- What is the difference between clearml and awesome-mlops?
- clearml: MLOps/LLMOps solution for CI/CD in AI workloads. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose clearml over awesome-mlops?
- Choose clearml over awesome-mlops when 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; More recently updated (last pushed Jul 27, 2026).
- When should I choose awesome-mlops over clearml?
- Choose awesome-mlops over clearml when Tags unique to awesome-mlops: data-science, engineering, federated-learning, machine-learning; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 6.8k) - visibility, not fit.
- 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 awesome-mlops?
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
- Is clearml or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,127 vs 6,805). Stars measure visibility, not whether either tool fits your constraints.
- Are clearml and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to clearml or awesome-mlops?
- GraphCanon lists graph-backed alternatives at clearml alternatives and awesome-mlops alternatives (clearml markdown twin, awesome-mlops 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 awesome-mlops?
- clearml: Active. awesome-mlops: Dormant. 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 awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: clearml trust report; awesome-mlops trust report.