Comparison
clearml vs Awesome-LLMOps
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-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · clearml alternatives · Awesome-LLMOps alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | clearml | Awesome-LLMOps |
|---|---|---|
| Maintenance | Active (7d since push) As of 2w · github_public_v1 | Slowing (91d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 1d · 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-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- clearml
- 6.8k
- Awesome-LLMOps
- 5.9k
Forks
- clearml
- 785
- Awesome-LLMOps
- 993
Open issues
- clearml
- 573
- Awesome-LLMOps
- 247
Language
- clearml
- Python
- Awesome-LLMOps
- Shell
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-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- clearml
- -
- Awesome-LLMOps
- -
Runtime
- clearml
- -
- Awesome-LLMOps
- -
License
- clearml
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- clearml
- Jul 27, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- clearml
- Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- clearml
- Active (82%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- clearml
- 7d
- Awesome-LLMOps
- 91d
Open issues (now)
- clearml
- 573
- Awesome-LLMOps
- 247
Stars delta
- clearml
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- clearml
- Unknown
- Awesome-LLMOps
- +66 (30d)
OSV dependency advisories
- clearml
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- clearml
- Trust report
- Awesome-LLMOps
- Trust report
Choose clearml if…
- clearml is primarily Python; Awesome-LLMOps is Shell.
- License: clearml is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- 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 Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; clearml is Python.
- License: Awesome-LLMOps is CC0-1.0, clearml is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
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 (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: clearml 6.8k · Awesome-LLMOps 5.9k (synced Aug 3, 2026).
Common questions
- What is the difference between clearml and Awesome-LLMOps?
- clearml: MLOps/LLMOps solution for CI/CD in AI workloads. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose clearml over Awesome-LLMOps?
- Choose clearml over Awesome-LLMOps when clearml is primarily Python; Awesome-LLMOps is Shell; License: clearml is Apache-2.0, Awesome-LLMOps is CC0-1.0; 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 Awesome-LLMOps over clearml?
- Choose Awesome-LLMOps over clearml when Awesome-LLMOps is primarily Shell; clearml is Python; License: Awesome-LLMOps is CC0-1.0, clearml is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- 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-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is clearml or Awesome-LLMOps more popular on GitHub?
- clearml has more GitHub stars (6,805 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are clearml and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (clearml: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to clearml or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at clearml alternatives and Awesome-LLMOps alternatives (clearml markdown twin, Awesome-LLMOps 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-LLMOps?
- clearml: Active. Awesome-LLMOps: Slowing. 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-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: clearml trust report; Awesome-LLMOps trust report.