Comparison
Awesome-LLMOps vs awesome-mlops
Verdict
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; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · Awesome-LLMOps alternatives · awesome-mlops alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | Awesome-LLMOps | awesome-mlops |
|---|---|---|
| Maintenance | Slowing (91d since push) As of 5d · github_public_v1 | Dormant (621d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 5d · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
- awesome-mlops
- A curated list of references for MLOps
Stars
- Awesome-LLMOps
- 5.9k
- awesome-mlops
- 14k
Forks
- Awesome-LLMOps
- 993
- awesome-mlops
- 2.1k
Open issues
- Awesome-LLMOps
- 247
- awesome-mlops
- 44
Language
- Awesome-LLMOps
- Shell
- awesome-mlops
- -
Adopt for
- 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.
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- Awesome-LLMOps
- -
- awesome-mlops
- -
Runtime
- Awesome-LLMOps
- -
- awesome-mlops
- -
License
- Awesome-LLMOps
- CC0-1.0
- awesome-mlops
- -
Last pushed
- Awesome-LLMOps
- May 21, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Maintenance
- Awesome-LLMOps
- Slowing (36%)
- awesome-mlops
- Dormant (18%)
Days since push
- Awesome-LLMOps
- 91d
- awesome-mlops
- 621d
Open issues (now)
- Awesome-LLMOps
- 247
- awesome-mlops
- 44
Stars delta
- Awesome-LLMOps
- +28 (30d)
- awesome-mlops
- Unknown
Open issues delta
- Awesome-LLMOps
- +66 (30d)
- awesome-mlops
- Unknown
Owner type
- Awesome-LLMOps
- Organization
- awesome-mlops
- User
Full report
- Awesome-LLMOps
- Trust report
- awesome-mlops
- Trust report
Choose Awesome-LLMOps if…
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops.
- 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.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, data-science, devops, engineering.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- More GitHub stars (14k vs 5.9k) - 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 (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 (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: Awesome-LLMOps 5.9k · awesome-mlops 14k (synced Aug 20, 2026).
Common questions
- What is the difference between Awesome-LLMOps and awesome-mlops?
- Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMOps over awesome-mlops?
- Choose Awesome-LLMOps over awesome-mlops when Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; 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 choose awesome-mlops over Awesome-LLMOps?
- Choose awesome-mlops over Awesome-LLMOps when Tags unique to awesome-mlops: ai, data-science, devops, engineering; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 5.9k) - visibility, not fit.
- 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.
- 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 Awesome-LLMOps or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,127 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMOps and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-LLMOps or awesome-mlops?
- GraphCanon lists graph-backed alternatives at Awesome-LLMOps alternatives and awesome-mlops alternatives (Awesome-LLMOps 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, Awesome-LLMOps or awesome-mlops?
- Awesome-LLMOps: Slowing. 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 Awesome-LLMOps and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMOps trust report; awesome-mlops trust report.