Comparison
awesome-mlops vs Awesome-LLMOps
Verdict
Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; 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 · awesome-mlops alternatives · Awesome-LLMOps alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | awesome-mlops | Awesome-LLMOps |
|---|---|---|
| Maintenance | Slowing (97d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 5d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 5d · 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-mlops
- A curated list of awesome MLOps tools.
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- awesome-mlops
- 5.2k
- Awesome-LLMOps
- 5.9k
Forks
- awesome-mlops
- 762
- Awesome-LLMOps
- 993
Open issues
- awesome-mlops
- 71
- Awesome-LLMOps
- 247
Language
- awesome-mlops
- Python
- Awesome-LLMOps
- Shell
Adopt for
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
- 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
- awesome-mlops
- -
- Awesome-LLMOps
- -
Runtime
- awesome-mlops
- -
- Awesome-LLMOps
- -
License
- awesome-mlops
- -
- Awesome-LLMOps
- CC0-1.0
Last pushed
- awesome-mlops
- Apr 29, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Days since push
- awesome-mlops
- 97d
- Awesome-LLMOps
- 91d
Open issues (now)
- awesome-mlops
- 71
- Awesome-LLMOps
- 247
Stars delta
- awesome-mlops
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- awesome-mlops
- Unknown
- Awesome-LLMOps
- +66 (30d)
Owner type
- awesome-mlops
- User
- Awesome-LLMOps
- Organization
Full report
- awesome-mlops
- Trust report
- Awesome-LLMOps
- Trust report
Choose awesome-mlops if…
- awesome-mlops is primarily Python; Awesome-LLMOps is Shell.
- Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
- Also covers Developer Tools.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; awesome-mlops is Python.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops.
- Also covers Computer Vision, Data & Retrieval, 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
awesome-mlops trust report →Awesome-LLMOps trust report →Developer Tools category →Evaluation & Observability category →Inference & Serving category →Model Training category →Computer Vision category →Data & Retrieval category →LLM Frameworks category →Speech & Audio 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 (kelvins/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Aug 4, 2026
- Last push (kelvins/awesome-mlops) · observed Apr 29, 2026
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 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: awesome-mlops 5.2k · Awesome-LLMOps 5.9k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-mlops and Awesome-LLMOps?
- awesome-mlops: A curated list of awesome MLOps tools.. 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 awesome-mlops over Awesome-LLMOps?
- Choose awesome-mlops over Awesome-LLMOps when awesome-mlops is primarily Python; Awesome-LLMOps is Shell; Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I choose Awesome-LLMOps over awesome-mlops?
- Choose Awesome-LLMOps over awesome-mlops when Awesome-LLMOps is primarily Shell; awesome-mlops is Python; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, 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 awesome-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- 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 awesome-mlops or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 5,229). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-mlops and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-mlops or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at awesome-mlops alternatives and Awesome-LLMOps alternatives (awesome-mlops 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, awesome-mlops or Awesome-LLMOps?
- awesome-mlops: Slowing. 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 awesome-mlops and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-mlops trust report; Awesome-LLMOps trust report.