Comparison
awesome-open-mlops vs Awesome-LLMOps
Verdict
Pick awesome-open-mlops if awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs; 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-open-mlops alternatives · Awesome-LLMOps alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | awesome-open-mlops | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (442d since push) As of 2w · github_public_v1 | Slowing (91d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 4d · 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-open-mlops
- Model deployment and serving guide with open-source MLOps tools
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- awesome-open-mlops
- 482
- Awesome-LLMOps
- 5.9k
Forks
- awesome-open-mlops
- 54
- Awesome-LLMOps
- 993
Open issues
- awesome-open-mlops
- 6
- Awesome-LLMOps
- 247
Language
- awesome-open-mlops
- -
- Awesome-LLMOps
- Shell
Adopt for
- awesome-open-mlops
- awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs.
- 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-open-mlops
- -
- Awesome-LLMOps
- -
Runtime
- awesome-open-mlops
- -
- Awesome-LLMOps
- -
License
- awesome-open-mlops
- Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- awesome-open-mlops
- May 19, 2025
- Awesome-LLMOps
- May 21, 2026
Categories
- awesome-open-mlops
- Inference & Serving
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- awesome-open-mlops
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- awesome-open-mlops
- 442d
- Awesome-LLMOps
- 91d
Open issues (now)
- awesome-open-mlops
- 6
- Awesome-LLMOps
- 247
Stars delta
- awesome-open-mlops
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- awesome-open-mlops
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- awesome-open-mlops
- Trust report
- Awesome-LLMOps
- Trust report
Choose awesome-open-mlops if…
- License: awesome-open-mlops is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- No specific details available.
- Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource..
- Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning.
- When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases
When NOT to use awesome-open-mlops
- Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects
- Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required
Choose Awesome-LLMOps if…
- License: Awesome-LLMOps is CC0-1.0, awesome-open-mlops is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, 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-open-mlops trust report →Awesome-LLMOps trust report →Inference & Serving category →Computer Vision category →Data & Retrieval category →Evaluation & Observability category →LLM Frameworks category →Model Training 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 (fuzzylabs/awesome-open-mlops) · observed Aug 4, 2026
- GitHub forks (fuzzylabs/awesome-open-mlops) · observed Aug 4, 2026
- Last push (fuzzylabs/awesome-open-mlops) · observed May 19, 2025
- License file (Apache-2.0) · 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-open-mlops 482 · Awesome-LLMOps 5.9k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-open-mlops and Awesome-LLMOps?
- awesome-open-mlops: Model deployment and serving guide with open-source 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-open-mlops over Awesome-LLMOps?
- Choose awesome-open-mlops over Awesome-LLMOps when License: awesome-open-mlops is Apache-2.0, Awesome-LLMOps is CC0-1.0; No specific details available; Pricing:
awesome-open-mlopsis freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.; Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning; When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases. - When should I choose Awesome-LLMOps over awesome-open-mlops?
- Choose Awesome-LLMOps over awesome-open-mlops when License: Awesome-LLMOps is CC0-1.0, awesome-open-mlops is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, 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-open-mlops?
- Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required
- 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-open-mlops or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 482). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-open-mlops and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to awesome-open-mlops or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at awesome-open-mlops alternatives and Awesome-LLMOps alternatives (awesome-open-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-open-mlops or Awesome-LLMOps?
- awesome-open-mlops: Dormant. 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-open-mlops and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-open-mlops trust report; Awesome-LLMOps trust report.