Comparison
Made-With-ML vs Awesome-LLMOps
Verdict
Pick Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows; 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 · Made-With-ML alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
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Trust & integrity
| Signal | Made-With-ML | Awesome-LLMOps |
|---|---|---|
| Maintenance | Slowing (199d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- Made-With-ML
- Learn to develop, deploy and iterate on production-grade ML applications
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- Made-With-ML
- 50k
- Awesome-LLMOps
- 5.9k
Forks
- Made-With-ML
- 7.8k
- Awesome-LLMOps
- 1.1k
Open issues
- Made-With-ML
- 25
- Awesome-LLMOps
- 317
Language
- Made-With-ML
- Jupyter Notebook
- Awesome-LLMOps
- Shell
Adopt for
- Made-With-ML
- Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows.
- 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
- Made-With-ML
- -
- Awesome-LLMOps
- -
Runtime
- Made-With-ML
- -
- Awesome-LLMOps
- -
License
- Made-With-ML
- MIT
- Awesome-LLMOps
- CC0-1.0
Last pushed
- Made-With-ML
- Mar 4, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- Made-With-ML
- Developer Tools, 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
- Made-With-ML
- 199d
- Awesome-LLMOps
- 121d
Open issues (now)
- Made-With-ML
- 25
- Awesome-LLMOps
- 317
Stars delta
- Made-With-ML
- +473 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- Made-With-ML
- -1 (30d)
- Awesome-LLMOps
- +70 (30d)
Owner type
- Made-With-ML
- User
- Awesome-LLMOps
- Organization
OSV dependency advisories
- Made-With-ML
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- Made-With-ML
- Trust report
- Awesome-LLMOps
- Trust report
Choose Made-With-ML if…
- Made-With-ML is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- License: Made-With-ML is MIT, Awesome-LLMOps is CC0-1.0.
- Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided..
- Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
- Also covers Developer Tools.
- If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch.
When NOT to use Made-With-ML
- If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch.
- For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; Made-With-ML is Jupyter Notebook.
- License: Awesome-LLMOps is CC0-1.0, Made-With-ML is MIT.
- 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.
Explore
Made-With-ML trust report →Awesome-LLMOps trust report →Developer Tools category →Inference & Serving category →Model Training category →Computer Vision category →Data & Retrieval category →Evaluation & Observability 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 (GokuMohandas/Made-With-ML) · observed Sep 20, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Sep 20, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Made-With-ML 50k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between Made-With-ML and Awesome-LLMOps?
- Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. 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 Made-With-ML over Awesome-LLMOps?
- Choose Made-With-ML over Awesome-LLMOps when Made-With-ML is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: Made-With-ML is MIT, Awesome-LLMOps is CC0-1.0; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers Developer Tools; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch.
- When should I choose Awesome-LLMOps over Made-With-ML?
- Choose Awesome-LLMOps over Made-With-ML when Awesome-LLMOps is primarily Shell; Made-With-ML is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, Made-With-ML is MIT; 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 avoid Made-With-ML?
- If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support.
- 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 Made-With-ML or Awesome-LLMOps more popular on GitHub?
- Made-With-ML has more GitHub stars (49,547 vs 5,941). Stars measure visibility, not whether either tool fits your constraints.
- Are Made-With-ML and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (Made-With-ML: MIT, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to Made-With-ML or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at Made-With-ML alternatives and Awesome-LLMOps alternatives (Made-With-ML 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, Made-With-ML or Awesome-LLMOps?
- Made-With-ML: 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 Made-With-ML and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Made-With-ML trust report; Awesome-LLMOps trust report.