Comparison
Awesome-LLMOps vs zeno
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 zeno if zeno combines Python API with an interactive UI for evaluating ML models across various tasks.
Markdown twin · Awesome-LLMOps alternatives · zeno alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | Awesome-LLMOps | zeno |
|---|---|---|
| Maintenance | Slowing (91d since push) As of 3d · github_public_v1 | Archived (1032d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- zeno
- AI Data Management & Evaluation Platform
Stars
- Awesome-LLMOps
- 5.9k
- zeno
- 214
Forks
- Awesome-LLMOps
- 993
- zeno
- 11
Open issues
- Awesome-LLMOps
- 247
- zeno
- 45
Language
- Awesome-LLMOps
- Shell
- zeno
- Svelte
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.
- zeno
- Zeno combines Python API with an interactive UI for evaluating ML models across various tasks.
Persona
- Awesome-LLMOps
- -
- zeno
- -
Runtime
- Awesome-LLMOps
- -
- zeno
- -
License
- Awesome-LLMOps
- CC0-1.0
- zeno
- MIT
Last pushed
- Awesome-LLMOps
- May 21, 2026
- zeno
- Oct 5, 2023
Categories
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- zeno
- Evaluation & Observability
Trust and health
Maintenance
- Awesome-LLMOps
- Slowing (36%)
- zeno
- Archived (8%)
Days since push
- Awesome-LLMOps
- 91d
- zeno
- 1032d
Archived on GitHub
- Awesome-LLMOps
- No
- zeno
- Yes
Open issues (now)
- Awesome-LLMOps
- 247
- zeno
- 45
Stars delta
- Awesome-LLMOps
- +28 (30d)
- zeno
- Unknown
Open issues delta
- Awesome-LLMOps
- +66 (30d)
- zeno
- Unknown
OSV dependency advisories
- Awesome-LLMOps
- No lockfile (source not queried)
- zeno
- Published findings
Full report
- Awesome-LLMOps
- Trust report
- zeno
- Trust report
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; zeno is Svelte.
- License: Awesome-LLMOps is CC0-1.0, zeno is MIT.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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.
Choose zeno if…
- zeno is primarily Svelte; Awesome-LLMOps is Shell.
- License: zeno is MIT, Awesome-LLMOps is CC0-1.0.
- Tags unique to zeno: ai, data-science, evaluation-framework, machine-learning.
- You need to analyze model performance interactively via a user interface
When NOT to use zeno
- Your project requires real-time monitoring capabilities not provided by Zeno's evaluation framework
- If you are looking for a specialized tool tailored only to specific data types, like just images or text without the modular versatility of Zeno
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 (zeno-ml/zeno) · observed Aug 3, 2026
- GitHub forks (zeno-ml/zeno) · observed Aug 3, 2026
- Last push (zeno-ml/zeno) · observed Oct 5, 2023
- License file (MIT) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMOps 5.9k · zeno 214 (synced Aug 20, 2026).
Common questions
- What is the difference between Awesome-LLMOps and zeno?
- Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. zeno: AI Data Management & Evaluation Platform. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMOps over zeno?
- Choose Awesome-LLMOps over zeno when Awesome-LLMOps is primarily Shell; zeno is Svelte; License: Awesome-LLMOps is CC0-1.0, zeno is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 choose zeno over Awesome-LLMOps?
- Choose zeno over Awesome-LLMOps when zeno is primarily Svelte; Awesome-LLMOps is Shell; License: zeno is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to zeno: ai, data-science, evaluation-framework, machine-learning; You need to analyze model performance interactively via a user interface.
- 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 zeno?
- Your project requires real-time monitoring capabilities not provided by Zeno's evaluation framework If you are looking for a specialized tool tailored only to specific data types, like just images or text without the modular versatility of Zeno
- Is Awesome-LLMOps or zeno more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 214). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMOps and zeno open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, zeno: MIT).
- Where can I find alternatives to Awesome-LLMOps or zeno?
- GraphCanon lists graph-backed alternatives at Awesome-LLMOps alternatives and zeno alternatives (Awesome-LLMOps markdown twin, zeno 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 zeno?
- Awesome-LLMOps: Slowing. zeno: Archived. 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 zeno?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMOps trust report; zeno trust report.