Comparison
Awesome-LLMOps vs tiger
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 tiger if tiger is an open-source toolkit improving LLM application trustworthiness with its AI safety suite TigerArmor, embedding-RAG combo TigerRAG, and fine-tuning tool TigerTune.
Markdown twin · Awesome-LLMOps alternatives · tiger alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | Awesome-LLMOps | tiger |
|---|---|---|
| Maintenance | Slowing (91d since push) As of 5d · github_public_v1 | Dormant (996d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 5d · github_public_v1 | Not a fork · Personal account As of 1d · 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
- tiger
- Open Source LLM toolkit for trustworthy applications
Stars
- Awesome-LLMOps
- 5.9k
- tiger
- 404
Forks
- Awesome-LLMOps
- 993
- tiger
- 27
Open issues
- Awesome-LLMOps
- 247
- tiger
- 7
Language
- Awesome-LLMOps
- Shell
- tiger
- Jupyter Notebook
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.
- tiger
- Tiger is an open-source toolkit improving LLM application trustworthiness with its AI safety suite TigerArmor, embedding-RAG combo TigerRAG, and fine-tuning tool TigerTune.
Persona
- Awesome-LLMOps
- -
- tiger
- -
Runtime
- Awesome-LLMOps
- -
- tiger
- -
License
- Awesome-LLMOps
- CC0-1.0
- tiger
- Apache-2.0
Last pushed
- Awesome-LLMOps
- May 21, 2026
- tiger
- Dec 2, 2023
Categories
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- tiger
- Data & Retrieval, LLM Frameworks, Model Training
Trust and health
Maintenance
- Awesome-LLMOps
- Slowing (36%)
- tiger
- Dormant (18%)
Days since push
- Awesome-LLMOps
- 91d
- tiger
- 996d
Open issues (now)
- Awesome-LLMOps
- 247
- tiger
- 7
Stars delta
- Awesome-LLMOps
- +28 (30d)
- tiger
- 0 (30d)
Open issues delta
- Awesome-LLMOps
- +66 (30d)
- tiger
- 0 (30d)
Owner type
- Awesome-LLMOps
- Organization
- tiger
- User
Full report
- Awesome-LLMOps
- Trust report
- tiger
- Trust report
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; tiger is Jupyter Notebook.
- License: Awesome-LLMOps is CC0-1.0, tiger is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Evaluation & Observability, Inference & Serving, 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 tiger if…
- tiger is primarily Jupyter Notebook; Awesome-LLMOps is Shell.
- License: tiger is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to tiger: ai safety, classification, data-augmentation, fine-tuning.
- Projects demanding enhanced model safety and reliability in production.
When NOT to use tiger
- For teams needing a comprehensive low-level LLM framework like Hugging Face Transformers due to lack of foundational models support by Tiger.
- If priority lies with real-time model deployment automation as opposed to pre-deployment reliability checks and training enhancements.
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 (tigerlab-ai/tiger) · observed Aug 24, 2026
- GitHub forks (tigerlab-ai/tiger) · observed Aug 24, 2026
- Last push (tigerlab-ai/tiger) · observed Dec 2, 2023
- License file (Apache-2.0) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMOps 5.9k · tiger 404 (synced Aug 20, 2026).
Common questions
- What is the difference between Awesome-LLMOps and tiger?
- Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. tiger: Open Source LLM toolkit for trustworthy applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMOps over tiger?
- Choose Awesome-LLMOps over tiger when Awesome-LLMOps is primarily Shell; tiger is Jupyter Notebook; License: Awesome-LLMOps is CC0-1.0, tiger is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I choose tiger over Awesome-LLMOps?
- Choose tiger over Awesome-LLMOps when tiger is primarily Jupyter Notebook; Awesome-LLMOps is Shell; License: tiger is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to tiger: ai safety, classification, data-augmentation, fine-tuning; Projects demanding enhanced model safety and reliability in production.
- 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 tiger?
- For teams needing a comprehensive low-level LLM framework like Hugging Face Transformers due to lack of foundational models support by Tiger. If priority lies with real-time model deployment automation as opposed to pre-deployment reliability checks and training enhancements.
- Is Awesome-LLMOps or tiger more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 404). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMOps and tiger open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, tiger: Apache-2.0).
- Where can I find alternatives to Awesome-LLMOps or tiger?
- GraphCanon lists graph-backed alternatives at Awesome-LLMOps alternatives and tiger alternatives (Awesome-LLMOps markdown twin, tiger 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 tiger?
- Awesome-LLMOps: Slowing. tiger: 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 tiger?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMOps trust report; tiger trust report.