Comparison
whodb vs Awesome-LLMOps
Verdict
Pick whodb if whodb offers database exploration with AI integration for multiple databases including ClickHouse, Elasticsearch, MariaDB, MongoDB, MySQL, PostgreSQL, and SQLite3; 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 · whodb alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
8views this month
vs
Trust & integrity
| Signal | whodb | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (0d 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 · Organization 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 | No lockfile (source not queried) 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
- whodb
- Where data access meets operational intelligence
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- whodb
- 5.0k
- Awesome-LLMOps
- 5.9k
Forks
- whodb
- 240
- Awesome-LLMOps
- 1.1k
Open issues
- whodb
- 32
- Awesome-LLMOps
- 317
Language
- whodb
- Go
- Awesome-LLMOps
- Shell
Adopt for
- whodb
- Whodb offers database exploration with AI integration for multiple databases including ClickHouse, Elasticsearch, MariaDB, MongoDB, MySQL, PostgreSQL, and SQLite3.
- 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
- whodb
- -
- Awesome-LLMOps
- -
Runtime
- whodb
- -
- Awesome-LLMOps
- -
License
- whodb
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- whodb
- Sep 20, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- whodb
- Data & Retrieval
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- whodb
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- whodb
- 0d
- Awesome-LLMOps
- 121d
Open issues (now)
- whodb
- 32
- Awesome-LLMOps
- 317
Open issues delta
- whodb
- +12 (30d)
- Awesome-LLMOps
- +70 (30d)
Full report
- whodb
- Trust report
- Awesome-LLMOps
- Trust report
Choose whodb if…
- whodb is primarily Go; Awesome-LLMOps is Shell.
- License: whodb is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to whodb: anthropic, clickhouse, data-analysis, data-visualization.
- Suitable for developers and small teams looking for a free production-grade tool
When NOT to use whodb
- Not recommended if your project is incompatible with Apache-2.0 licensing
- Avoid if you require per-seat pricing that Whodb does not offer across any plans
- Skipping competitors with more customized AI integrations beyond the support for tools like Ollama, Anthropic, or OpenAI
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; whodb is Go.
- License: Awesome-LLMOps is CC0-1.0, whodb is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Evaluation & Observability, 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (clidey/whodb) · observed Sep 20, 2026
- GitHub forks (clidey/whodb) · observed Sep 20, 2026
- Last push (clidey/whodb) · observed Sep 20, 2026
- License file (Apache-2.0) · 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: whodb 5.0k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
Common questions
- What is the difference between whodb and Awesome-LLMOps?
- whodb: Where data access meets operational intelligence. 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 whodb over Awesome-LLMOps?
- Choose whodb over Awesome-LLMOps when whodb is primarily Go; Awesome-LLMOps is Shell; License: whodb is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to whodb: anthropic, clickhouse, data-analysis, data-visualization; Suitable for developers and small teams looking for a free production-grade tool.
- When should I choose Awesome-LLMOps over whodb?
- Choose Awesome-LLMOps over whodb when Awesome-LLMOps is primarily Shell; whodb is Go; License: Awesome-LLMOps is CC0-1.0, whodb is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, 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 avoid whodb?
- Not recommended if your project is incompatible with Apache-2.0 licensing Avoid if you require per-seat pricing that Whodb does not offer across any plans Skipping competitors with more customized AI integrations beyond the support for tools like Ollama, Anthropic, or OpenAI
- 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 whodb or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 5,033). Stars measure visibility, not whether either tool fits your constraints.
- Are whodb and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (whodb: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to whodb or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at whodb alternatives and Awesome-LLMOps alternatives (whodb 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, whodb or Awesome-LLMOps?
- whodb: Very active. 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 whodb and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: whodb trust report; Awesome-LLMOps trust report.