Comparison
Awesome-LLMOps vs lakeFS
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 lakeFS if lakeFS provides Git-like functionality for managing versions of data in a data lake, compatible with storage solutions like S3 and Azure.
Markdown twin · Awesome-LLMOps alternatives · lakeFS alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Awesome-LLMOps | lakeFS |
|---|---|---|
| Maintenance | Slowing (91d since push) As of today · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · 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
- lakeFS
- Data version control for your data lake
Stars
- Awesome-LLMOps
- 5.9k
- lakeFS
- 5.5k
Forks
- Awesome-LLMOps
- 993
- lakeFS
- 472
Open issues
- Awesome-LLMOps
- 247
- lakeFS
- 437
Language
- Awesome-LLMOps
- Shell
- lakeFS
- Go
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.
- lakeFS
- lakeFS provides Git-like functionality for managing versions of data in a data lake, compatible with storage solutions like S3 and Azure.
Persona
- Awesome-LLMOps
- -
- lakeFS
- -
Runtime
- Awesome-LLMOps
- -
- lakeFS
- -
License
- Awesome-LLMOps
- CC0-1.0
- lakeFS
- Apache-2.0
Last pushed
- Awesome-LLMOps
- May 21, 2026
- lakeFS
- Aug 3, 2026
Categories
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
- lakeFS
- Data & Retrieval
Trust and health
Maintenance
- Awesome-LLMOps
- Slowing (36%)
- lakeFS
- Very active (96%)
Days since push
- Awesome-LLMOps
- 91d
- lakeFS
- 0d
Open issues (now)
- Awesome-LLMOps
- 247
- lakeFS
- 437
Stars delta
- Awesome-LLMOps
- +28 (30d)
- lakeFS
- Unknown
Open issues delta
- Awesome-LLMOps
- +66 (30d)
- lakeFS
- Unknown
OSV dependency advisories
- Awesome-LLMOps
- No lockfile (source not queried)
- lakeFS
- Published findings
Full report
- Awesome-LLMOps
- Trust report
- lakeFS
- Trust report
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; lakeFS is Go.
- License: Awesome-LLMOps is CC0-1.0, lakeFS 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.
Choose lakeFS if…
- lakeFS is primarily Go; Awesome-LLMOps is Shell.
- License: lakeFS is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to lakeFS: apache-spark, aws-s3, azure-blob-storage, data-engineering.
- lakeFS ships Docker support for self-hosted deployment.
- When you need version control for large-scale datasets stored in a data lake, similar to how codebases are managed with Git.
When NOT to use lakeFS
- If your use case involves managing small files or datasets that do not benefit from a Git-like history for data changes due to overhead.
- For situations where compliance requirements preclude open-source solutions or those under the Apache 2.0 license, as lakeFS may not meet these specific needs.
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 (treeverse/lakeFS) · observed Aug 3, 2026
- GitHub forks (treeverse/lakeFS) · observed Aug 3, 2026
- Last push (treeverse/lakeFS) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMOps 5.9k · lakeFS 5.5k (synced Aug 20, 2026).
Common questions
- What is the difference between Awesome-LLMOps and lakeFS?
- Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. lakeFS: Data version control for your data lake. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMOps over lakeFS?
- Choose Awesome-LLMOps over lakeFS when Awesome-LLMOps is primarily Shell; lakeFS is Go; License: Awesome-LLMOps is CC0-1.0, lakeFS 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 choose lakeFS over Awesome-LLMOps?
- Choose lakeFS over Awesome-LLMOps when lakeFS is primarily Go; Awesome-LLMOps is Shell; License: lakeFS is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to lakeFS: apache-spark, aws-s3, azure-blob-storage, data-engineering; lakeFS ships Docker support for self-hosted deployment; When you need version control for large-scale datasets stored in a data lake, similar to how codebases are managed with Git.
- 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 lakeFS?
- If your use case involves managing small files or datasets that do not benefit from a Git-like history for data changes due to overhead. For situations where compliance requirements preclude open-source solutions or those under the Apache 2.0 license, as lakeFS may not meet these specific needs.
- Is Awesome-LLMOps or lakeFS more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 5,480). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMOps and lakeFS open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, lakeFS: Apache-2.0).
- Where can I find alternatives to Awesome-LLMOps or lakeFS?
- GraphCanon lists graph-backed alternatives at Awesome-LLMOps alternatives and lakeFS alternatives (Awesome-LLMOps markdown twin, lakeFS 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 lakeFS?
- Awesome-LLMOps: Slowing. lakeFS: Very active. 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 lakeFS?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMOps trust report; lakeFS trust report.