Comparison
starwhale vs Awesome-LLMOps
Verdict
Pick starwhale if starwhale is an MLOps/LLMOps platform that focuses on model management for AI and large language models through various runtime setups; 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 · starwhale alternatives · Awesome-LLMOps alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | starwhale | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (591d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 5d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 5d · 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
- starwhale
- an MLOps/LLMOps platform
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- starwhale
- 237
- Awesome-LLMOps
- 5.9k
Forks
- starwhale
- 38
- Awesome-LLMOps
- 993
Open issues
- starwhale
- 120
- Awesome-LLMOps
- 247
Language
- starwhale
- Java
- Awesome-LLMOps
- Shell
Adopt for
- starwhale
- Starwhale is an MLOps/LLMOps platform that focuses on model management for AI and large language models through various runtime setups.
- 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
- starwhale
- -
- Awesome-LLMOps
- -
Runtime
- starwhale
- -
- Awesome-LLMOps
- -
License
- starwhale
- Starwhale uses the Apache License 2.0, which is permissive and allows for usage in both open source and commercial applications with attribution.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- starwhale
- Dec 20, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- starwhale
- Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- starwhale
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- starwhale
- 591d
- Awesome-LLMOps
- 91d
Open issues (now)
- starwhale
- 120
- Awesome-LLMOps
- 247
Stars delta
- starwhale
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- starwhale
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- starwhale
- Trust report
- Awesome-LLMOps
- Trust report
Choose starwhale if…
- starwhale is primarily Java; Awesome-LLMOps is Shell.
- License: starwhale is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Requirements: Requires Docker; Supports runtime builds through Docker images for compatibility across various system environments..
- Tags unique to starwhale: ai, cloud-native, dataset, datastore.
- When the need arises to manage models across different runtimes, including local environment configurations via runtime.yaml or conda environments, Docker images, or shell commands.
When NOT to use starwhale
- In scenarios where an exclusive user preference leans towards Python-based operations over Java and the tool's CLI interactions do not meet operational demands.
- For teams that require real-time model serving and have strict latency requirements, as Starwhale may not optimize for such use cases beyond its MLOps capabilities.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; starwhale is Java.
- License: Awesome-LLMOps is CC0-1.0, starwhale is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, 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 (star-whale/starwhale) · observed Aug 3, 2026
- GitHub forks (star-whale/starwhale) · observed Aug 3, 2026
- Last push (star-whale/starwhale) · observed Dec 20, 2024
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: starwhale 237 · Awesome-LLMOps 5.9k (synced Aug 3, 2026).
Common questions
- What is the difference between starwhale and Awesome-LLMOps?
- starwhale: an MLOps/LLMOps platform. 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 starwhale over Awesome-LLMOps?
- Choose starwhale over Awesome-LLMOps when starwhale is primarily Java; Awesome-LLMOps is Shell; License: starwhale is Apache-2.0, Awesome-LLMOps is CC0-1.0; Requirements: Requires Docker; Supports runtime builds through Docker images for compatibility across various system environments.; Tags unique to starwhale: ai, cloud-native, dataset, datastore; When the need arises to manage models across different runtimes, including local environment configurations via runtime.yaml or conda environments, Docker images, or shell commands.
- When should I choose Awesome-LLMOps over starwhale?
- Choose Awesome-LLMOps over starwhale when Awesome-LLMOps is primarily Shell; starwhale is Java; License: Awesome-LLMOps is CC0-1.0, starwhale is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid starwhale?
- In scenarios where an exclusive user preference leans towards Python-based operations over Java and the tool's CLI interactions do not meet operational demands. For teams that require real-time model serving and have strict latency requirements, as Starwhale may not optimize for such use cases beyond its MLOps capabilities.
- 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 starwhale or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 237). Stars measure visibility, not whether either tool fits your constraints.
- Are starwhale and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (starwhale: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to starwhale or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at starwhale alternatives and Awesome-LLMOps alternatives (starwhale 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, starwhale or Awesome-LLMOps?
- starwhale: Dormant. 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 starwhale and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: starwhale trust report; Awesome-LLMOps trust report.