Comparison
scalene vs Awesome-LLMOps
Verdict
Pick scalene if scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations; 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 · scalene alternatives · Awesome-LLMOps alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | scalene | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (2d since push) As of 2w · github_public_v1 | Slowing (91d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | Published findings 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
- scalene
- High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- scalene
- 13k
- Awesome-LLMOps
- 5.9k
Forks
- scalene
- 435
- Awesome-LLMOps
- 993
Open issues
- scalene
- 151
- Awesome-LLMOps
- 247
Language
- scalene
- Python
- Awesome-LLMOps
- Shell
Adopt for
- scalene
- Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations.
- 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
- scalene
- -
- Awesome-LLMOps
- -
Runtime
- scalene
- -
- Awesome-LLMOps
- -
License
- scalene
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- scalene
- Aug 1, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- scalene
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- scalene
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- scalene
- 2d
- Awesome-LLMOps
- 91d
Open issues (now)
- scalene
- 151
- Awesome-LLMOps
- 247
Stars delta
- scalene
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- scalene
- Unknown
- Awesome-LLMOps
- +66 (30d)
OSV dependency advisories
- scalene
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- scalene
- Trust report
- Awesome-LLMOps
- Trust report
Choose scalene if…
- scalene is primarily Python; Awesome-LLMOps is Shell.
- License: scalene is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to scalene: cpu-profiling, gpu-programming, memory-allocation, profiler.
- When you need precise profiling of both CPU and GPU performance in Python applications
When NOT to use scalene
- If your project does not involve Python, as Scalene is specific to this language
- Avoid if your system lacks necessary dependencies like Visual C++ Redistributable on Windows
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; scalene is Python.
- License: Awesome-LLMOps is CC0-1.0, scalene is Apache-2.0.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (plasma-umass/scalene) · observed Aug 4, 2026
- GitHub forks (plasma-umass/scalene) · observed Aug 4, 2026
- Last push (plasma-umass/scalene) · observed Aug 1, 2026
- License file (Apache-2.0) · observed Aug 4, 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: scalene 13k · Awesome-LLMOps 5.9k (synced Aug 4, 2026).
Common questions
- What is the difference between scalene and Awesome-LLMOps?
- scalene: High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization. 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 scalene over Awesome-LLMOps?
- Choose scalene over Awesome-LLMOps when scalene is primarily Python; Awesome-LLMOps is Shell; License: scalene is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to scalene: cpu-profiling, gpu-programming, memory-allocation, profiler; When you need precise profiling of both CPU and GPU performance in Python applications.
- When should I choose Awesome-LLMOps over scalene?
- Choose Awesome-LLMOps over scalene when Awesome-LLMOps is primarily Shell; scalene is Python; License: Awesome-LLMOps is CC0-1.0, scalene is Apache-2.0; 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 avoid scalene?
- If your project does not involve Python, as Scalene is specific to this language Avoid if your system lacks necessary dependencies like Visual C++ Redistributable on Windows
- 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 scalene or Awesome-LLMOps more popular on GitHub?
- scalene has more GitHub stars (13,485 vs 5,915). Stars measure visibility, not whether either tool fits your constraints.
- Are scalene and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (scalene: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to scalene or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at scalene alternatives and Awesome-LLMOps alternatives (scalene 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, scalene or Awesome-LLMOps?
- scalene: 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 scalene and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: scalene trust report; Awesome-LLMOps trust report.