--- title: "scalene vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/plasma-umass-scalene-vs-tensorchord-awesome-llmops" tools: ["plasma-umass-scalene", "tensorchord-awesome-llmops"] --- # scalene vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## 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. [scalene](https://github.com/plasma-umass/scalene) reports 13k GitHub stars, 435 forks, and 151 open issues, last pushed Aug 1, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [scalene's repository](https://github.com/plasma-umass/scalene) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [scalene](/tools/plasma-umass-scalene.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization | An awesome & curated list of best LLMOps tools for developers | | Stars | 13,485 | 5,915 | | Forks | 435 | 993 | | Open issues | 151 | 247 | | Language | Python | Shell | | Adopt for | Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Evaluation & Observability | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [scalene](/tools/plasma-umass-scalene.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 91d | | Open issues (now) | 151 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/plasma-umass-scalene/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: scalene - **Adopt for:** Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. ## Decision facts: Awesome-LLMOps - **Adopt for:** 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. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/plasma-umass-scalene/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([scalene markdown twin](/tools/plasma-umass-scalene/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md)), 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](/compare/plasma-umass-scalene-vs-tensorchord-awesome-llmops.md) 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](/tools/plasma-umass-scalene/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=plasma-umass-scalene`](/api/graphcanon/graph?tool=plasma-umass-scalene) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_