--- title: "evidently vs scalene" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evidentlyai-evidently-vs-plasma-umass-scalene" tools: ["evidentlyai-evidently", "plasma-umass-scalene"] --- # evidently vs scalene *GraphCanon updated Aug 7, 2026* ## Verdict Pick evidently if evidently provides comprehensive observability across a wide range of data types and metrics, particularly suited for integration with Jupyter Notebook environments; pick scalene if scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. [evidently](https://discord.gg/xZjKRaNp8b) reports 7.8k GitHub stars, 895 forks, and 295 open issues, last pushed Aug 5, 2026. [scalene](https://github.com/plasma-umass/scalene) has 13k stars, 435 forks, and 151 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [evidently's repository](https://github.com/evidentlyai/evidently) and [scalene's repository](https://github.com/plasma-umass/scalene). | | [evidently](/tools/evidentlyai-evidently.md) | [scalene](/tools/plasma-umass-scalene.md) | | --- | --- | --- | | Tagline | An open-source ML and LLM observability framework. | High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization | | Stars | 7,790 | 13,485 | | Forks | 895 | 435 | | Open issues | 295 | 151 | | Language | Jupyter Notebook | Python | | Adopt for | Evidently provides comprehensive observability across a wide range of data types and metrics, particularly suited for integration with Jupyter Notebook environments. | Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evidently](/tools/evidentlyai-evidently.md) | [scalene](/tools/plasma-umass-scalene.md) | | --- | --- | --- | | Open issues (now) | 295 | 151 | | Stars delta | +117 (30d) | Unknown | | Open issues delta | +10 (30d) | Unknown | | Full report | [trust report](/tools/evidentlyai-evidently/trust.md) | [trust report](/tools/plasma-umass-scalene/trust.md) | ## Shared compatibility - **Python**: [evidently](/tools/evidentlyai-evidently.md) - Python runtime; [scalene](/tools/plasma-umass-scalene.md) - Python runtime ## Decision facts: evidently - **Adopt for:** Evidently provides comprehensive observability across a wide range of data types and metrics, particularly suited for integration with Jupyter Notebook environments. ## Decision facts: scalene - **Adopt for:** Scalene is a high-performance CPU, GPU, and memory profiler for Python that uses AI to suggest optimizations. ## Choose when ### Choose evidently if… - evidently is primarily Jupyter Notebook; scalene is Python. - Tags unique to evidently: data-drift, data-quality, data-validation, gen-ai. - Integrating into projects using Jupyter Notebooks where detailed observability is needed ### Choose scalene if… - scalene is primarily Python; evidently is Jupyter Notebook. - 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 evidently - For developers preferring non-Jupyter based development environments - Projects needing fewer, simpler monitoring tools without extensive metric support ## 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 ## Common questions ### What is the difference between evidently and scalene? evidently: An open-source ML and LLM observability framework.. scalene: High-performance CPU, GPU, and memory profiler for Python with AI-powered optimization. See the comparison table for live GitHub stats and shared categories. ### When should I choose evidently over scalene? Choose evidently over scalene when evidently is primarily Jupyter Notebook; scalene is Python; Tags unique to evidently: data-drift, data-quality, data-validation, gen-ai; Integrating into projects using Jupyter Notebooks where detailed observability is needed. ### When should I choose scalene over evidently? Choose scalene over evidently when scalene is primarily Python; evidently is Jupyter Notebook; 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 avoid evidently? For developers preferring non-Jupyter based development environments Projects needing fewer, simpler monitoring tools without extensive metric support ### 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 ### Is evidently or scalene more popular on GitHub? scalene has more GitHub stars (13,485 vs 7,790). Stars measure visibility, not whether either tool fits your constraints. ### Are evidently and scalene open source? Yes - both are open-source projects on GitHub (evidently: Apache-2.0, scalene: Apache-2.0). ### Where can I find alternatives to evidently or scalene? GraphCanon lists graph-backed alternatives at [evidently alternatives](/tools/evidentlyai-evidently/alternatives) and [scalene alternatives](/tools/plasma-umass-scalene/alternatives) ([evidently markdown twin](/tools/evidentlyai-evidently/alternatives.md), [scalene markdown twin](/tools/plasma-umass-scalene/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/evidentlyai-evidently-vs-plasma-umass-scalene.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, evidently or scalene? evidently: Very active. scalene: 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 evidently and scalene? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evidently trust report](/tools/evidentlyai-evidently/trust); [scalene trust report](/tools/plasma-umass-scalene/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evidentlyai-evidently`](/api/graphcanon/graph?tool=evidentlyai-evidently) - 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/_