Phoenix vs PlotNeuralNet
A data-driven comparison of two Machine learning visualization tools: popularity, development activity, licensing and installation. Updated .
PhoenixML observability in a notebook with UMAP visualizations
PlotNeuralNetLaTeX code for drawing neural network architecture diagrams.
Key differences
- PlotNeuralNet has 2.1× as many GitHub stars as Phoenix (25K vs 11.7K).
- Phoenix had more development activity over the last 12 months: 3,725 commits vs 0.
- Phoenix is licensed under Elastic-2.0; PlotNeuralNet under MIT.
- Phoenix is written primarily in Python, PlotNeuralNet in TeX.
- PlotNeuralNet is the older project: its repository was created in 2018, Phoenix's in 2022.
- PlotNeuralNet has had no commits since Nov 6, 2020.
By the numbers
- Phoenix
- PlotNeuralNet
Show data as a table
| Metric | Phoenix | PlotNeuralNet |
|---|---|---|
| GitHub stars | 11,704 | 25,009 |
| Commits, last 12 months | 3,725 | 0 |
| Contributors | 235 | 10 |
| Forks | 1,181 | 3,059 |
Development activity
Commits per month to each default branch, last 12 complete months.
- Phoenix
- PlotNeuralNet
Show data as a table
| Month | Phoenix | PlotNeuralNet |
|---|---|---|
| Oct 2025 | 235 | 0 |
| Nov 2025 | 198 | 0 |
| Dec 2025 | 156 | 0 |
| Jan 2026 | 158 | 0 |
| Feb 2026 | 587 | 0 |
| Mar 2026 | 328 | 0 |
| Apr 2026 | 289 | 0 |
| May 2026 | 287 | 0 |
| Jun 2026 | 252 | 0 |
| Jul 2026 | 463 | 0 |
| Aug 2026 | 417 | 0 |
| Sep 2026 | 355 | 0 |
Side by side
| Attribute | Phoenix | PlotNeuralNet |
|---|---|---|
| Description | ML observability in a notebook with UMAP visualizations | LaTeX code for drawing neural network architecture diagrams. |
| Category | Machine learning visualization tools | Machine learning visualization tools |
| License | Elastic-2.0 | MIT |
| Language | Python | TeX |
| Status | Active | Inactive |
| Latest release | arize-phoenix-v20.19.0 () | v1.0.0 () |
| Last commit | ||
| Repository created | ||
| Open issues | 946 | 70 |
| Install | pip install arize-phoenix | – |
Which should you choose?
Popularity and activity are only part of the story. Consider which ecosystem you work in, the chart types and interactions you need, how much data you render, and whether the license fits your project. Both tool pages list alternatives if neither is quite right.
-
Phoenix
ML observability in a notebook with UMAP visualizations
-
PlotNeuralNet
LaTeX code for drawing neural network architecture diagrams.