Key differences

  • Phoenix and FiftyOne have a similar number of GitHub stars (11.7K vs 11.1K).
  • FiftyOne had more development activity over the last 12 months: 8,917 commits vs 3,725.
  • Phoenix is downloaded 4.7× as often on PyPI (145.1K vs 31.1K per week).
  • FiftyOne is licensed under Apache-2.0; Phoenix under Elastic-2.0.
  • FiftyOne is written primarily in TypeScript, Phoenix in Python.
  • FiftyOne is the older project: its repository was created in 2020, Phoenix's in 2022.

By the numbers

  • FiftyOne
  • Phoenix
GitHub stars
11.1K 11.7K
PyPI downloads / week
31.1K 145.1K
Commits, last 12 months
8.9K 3.7K
Contributors
170 235
Forks
833 1.2K
Show data as a table
FiftyOne vs Phoenix by the numbers
MetricFiftyOnePhoenix
GitHub stars11,14311,704
PyPI downloads / week31,050145,091
Commits, last 12 months8,9173,725
Contributors170235
Forks8331,181

Development activity

Commits per month to each default branch, last 12 complete months.

  • FiftyOne
  • Phoenix
05001K1.5K Oct 2025 Nov Dec Jan 2026 Feb Mar Apr May Jun Jul Aug SepFiftyOnePhoenix
05001K1.5K Oct 2025 Jan 2026 Apr Jul FiftyOnePhoenix
Show data as a table
FiftyOne vs Phoenix: commits per month
MonthFiftyOnePhoenix
Oct 2025430235
Nov 2025232198
Dec 2025178156
Jan 2026638158
Feb 2026698587
Mar 2026519328
Apr 2026496289
May 2026847287
Jun 20261,182252
Jul 20261,326463
Aug 20261,426417
Sep 2026945355

Side by side

AttributeFiftyOnePhoenix
DescriptionTool for visualizing, curating and evaluating computer vision datasets and models.ML observability in a notebook with UMAP visualizations
CategoryMachine learning visualization toolsMachine learning visualization tools
LicenseApache-2.0Elastic-2.0
LanguageTypeScriptPython
StatusActiveActive
Latest releasev1.22.1 ()arize-phoenix-v20.19.0 ()
Last commit
Repository created
Open issues513946
Install
pip install fiftyone
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.

  • FiftyOne

    Tool for visualizing, curating and evaluating computer vision datasets and models.

    11.1K starsTypeScriptApache-2.0

  • Phoenix

    ML observability in a notebook with UMAP visualizations

    11.7K starsPythonElastic-2.0

More comparisons