Strongest current signal
Package downloads
34.1M/30d
Tracked on PyPI
ML experiment tracking and model management platform.
Best current coverage: 34.1M downloads/30d, 2.0K dependents, and 9.3K public repos.
Lead signals
Package pull and public code usage both show up clearly for Weights & Biases.
Strongest current signal
34.1M/30d
Tracked on PyPI
2.0K
Known dependents on PyPI
11.0K
Main repository stars
6/30d
Ranked #29 in category discussion
Research Brief
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package pull and existing public code are the clearest current signals for Weights & Biases.
Package pull
34.1M/30d
Tracked package pull on PyPI
Existing public code
9.3K
Public repositories importing tracked packages
Downstream usage
2.0K
Known dependents on PyPI
Engineering activity
8
GitHub contributors active in the last 30 days
Coverage includes npm and PyPI registries, GitHub, public code import detection, developer discussion, and recent company news where available. As of April 14, 2026. Methodology
Sustainability and maintenance signals from the primary public repository.
These signals come from public code import detection and tracked hiring posts rather than registry totals alone.
Public repositories and source files importing packages tied to Weights & Biases.
Background and reference details
background, categories, funding, and tools stay collapsed until you need them.
Weights & Biases is the standard tool for ML experiment tracking. Used by OpenAI, Anthropic, and thousands of ML teams. Raised $200M+ Series C at $1.25B valuation.
Raised $250M total - DAI rank #8 (top 3%) suggests strong developer adoption relative to funding.
Daniel Gross, Nat Friedman
Felicis, Bond
Insight Partners
Trinity Ventures, Coatue
Trinity Ventures
665K PyPI(50% of company total)
Track and version datasets, models, and other files used in ML workflows for reproducibility.
665K PyPI(50% of company total)
ML experiment tracking and model management platform.
Log, visualize, and compare machine learning experiments to track model performance and development.
Version control and manage the lifecycle of machine learning models, from training to deployment.
Create interactive dashboards and shareable reports to communicate ML findings and progress.
Automate hyperparameter optimization to efficiently find the best model configurations.
Public pricing snapshots collected for Weights & Biases
Source: Company pricing pageUpdates: WeeklyNote: Extracted via automated page analysis; verify on sourceMethodology →Historical metrics for Weights & Biases
Weights & Biases: GitHub Stars up 1% (10.9K to 11.0K). Contributors up 300% (2 to 8).
| Date | PyPI Downloads | GitHub Stars | Contributors |
|---|---|---|---|
| Mar 16, 2026 | - | 10.9K | 2 |
| Mar 22, 2026 | 4.9M | - | - |
| Mar 23, 2026 | - | 10.9K | 10 |
| Mar 29, 2026 | 5.0M | - | - |
| Mar 30, 2026 | - | 10.9K | 9 |
| Apr 5, 2026 | 4.6M | - | - |
| Apr 6, 2026 | - | 11.0K | 4 |
| Apr 12, 2026 | 3.1M | - | - |
| Apr 13, 2026 | 666.4K | 11.0K | 1 |
| Apr 14, 2026 | - | 11.0K | 8 |