Developer ecosystems · Developer-tool package registry and usage analytics
Homebrew Formulae API and analytics: coverage and access
Estimate macOS developer-tool demand, compare CLI and infrastructure package traction, and identify installation trends for local-development products. High: Homebrew is a practical discovery and installation channel for macOS developer tools, CLIs, databases, and data infrastructure.
What it covers
Geography and granularity
Geography: Use the geography stated by the source; coverage can vary by dataset and release.
Granularity: Estimate macOS developer-tool demand, compare CLI and infrastructure package traction, and identify installation trends for local-development products.
Source type: Developer-tool package registry and usage analytics
Potential use
Estimate macOS developer-tool demand, compare CLI and infrastructure package traction, and identify installation trends for local-development products. High: Homebrew is a practical discovery and installation channel for macOS developer tools, CLIs, databases, and data infrastructure.
Access and cost
Public source
Public formula/cask JSON and aggregate analytics endpoints are documented and browsable without a paid data subscription.
Cost: Access may involve paid products or services; verify current pricing and licensing with the provider.
Update cadence: Release cadence was not normalized in this review; check the source's dates, update notes, and API limits before comparing periods.
- Provider page ↗
- Documentation and access details ↗Check the provider's current terms and instructions before use
Interpret carefully
Limits that affect the decision
Analytics are aggregate, time-windowed, and affected by opt-outs and Homebrew's collection design; they indicate Homebrew activity, not total software usage or unique users.
Release cadence was not normalized in this review; check the source's dates, update notes, and API limits before comparing periods.
This record summarizes source documentation; Jon Bishop did not conduct an independent audit of the underlying dataset.