Open Source Analytics Apps
Discover open source analytics software that collects, visualizes, and interprets data. Boost decision‑making with dashboards, and predictive insights.
Apps in Analytics
Language
License
SerpBear
Analytics · Marketing
Self-hosted keyword rank tracker with Google Search Console integration, multi-provider SERP scraping, and a built-in REST API.
Fathom Lite
Analytics
A simple, self-hosted website analytics tool built with Go and Preact that lets you understand your traffic without handing data to third parties.
Litlyx
Analytics
Cookie-free, AI-powered web analytics you can self-host in 30 seconds with Docker.
Trench
Analytics · Data Engineering · Monitoring
Open-source event tracking infrastructure built on Kafka and ClickHouse that handles thousands of events per second on a single node, with full Segment API compatibility and no cookies.
Prisme Analytics
Analytics
A self-hosted, privacy-focused web analytics platform built on Go and ClickHouse, with a ~2KB cookieless tracking script and Grafana-based dashboards for users, teams, and multi-organization access.
About Analytics
Analytics software empowers businesses and developers to transform raw data into actionable insights. This category includes business intelligence dashboards, real‑time monitoring tools, data visualization libraries, event tracking platforms, and predictive analytics engines. Popular examples range from open‑source charting frameworks like Chart.js to full‑stack solutions such as Tableau, Google Analytics, and Apache Superset.
Typical use cases cover performance monitoring (e.g., server metrics or app usage), sales & marketing analysis (conversion funnels, cohort tracking), product telemetry (feature adoption, error rates), and advanced forecasting (sales predictions, churn modeling). By aggregating data from APIs, databases, logs, or third‑party services, analytics apps help teams identify trends, spot anomalies, and make data‑driven decisions faster.
For developers, integrating analytics means adding value without reinventing the wheel—whether embedding a custom dashboard, exporting metrics to a time‑series database, or leveraging machine‑learning models for predictive insights. End users benefit from clear visual reports, automated alerts, and deeper understanding of their own behavior, ultimately leading to improved performance, higher ROI, and smarter product strategies.
