What is PostHog?

PostHog is an open-source product analytics platform that lets you track how users interact with your website or app. It gives you charts, funnels, and heatmaps so you can see what’s working and what isn’t, all without sending data to a third-party service.

Let's break it down

  • Open-source: The code is publicly available and anyone can view, modify, or host it themselves.
  • Product analytics platform: A tool that collects data about how people use a product (like clicks, page views, or sign-ups) and turns that data into visual reports.
  • Track how users interact: Record actions such as button clicks, form submissions, or navigation steps.
  • Charts, funnels, heatmaps: Visual ways to show data - charts for trends, funnels for step-by-step conversion, heatmaps for where users click most.
  • Without sending data to a third-party: All data stays on your own servers, giving you more privacy and control.

Why does it matter?

Understanding user behavior helps you improve your product, keep customers happy, and grow revenue. With PostHog you get these insights without paying expensive SaaS fees or risking data privacy by sharing information with external vendors.

Where is it used?

  • A SaaS startup uses PostHog to see why trial users drop off before upgrading.
  • An e-commerce site tracks which product pages lead to purchases and which cause visitors to leave.
  • A mobile app team monitors in-app events to decide which new feature to prioritize.
  • An internal tool for a company’s HR portal measures how often employees use self-service features, guiding UI redesigns.

Good things about it

  • Free to use and customizable because it’s open-source.
  • Data stays on your own infrastructure, enhancing privacy and security.
  • Offers a full suite of analytics (events, funnels, cohorts, heatmaps) in one place.
  • Easy to self-host with Docker or use the hosted cloud version if you prefer.
  • Extensible with plugins and integrations for other tools (e.g., Slack, Zapier).

Not-so-good things

  • Requires technical effort to set up, maintain, and scale on your own servers.
  • The UI and feature set may feel less polished than some commercial analytics platforms.
  • Advanced statistical analysis or machine-learning insights are limited compared to specialized tools.
  • Community support is good but not guaranteed; you may need in-house expertise for troubleshooting.