LogRocket is session replay and product analytics software for web and mobile app teams, with AI issue detection and pricing based on sessions captured.
LogRocket lets developers and product managers watch exactly what a person did before a page broke or a signup stalled. An error alert or a falling conversion chart says that something went wrong, and a recording shows how it happened. LogRocket, Inc. builds and owns the platform, run from its Boston headquarters. Replay, errors and analytics live in one account and share the same sessions. Session Replay plays back each visit beside the logs, network activity and errors captured with it. Product Analytics adds conversion funnels, path analysis, cohort analysis, retention charts and dashboards. Its Autocapture collects events, pages and actions without code changes. Galileo AI summarizes sessions, detects issues on its own and answers questions about the product. AI Issues lists detected problems with a thumbnail of each affected session, UX Analytics draws clickmaps, heatmaps and rage-click maps, and Performance Monitoring tracks frontend speed. Frontend engineers use LogRocket to reproduce a reported bug without asking the customer for steps. Product managers use it to follow conversion, feature adoption and the paths behind both. Support teams rely on Galileo AI to analyze feedback arriving through Zendesk, Intercom, Qualtrics and Gong. Organisations under security review find SOC 2, ISO 27001, FedRAMP and StateRAMP on its compliance list, plus a self-hosted deployment that keeps data inside their own network. A release review often begins when an alert fires because a key metric has fallen below its threshold. The team opens the funnel step where users left and plays the sessions behind the drop. Product managers then read retention and path reports to see whether the fix held. Teams already routing events through Segment can add LogRocket as a destination, and engineers can connect Sentry from the observability side. A vague ticket becomes a recording an engineer can step through, logs and network activity included.
Features
- Included: Funnel conversion analysis
- Included: Retention and cohort curves
- Included: User path and journey analysis
- Included: Behavioral cohorts by event history
- Included: Autocapture of clicks and pageviews
- Not included: Tracking plan with schema enforcement
- Included: Session replay attached to product events
- Not included: Warehouse-native event import and export
- Not included: Account-level group analytics
- Not included: Built-in experiments with feature flags
- Included: Alerts on product metric changes
- Included: In-app guides and surveys
- Not included: Native SDKs for web, iOS and Android
- Not included: Identity resolution across devices
- Not included: Cohort sync to marketing and ad tools
- Not included: EU data residency option
- Not included: Free tier with monthly event allowance
Additional Features
- Session Replay with logs and network activity
- Galileo AI session summaries
- AI issue detection
- AI product feedback analysis from Zendesk, Intercom, Qualtrics, G2 and Gong
- AI-powered funnel insights
- Clickmaps, heatmaps and rage-click maps
- Frontend performance monitoring
- AI Error Tracking with unlimited error events
Best for
- Frontend engineers reproducing bugs that users report
- Product managers tracking conversion and feature adoption in web and mobile apps
- Teams that want session replay, error tracking and product analytics in one tool
- Support teams analyzing ticket feedback alongside user sessions
- Organisations that must keep session data on their own infrastructure
- Buyers that require SOC 2, ISO 27001, FedRAMP or StateRAMP
Use cases
- reproducing a bug a customer reported by watching the session
- finding the step where users drop out of a signup funnel
- spotting rage clicks with clickmaps and heatmaps
- measuring retention and adoption after a feature release
- monitoring frontend performance in a production web app
- summarizing long sessions with Galileo AI
- surfacing issues users hit without reporting them
- analyzing support tickets and survey answers for recurring problems
- comparing how different user cohorts move through an app
- getting alerted when a key metric drops below a threshold
- running a targeted in-app survey and replaying the sessions behind answers
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