Interactive LogRocket Demo
LogRocket is a frontend monitoring and session replay tool for web and mobile apps. Engineering and product teams use it to replay exactly what a user did before an error, paired with performance data and analytics, so bugs and UX problems are diagnosed from real sessions.
What is LogRocket?
LogRocket is a monitoring tool that combines session replay with frontend performance and error tracking. When something goes wrong in your web or mobile app, LogRocket lets you replay the user's session, watching their clicks and navigation, while also showing the technical layer underneath: the network requests, console logs, and errors that occurred. Instead of trying to reproduce a bug from a vague report, you watch it happen and see the code-level detail at the same time.
That pairing is the point. A session replay alone shows you what the user did; LogRocket ties it to the JavaScript errors, failed network calls, and performance issues that happened during that session. So when a user hits a problem, you see both their experience and the cause in one view. It also captures Redux or other state, which helps engineers understand the exact application state when something broke.
Beyond debugging, LogRocket includes product analytics: funnels, user paths, and the ability to spot where users encounter errors or frustration signals like rage clicks. This gives product teams a view of issues affecting the experience, not just engineering. It supports web and mobile apps and integrates with error tools and issue trackers. Pricing is based on the number of sessions you record, with tiers that scale for larger teams and higher traffic.
How to get started with LogRocket
- 1
Install LogRocket in your app
Add the LogRocket SDK to your web or mobile application and initialize it with your app ID. For frontend frameworks this is a small amount of code. Once it is running, LogRocket begins recording sessions and capturing the network, console, and error data that make its replays useful.
- 2
Set up privacy masking
Before recording real users, configure masking so sensitive fields like passwords and payment details are excluded from recordings. Doing this first protects user data from the very first session. It is the responsible order of operations, rather than discovering later that something private was captured.
- 3
Connect error tracking and issue tools
Integrate LogRocket with your error monitoring and issue tracker so a captured error links straight to the session where it happened. This connection is what lets an engineer go from an error report to watching the exact user session that triggered it, which is where most of the debugging time is saved.
- 4
Identify users and capture state
Pass user identifiers so you can find sessions for a specific person, and capture application state like Redux if your app uses it. With users identified and state recorded, a replay shows not just what happened but the exact conditions in your app at the time, which often reveals the root cause.
- 5
Investigate issues and frustration signals
Use LogRocket to replay sessions tied to errors, and review funnels and frustration signals like rage clicks to find where users struggle. Connect what you see to concrete fixes, both technical bugs and confusing experiences. The tool pays off when you close that loop, turning observed problems into shipped improvements.
Who is LogRocket most useful for?
LogRocket is most useful for engineering and product teams at companies running web or mobile apps where understanding real user problems quickly matters. For a frontend engineer, the value is concrete: a bug report comes in, and instead of guessing, they replay the exact session with the network activity, console errors, and application state visible. That turns a frustrating reproduction hunt into watching the problem directly, which is the main reason teams adopt it.
It fits teams that sit at the intersection of engineering and product, because it serves both. Engineers use it to debug, while product managers use the analytics and frustration signals to find where the experience breaks down for users, not just where the code throws. A team can see that users rage-click a button that silently fails, connect that to the underlying error, and fix both the bug and the confusing experience. For onboarding flows that replays reveal as confusing, an interactive Supademo can guide users through the step.
It is less of a fit for teams that only need backend error monitoring, where an error-first tool covers the need more directly, or for those with strict constraints on recording user sessions. As with any session recording, privacy matters: sensitive data must be masked and consent handled appropriately. For frontend-heavy teams that want to see and fix what real users experience, though, LogRocket's combination of replay and technical detail is its strength.
Monitoring and analytics tools differ in whether they lead with backend errors, behavior analytics, or full-stack observability, so the right choice depends on where your problems surface and whether you need debugging detail, UX insight, or both.
Sentry is error-first, strong at capturing exceptions and performance issues across frontend and backend with detailed stack traces and release tracking. It casts a wider net across the stack than LogRocket's frontend focus. Many teams use Sentry for backend and error monitoring and LogRocket for full session replay, and the two are sometimes run side by side.
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FullStory combines session replay with deep digital experience analytics, aimed at larger organizations wanting rich behavioral insight. It is more analytics-and-enterprise focused, where LogRocket leans harder into the engineering side with technical debugging detail like network and state capture. Teams prioritizing broad experience analytics consider FullStory; those prioritizing frontend debugging lean to LogRocket.
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Smartlook pairs session recordings with event analytics and is notable for solid native mobile app recording. It leans toward behavior and funnel analysis across web and mobile. LogRocket leans toward engineering debugging with its technical detail, so the choice depends on whether you are mainly chasing UX behavior or technical bugs in the frontend.
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PostHog bundles product analytics, session replay, feature flags, and experimentation in an open-source-friendly platform that can be self-hosted. Teams wanting a broad analytics toolkit in one product, with replay as one feature among several, lean toward it. LogRocket goes deeper on frontend debugging detail specifically, so the decision comes down to breadth versus debugging depth.
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FAQs on LogRocket
Commonly asked questions about LogRocket. Have more? Reach out and our team will be happy to help.
What makes LogRocket different from a plain session replay tool?
A plain session replay shows what the user did. LogRocket ties that replay to the technical layer: the JavaScript errors, network requests, console logs, and application state that occurred during the session. So you see both the user's experience and the underlying cause in one view. That pairing is what lets engineers diagnose a bug from a real session instead of trying to reproduce it from a vague report.
How does LogRocket compare to Sentry?
LogRocket and Sentry lead with different strengths. Sentry is error-first, strong at capturing exceptions across the whole stack including the backend. LogRocket is frontend-first, leading with session replay tied to frontend errors, performance, and product analytics. Many teams use Sentry for backend and error monitoring and LogRocket to see the full user session, and the two are sometimes run together.
Is LogRocket for engineers or product teams?
Both, which is part of its appeal. Engineers use the session replay, network data, and captured application state to debug issues. Product teams use the analytics, funnels, and frustration signals like rage clicks to find where the user experience breaks down. A team can connect a confusing experience to the underlying technical cause, so the same tool serves debugging and product insight from different angles.
What are frustration signals?
Frustration signals are behaviors that suggest a user is struggling, such as rage clicks, where someone repeatedly clicks an element that is not responding, or sudden exits. LogRocket surfaces these and lets you jump to the sessions where they happened. They are useful because they point you to problems users hit but may never report, like a button that silently fails, so you can find and fix issues you would otherwise miss.
How does LogRocket handle privacy in recordings?
LogRocket provides controls to mask or exclude sensitive data so things like passwords, payment details, and personal information are not captured in recordings. As with any session recording tool, teams are responsible for configuring that masking and handling user consent according to the regulations that apply to them. Set up properly, it gives you the debugging and behavioral insight without recording private data, but that configuration is the team's responsibility.