Multiplayer vs LogRocket: which session replay tool actually fixes bugs?

You're using LogRocket and, for "full stack" visibility you also integrate it with Datadog. A user reports a checkout error. You open the session replay, see the frontend flow, and follow the link to the backend integration... but the trace is sampled out. You see some backend data, but you're missing the actual request payload that failed. Now you're back in Datadog, manually searching for the right trace, trying to find the request content, and piecing together what actually broke.

This is the gap between frontend analytics tools with backend integrations and true full-stack debugging tools.

TL;DR

Choose Multiplayer if: You need to resolve technical issues fast, with complete, unsampled, frontend + backend context in one place, and you need to improve your debugging workflows across multiple teams (e.g. Support → Engineering).

Choose LogRocket if: You primarily need user behavior analytics and frontend monitoring.

Key difference: LogRocket captures frontend behavior with optional sampled backend data through third-party integrations. Multiplayer captures complete, unsampled full-stack sessions (frontend and backend) out of the box, with no integrations required.

Quick comparison

More resources:

• Multiplayer vs LogRocket deep dive
• Multiplayer vs other tools

The real difference: frontend + integrations vs native full-stack

LogRocket: frontend-first with partial backend visibility

LogRocket captures comprehensive frontend data: clicks, page loads, console logs, network requests. For product analytics and UX monitoring, this works well. You can also integrate with APM tools like Datadog or New Relic to link out to some backend data.

But here's the catch: the backend data is sampled, and critical debugging information is still missing. Even with integrations configured, you don't get:

• Complete, unsampled logs and traces (APM sampling means you might miss the exact data you need)
• Request/response content and headers from internal service calls

Not to mention that the backend data still lives in a separate tool. When debugging a production issue, you're forced to:

• Search LogRocket session replays for the frontend behavior
• Follow a link to switch to your APM tool for backend data (hoping it wasn’t sampled out)
• Manually correlate timestamps between systems
• Still miss critical data like full request/response content and headers from internal services

Multiplayer: complete full-stack context by default

Multiplayer captures full-stack session recordings natively, with zero sampling. Every frontend action is automatically correlated with complete backend traces, logs, and request/response data, in a single timeline.

When that checkout error happens, you see:

• The user's click
• The API request
• The unsampled backend trace showing which service failed
• The exact error message and stack trace
• Request/response content and headers from internal service calls

No sampling gaps. No tool switching. No missing data.

By leveraging OpenTelemetry, Multiplayer works with any observability platform, ensuring no vendor lock in or the need for additional tools.

Recording control: always-on vs choose-your-adventure

LogRocket: Always-on recording via SDK

LogRocket uses always-on recording through its SDK: you're recording and storing everything, whether you need it or not. This works for aggregate analytics, but creates friction for debugging:

• No granular control over when and which sessions to capture
• Can't easily capture specific user cohorts or error scenarios
• Limited to SDK installation (no browser extensions or widgets for end-users)

Multiplayer: record what you need, when you need it

Multiplayer offers three recording modes and three installation methods. It’s a choose-your-own-adventure approach that adapts to your teams’ workflows.

Recording modes:

• On-demand: Start/stop recording manually. Perfect for reproducing specific bugs.
• Continuous: Start/stop recording in the background, for your entire working session. Great for development and QA to automatically save sessions with errors and exceptions.
• Conditional: Silent capture of specific user cohorts or error conditions.

Installation methods:

• In-app widget: Let users report issues with replays attached automatically, directly from your app
• Browser extension: Quickly capture a bug, unexpected behavior, or new feature idea
• SDK / CLI Apps: Full integration for programmatic control

Real scenario: Your support team gets a vague bug description. They ask the end-user to record a full stack session replay through the in-app widget. Support is able to fully understand the problem and they can reproduce the issue in 30 seconds. It’s immediately clear what the next steps (or possible fixes) are.

Support workflows: serial handoffs vs parallel collaboration

LogRocket: Built for analytics, adapted for debugging

LogRocket's collaboration features focus on sharing and reviewing sessions:

• Share session links
• View frontend behavior
• Check integrated backend data (when available and not sampled out)

But for technical debugging, you're still doing serial handoffs:

1. Support searches and watches the replay (frontend only)
2. Support checks for backend data (might be sampled out)
3. Support escalates to Engineering with partial context
4. Engineering opens APM tool to find complete traces
5. Engineering searches for request/response content
6. Multiple rounds of back-and-forth to gather full context

Multiplayer: complete context from the start

Multiplayer is built for parallel Support ↔ Engineering workflows:

Single, sharable timeline:

• Frontend screens, user actions, backend traces, logs, request/response data, and user feedback, all correlated automatically
• Support sees the user's experience; Engineering sees the technical root cause
• Both work from the same data, at the same time

Annotations and collaboration:

• Sketch directly on recordings
• Annotate any data point in the timeline
• Create interactive sandboxes for API integration
• Link sessions directly to Zendesk, Intercom, or Jira tickets

Real scenario: Support receives a bug report via the in-app widget (with replay automatically attached). They open it, see the user's error, scroll down to see the backend trace showing a 500 error from the auth service, view the exact request that failed, annotate the failing request, and share with the backend team, all in 60 seconds. The backend team has complete context and starts fixing the issue immediately.

What you actually get per session

LogRocket captures:

• User clicks ✓
• Page navigations ✓
• DOM events ✓
• Console messages ✓
• Network requests ✓
• Backend traces (link to another tool, sampled data) ✓

Multiplayer captures:

Everything LogRocket captures, plus:

• Correlated backend logs and traces (any observability platform, unsampled) ✓
• Backend errors ✓
• Full request/response content and headers (including from internal service calls) ✓
• User feedback integrated in the timeline ✓
• Service and dependency maps ✓

Integration and deployment: flexibility matters

LogRocket:

• SDK installation only
• Requires third-party APM integration for backend data (additional vendor, additional setup)
• Proprietary AI agent working with partial context and no support for AI coding workflows beyond their platform

Multiplayer:

• Multiple installation methods (extension, widget, SDK)
• Works with any observability platform, language, framework, architecture
• MCP server for AI-native debugging in your IDE or AI assistant

For AI-forward teams: LogRocket's proprietary AI works only within their platform and has limited context. Multiplayer's MCP server feeds complete session context (frontend + unsampled backend + annotations + full request/response data) directly to Claude, Cursor, or your AI tool of choice. Ask "why did this checkout fail?" and get answers grounded in complete, unsampled session data.

Which tool should you choose

Choose Multiplayer if:

• You need to fix bugs and resolve technical issues fast
• You want complete, unsampled backend visibility without integration complexity
• Your support team regularly escalates issues to engineering
• You need full request/response content from internal services and middleware
• You want flexible recording modes (not just always-on)
• You want AI-native debugging workflows with complete context

Choose LogRocket if:

• Your primary goal is product analytics and frontend monitoring
• PM and product teams are your main users
• You're comfortable with sampled backend data and managing APM integrations
• Always-on, frontend-focused recording meets your needs

Consider both if:

• You're a large organization where user analytics and technical debugging are handled by separate teams with separate objectives

The bottom line

LogRocket is a solid user analytics platform with frontend monitoring capabilities. The APM integrations add some backend visibility, but you're still working with sampled data, missing critical information, and switching between tools to piece together what happened.

Multiplayer gives you the complete picture: frontend and backend, unsampled traces, full request/response content, all correlated automatically in a single timeline. It's session replay designed for the reality of debugging modern distributed systems, where you need complete technical context to fix issues fast.