Multiplayer vs Datadog

Source: Dev.to

Introduction

Observability tools are crucial to large, complex, and scaling applications. They help engineers:

• Analyze system performance
• Investigate bugs and errors
• Resolve issues and identify points of failure

This article compares Multiplayer and Datadog, focusing on debugging and issue resolution.

Multiplayer

Multiplayer is a full‑stack session recording tool that provides engineers with complete, correlated replays, capturing everything from the frontend to the backend without requiring external integrations. It natively supports all observability stacks, giving full debugging context from day one.

Use cases

• QA engineers – Automatic step‑by‑step capture of issues the moment they occur, reducing the need for lengthy reproduction.
• Developers – Complete error tracing that is full‑stack and AI‑ready for contextual debugging.
• End‑users – Easy bug reporting and less time spent explaining issues to support engineers.

Datadog

Datadog is a monitoring and observability platform designed for performance evaluation, primarily aimed at DevOps teams. Its biggest strength is helping support teams identify performance bottlenecks and monitor system health and availability. Datadog also offers a session‑replay add‑on to its Real User Monitoring (RUM) platform, but this is a separate subscription and is geared more toward identifying web‑application trends and performance rather than fixing bugs.

Feature Comparison

We illustrate the differences with a simple example: an online banking application that shows users a “transaction failed” message while still debiting money.

Session Capture

Multiplayer allows session capture via a browser extension, in‑app widget, or automatic SDK integration. It can be self‑hosted or cloud‑hosted.

• Continuous or conditional recording can automatically flag sessions where a transaction failure occurs.
• For retrospective reports, support can request an on‑demand recording via the widget or extension.
• Self‑hosting and masking features address data residency and compliance for sensitive financial data.

Datadog session replay is a SaaS solution integrated via an SDK as part of RUM. It records all user sessions continuously. Locating a specific failure requires filtering large volumes of data by timestamps, user IDs, or error patterns.

Collaboration Across Teams

After identifying the transaction issue, support engineers need to hand off context to developers.

Multiplayer provides a shareable link to each full‑stack session recording that includes:

• Complete user actions and UI state
• Frontend errors and console messages
• Backend distributed tracing, request/response content, and headers

Support can annotate the recording, highlight the failure moment, add notes, and attach the link directly to help‑desk tickets (Jira, Zendesk, Linear, etc.).

Datadog requires multiple steps:

1. Filter sessions to locate the relevant replay.
2. Extract timestamp and user ID.
3. Open Datadog APM separately and search for matching traces.
4. Manually correlate backend activity with frontend behavior.
5. Share multiple links or compile information from different dashboards.

While RUM and APM can be linked, developers still need to hop between tools.

Debugging

Assume the transaction failure is caused by a race condition in the payment‑processing microservice, where duplicate idempotency checks fail due to insufficient database transaction isolation.

# Backend Bug: Race condition in payment processing
@transaction.atomic
def process_payment(user_id, amount, idempotency_key):
    # Check if transaction already processed
    existing = Transaction.objects.filter(
        idempotency_key=idempotency_key
    ).first()

    if existing:
        return existing  # Duplicate request

    # Race condition window here – another request might pass the check before this transaction commits

    account = Account.objects.get(user_id=user_id)
    if account.balance >= amount:
        account.balance -= amount
        account.save()

        transaction = Transaction.objects.create(
            user_id=user_id,
            amount=amount,
            idempotency_key=idempotency_key,
            status='completed'
        )
        return transaction
    else:
        raise InsufficientFundsError()

Multiplayer lets developers immediately access the full‑stack session recording, showing the exact user interaction (“Pay Bill” button click), the frontend request payload, and the corresponding backend trace. This unified view speeds up root‑cause analysis and reduces the back‑and‑forth between logs, traces, and UI recordings.

Datadog requires developers to:

• Locate the frontend replay in RUM.
• Manually open the matching trace in APM.
• Correlate timestamps and request IDs across two separate interfaces.

The additional context switches can prolong debugging time, especially for race‑condition issues that span multiple services.