DEV Community: Rohan Rajpal

Multiplayer vs Datadog

Rohan Rajpal — Fri, 12 Dec 2025 02:49:01 +0000

Observability tools are crucial to large, complex and scaling applications. They help engineers complete the following tasks with more precision and efficiency:

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

This article will compare 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. Multiplayer natively supports all observability stacks, giving you full debugging context from day one. Multiplayer supports these use cases:
QA engineers: Automatic step-by-step capture of issues, the moment they occur, limiting the need for elongated reproduction and investigation.
Developers: Complete error tracing that is both full-stack and AI-ready, for contextual debugging.
End-users: Easy bug reporting and less time spent on explaining issues to support engineers.

Datadog

Datadog is a monitoring and observability platform designed for performance evaluation, and primarily aimed at DevOps teams. Datadog’s biggest strength is helping support teams identify performance bottlenecks and keep an eye on system health and availability. Datadog also provides a session replay addon to their Real User Monitoring (RUM) platform, however, this is a separate platform that requires a different subscription and is more geared towards identifying web application trends, bottlenecks, and performance, as opposed to fixing bugs.

Feature comparison

Let’s analyze Multiplayer and Datadog’s features by using a simple example: an online banking application that is showing users that their debit transactions have failed, but debiting money nonetheless.

Session Capture

Multiplayer allows users to capture sessions through their browser extension, in-app widget, or automatic capture by integrating a library SDK. Multiplayer can be either self-hosted or cloud-hosted.

If “continuous” or “conditional” recording mode is enabled, the session would automatedly be flagged in our example of the banking application producing a transaction failure. Developers can then examine the automatically captured session data. If the issue is reported retrospectively, the support team can request a session recording via the widget or browser extension (“on-demand” recording mode). For teams handling sensitive financial data, self-hosting Multiplayer is an option to ensure full control over data residency and compliance requirements. Multiplayer offers a number of masking and data privacy features to further increase security over confidential data.

Datadog session replay is a SaaS solution integrated via an SDK as part of their Real User Monitoring (RUM) platform. It is fundamentally designed for always-on recording, capturing all user sessions. Consequently, investigating a specific reported transaction failure is a time-consuming process, as teams must filter through all recorded sessions using criteria such as timestamps, user IDs, or error patterns to locate the relevant event:

Collaboration across teams

After the transaction issue is identified, support engineers would need to escalate it to the development team along with context for investigation.

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 directly on the session recording to highlight the exact moment the transaction failed, add notes about what the user reported, and include context about their account status. They can then send a link to the developers directly or add it to their help desk ticket (Jira, Zendesk, Linear, etc.).

Locating the relevant session replay using Datadog first requires filtering potentially a large number of sessions. Once found, a link to the frontend replay can be shared with developers. However, providing the necessary backend context involves additional steps:
Extract the timestamp and user ID from the replay
Open Datadog's APM (Application Performance Monitoring) separately (if available)
Search for traces matching that timeframe
Manually correlate backend activity with frontend behavior
Share multiple links or compile information from different dashboards

RUM and APM can be linked, in the form of links to the other platform on the respective explorer, but this still involves developers hopping between tools.

Debugging

Let's assume the transaction failure is caused by a race condition in the payment processing microservice, where duplicate idempotency checks are failing due to insufficient database transaction isolation. The below logic creates a race condition where simultaneous requests using the same idempotency key could both bypass the duplicate check. This vulnerability exists in the gap between verifying an existing transaction and initiating a new one, potentially leading to the user being double-charged:

# 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 allows developers to immediately access the full-stack session recording:

The session automatically captures the user's "Pay Bill" button click
Frontend network request shows the POST to /api/payments with the idempotency key in headers
Backend trace shows two spans for the same idempotency key, occurring within milliseconds
Database queries reveal both transactions checking for duplicates and both finding none
Request/response content shows the second request returning success while the first was still processing

Furthermore, with Multiplayer's MCP (Model Context Protocol) server, developers can feed this complete session context directly to AI coding assistants like Cursor or Claude Code, asking "What caused this duplicate transaction?" and getting an immediate analysis based on the full stack trace.

Developers can auto-generate test scripts from the session, using Multiplayer, to ensure this race condition doesn't reoccur, capturing the exact sequence of events that triggered the bug.

Datadog would require a more manual investigation process:

Find the session replay showing the frontend "Transaction failed" error
Note the timestamp and user ID
Switch to Datadog APM to search for traces around that timestamp
Manually filter traces by service name and endpoint
Notice multiple traces but need to manually check each one
Examine application logs using user ID and correlate with trace IDs
Piece together the timeline across multiple dashboards

While Datadog's monitoring would potentially show aggregate metrics indicating increased payment errors, investigating the specific root cause requires significant tool-hopping and manual correlation. The developer might spend 30-45 minutes just gathering all the necessary context before even beginning the fix.
We can fix the race condition by using database-level locking:

# Backend: Fix race condition with proper locking
@transaction.atomic
def process_payment(user_id, amount, idempotency_key):
    # Use select_for_update with nowait to prevent race conditions
    try:
        existing = Transaction.objects.select_for_update(
            nowait=True
        ).filter(
            idempotency_key=idempotency_key
        ).first()

        if existing:
            return existing  # Duplicate request

    except DatabaseError:
        # Another transaction is processing this key
        raise DuplicateRequestError()

    account = Account.objects.select_for_update().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()

When to use Multiplayer:

When you need to quickly resolve specific technical support issues and bugs with full-stack context
When your support workflow involves escalations between customer success, support, and engineering teams
When you need flexible recording modes (on-demand, continuous, conditional) rather than always-on capture
When you want to integrate session context with AI coding assistants for faster debugging
When data residency, compliance, or privacy requirements make self-hosting essential

When to use Datadog:

When your primary need is comprehensive infrastructure and application performance monitoring at scale
When you need to track metrics, trends, and system-wide observability across distributed systems
When your focus is on proactive monitoring and alerting rather than reactive debugging
When DevOps teams are the primary users and deep monitoring integration is more important than support workflows
When session replay is a supplementary feature to broader monitoring needs, not the core use case

Multiplayer vs Fullstory

Rohan Rajpal — Mon, 24 Nov 2025 22:31:35 +0000

Session replay tools have become a key component of testing and debugging workflows in recent years. These tools recreate user interactions on web and mobile applications to give developers, product owners, and test engineers insights into user behaviour and provide debugging context. Multiplayer and Fullstory are two such popular tools. In this article, we’ll go through each tool and compare their capabilities to help you decide which one to pick.

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. Multiplayer natively supports all observability stacks, giving you full debugging context from day one. The tool aims to create a platform for collaboration between QA, developers and users in order to accelerate debugging and feature production. Multiplayer supports users in the following manner:

QA engineers: Automatic step-by-step capture of issues, the moment they occur, limiting the need for elongated reproduction and investigation.
Developers: Complete error tracing that is both full-stack and AI-ready, for contextual debugging.
End-users: Easy bug reporting and less time spent on explaining issues to support engineers.

Fullstory

Fullstory is a behavioral analytics and UX optimization platform that also provides session replays; however, recordings are limited to frontend data. Capturing frontend data helps QA recreate issues, developers solve frontend bugs, and marketing and UX teams investigate user behaviour. While these features are valuable, debugging with only frontend context provides developers with only a part of the puzzle, especially while trying to solve issues that span across the tech stack.
Feature Comparison

Let’s analyze Multiplayer and Fullstory’s features by using a simple example: an online e-commerce platform is trying to understand why their “add to cart” button is not adding any items to users’ carts.

Session Capture

The individual who identified the ‘add to cart’ issue could simply click on the record button and provide developers with the full context of the bug. If error-triggered recording is activated (continuous recording), the session would automatically be recorded and flagged. Teams can also decide to enable c*onditional recording* if users frequently interact with critical data and would like all sessions to be recorded.

Fullstory can be installed only via SDK and deployed as a SaaS solution. Fullstory thus captures all incoming data. The recorder would have to manually search the list of all session recordings and filter by console error or timestamp to find it again.

Collaboration across teams

After the session has been captured, support engineers would likely escalate the bug to developers, once they have confirmed that the ‘Add to cart” button is behaving incorrectly.

Multiplayer provides a link to each session recording, which contains user actions, UI state, visible errors, and system-level telemetry. This link can be included in the respective ticketing system that the firm uses (e.g., Zendesk, Jira). In addition, the support engineer can also choose to annotate over the session recording to highlight unexpected behaviour and UI issues, or add comments to specific data points (APIs, spans, traces).

Fullstory would first require a support agent to skim through its playlist of sessions or filter all sessions using an associated timestamp, user ID, or search for ‘rage clicks’, after being informed of a bug by a user.

Once they have found the session in question, they can extract a shareable link from it and include it in the development ticket. This link contains only frontend UI events and a field to enter a note in.

Debugging

Let’s assume the ‘add to cart’ button was not doing anything due to a backend cache invalidation issue, where the actual stock of the item was 0, but showing as available to users, and allowing them to click “add to cart” (without anything actually happening).

// Backend Bug: Cache not invalidated when stock changes
@Cacheable(value = "products", key = "#productId")
public Product getProduct(String productId) {
    return productRepository.findById(productId).orElse(null);
}

// When inventory updates, cache is not cleared
public void updateStock(String productId, int newStock) {
    Product product = productRepository.findById(productId).orElse(null);
    product.setStockQuantity(newStock);
    productRepository.save(product);
    // Missing: @CacheEvict to clear stale cache
}

In the above Java snippet, we see that when a stock is updated, a cache eviction strategy is missing, causing the frontend code to still believe that the product is available.

Multiplayer allows a developer to immediately find the relevant session recording, as it has been automatically captured when the error occurred. Using OpenTelemetry, when a frontend action triggers a backend call, the trace context (trace ID, span ID) is automatically propagated via HTTP headers, which can then be viewed on a unified frame by the developer. The developer would look at the stack trace and see no error with the frontend user action and associated headers, but quickly notice that the associated backend API call produces an “insufficient stock” error response message. His or her first instinct would be to investigate the inventory API calls.

Furthermore, if the developer is using an AI code assistant, they can leverage the Multiplayer MCP server to feed it with complete system context and make debugging even quicker.

Fullstory would first require a developer to skim through its playlist of sessions or filter all sessions using an associated timestamp, user ID, or search for ‘rage clicks’. He or she would then see that frontend-wise, there are no apparent issues. The developer would have to then copy the timestamp of the error and session metadata, open the backend tracing service (if any), and grep through backend logs to find the associated backend API call. While this method would still be successful, it would be time-consuming and involve frequent tool-hopping.

The root cause analysis with Multiplayer took about 3 minutes, and Fullstory took about 15. We can then add cache invalidation by simply adding the following line above the updateStock function.

// Backend: Add cache eviction when updating stock
@CacheEvict(value = "products", key = "#productId")
public void updateStock(String productId, int newStock) {
    Product product = productRepository.findById(productId).orElse(null);
    product.setStockQuantity(newStock);
    productRepository.save(product);
}

Note: When using Multiplayer, the developer can auto-generate a runnable test script from this session to ensure that the “Add to cart” button does not fail due to a stale cache.

Conclusion

When to use Multiplayer:

When you want to improve the debugging process for developers by quickly providing full stack context and reducing the time taken to conduct an RCA (Root Cause Analysis).
When your application has a complex backend system, and bugs typically span across various services.
When cross-team communication (support-development) is vital to solving user issues.
When your teams use AI coding assistants such as Cursor, Claude Code, etc.

When to use Fullstory:

When session recordings are primarily for UX analytics and to understand UI behaviour.
When your application is frontend-heavy, and backend tracing is not a priority.
When product managers and marketing teams are your primary user base for session recordings.