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
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