LaunchDarkly + Datadog
Connect LaunchDarkly and Datadog to Ship Features with Confidence
Correlate feature flag changes with application performance metrics in real time to catch issues before they reach your users.
Why integrate LaunchDarkly and Datadog?
LaunchDarkly and Datadog do two different jobs that belong together. One controls what your users see; the other tells you how your application is actually behaving. When a feature flag is toggled, infrastructure is deployed, or an experiment goes live, the effects show up almost immediately in your Datadog dashboards. Connecting the two means your teams can move faster, roll back smarter, and investigate incidents with full context around every flag change.
Automate & integrate LaunchDarkly & Datadog
Use case
Annotate Datadog Dashboards with Feature Flag Events
Every time a LaunchDarkly flag is toggled on or off, a corresponding event marker is automatically posted to Datadog. Engineers get a visual audit trail on their dashboards and can immediately tie flag changes to shifts in error budgets, latency, or user behavior — without digging through a separate tool.
Use case
Automated Feature Flag Rollback on SLO Breach
When Datadog detects that an SLO or alert threshold has been breached — elevated p99 latency, rising error rate — tray.ai triggers LaunchDarkly to automatically disable the flag associated with the most recent deployment. This closed-loop system cuts mean time to recovery by acting the moment performance degrades, without waiting for someone to notice.
Use case
Sync LaunchDarkly Audit Logs to Datadog for Compliance and Security Monitoring
Stream LaunchDarkly audit log events — flag creations, targeting rule updates, user segment changes — directly into Datadog Log Management. Security and compliance teams get centralized visibility into who changed what and when, and can build alert rules and dashboards on top of that flag governance activity.
Use case
Flag-Aware Incident Enrichment and Alerting
When Datadog fires an alert or creates an incident, tray.ai automatically fetches the current state of all active LaunchDarkly flags and appends that context to the incident record and any associated Slack or PagerDuty notifications. On-call responders know immediately which features were live at the time of the failure, which cuts root cause analysis down considerably.
Use case
Progressive Rollout Monitoring with Automated Pause
As a new feature rolls out through LaunchDarkly percentage-based targeting, tray.ai continuously monitors the Datadog metrics tied to that feature. If error rates or latency exceed defined thresholds, the automation pauses the rollout by freezing the current flag percentage and notifies the owning team — stopping a bad release before it reaches everyone.
Use case
Experiment Performance Tracking and Reporting
When LaunchDarkly experiments are created or completed, tray.ai automatically generates corresponding Datadog dashboards and monitor configurations scoped to the experiment's duration and user segments. After an experiment concludes, performance data is compiled into a structured report and sent to the relevant product and engineering stakeholders.
Use case
Environment Sync and Flag State Validation
Keep LaunchDarkly flag states consistent with expected infrastructure states in Datadog by running periodic validation workflows. When Datadog detects a deployment event or environment change, tray.ai queries LaunchDarkly to verify the correct flags are enabled for that environment and alerts teams to any mismatches that could cause unpredictable behavior.
Get started with LaunchDarkly & Datadog integration today
LaunchDarkly & Datadog Challenges
What challenges are there when working with LaunchDarkly & Datadog and how will using Tray.ai help?
Challenge
No Native Bidirectional Event Correlation Out of the Box
LaunchDarkly and Datadog each have their own event and webhook systems, but they don't exchange contextual data with one another natively. Teams end up manually cross-referencing flag change logs and metric dashboards when investigating incidents — which is slow and error-prone when things are on fire.
How Tray.ai Can Help:
tray.ai sits between both platforms, automatically routing LaunchDarkly flag events into Datadog as timestamped annotations and enriching Datadog incidents with current flag states, creating bidirectional context without any manual work.
Challenge
Complex Flag-to-Service Mapping for Automated Rollbacks
Automating rollbacks requires knowing which LaunchDarkly flag corresponds to which Datadog monitor or service. Maintaining that mapping by hand gets brittle fast, especially when teams use inconsistent naming conventions across tools.
How Tray.ai Can Help:
tray.ai workflows can implement flexible, configurable mapping logic — using tag conventions, naming patterns, or a lookup table — to dynamically resolve which flag to target based on which Datadog monitor fires, keeping rollback automation maintainable as your stack grows.
Challenge
High-Volume Flag Event Noise in Observability Pipelines
Large engineering organizations can generate hundreds of LaunchDarkly flag change events per day across multiple projects and environments. Sending all of them to Datadog indiscriminately pollutes dashboards, inflates log ingestion costs, and buries the signals that actually matter.
How Tray.ai Can Help:
tray.ai workflows include conditional logic and filter steps that let teams define exactly which flag changes — by environment, project, flag type, or severity — get forwarded to Datadog, so only high-signal events reach your observability platform.
Challenge
Managing Rollout Automation Across Multiple Environments
Feature flags in LaunchDarkly typically exist across production, staging, and development environments, each with different risk tolerances and Datadog metric baselines. A one-size-fits-all automation breaks down fast without per-environment configuration.
How Tray.ai Can Help:
tray.ai's workflow branching lets teams build environment-aware automation logic, applying different metric thresholds, notification channels, and rollback behaviors depending on the environment context in the LaunchDarkly event payload.
Challenge
Maintaining Workflow Reliability During API Rate Limits and Outages
Both LaunchDarkly and Datadog enforce API rate limits, and integrations that rely on polling or high-frequency event forwarding can hit those limits during peak activity — causing missed events, failed rollbacks, or incomplete incident enrichment at exactly the wrong moment.
How Tray.ai Can Help:
tray.ai includes built-in retry logic, exponential backoff, and error handling that gracefully manages rate limit responses from both APIs. Workflows can use queuing patterns to smooth out burst traffic and guarantee event delivery even under adverse API conditions.
Start using our pre-built LaunchDarkly & Datadog templates today
Start from scratch or use one of our pre-built LaunchDarkly & Datadog templates to quickly solve your most common use cases.
LaunchDarkly & Datadog Templates
Find pre-built LaunchDarkly & Datadog solutions for common use cases
Template
Post LaunchDarkly Flag Changes as Datadog Events
Automatically listens for any flag toggle, rule update, or targeting change in LaunchDarkly and immediately sends a structured event to Datadog, annotating your dashboards and alert timelines with precise change metadata including flag key, environment, modified by, and timestamp.
Steps:
- Trigger on LaunchDarkly webhook event for flag or rule changes
- Parse and enrich event payload with flag key, environment, and actor metadata
- Post structured event to Datadog Events API with appropriate tags and alert type
Connectors Used: LaunchDarkly, Datadog
Template
Auto-Disable LaunchDarkly Flag on Datadog Monitor Alert
Monitors a specific Datadog alert tied to a feature's performance SLO. When the monitor transitions to an ALERT state, tray.ai identifies the associated LaunchDarkly flag by tag convention and disables it, then posts a message to the team's incident channel with rollback confirmation and metric context.
Steps:
- Receive Datadog monitor webhook when alert threshold is breached
- Look up the associated LaunchDarkly flag using monitor tags or naming conventions
- Disable the flag via LaunchDarkly API and post rollback notification to Slack
Connectors Used: LaunchDarkly, Datadog
Template
Enrich Datadog Incidents with Active LaunchDarkly Flag State
When a new incident is created in Datadog, this template automatically retrieves all active and recently modified LaunchDarkly flags for the affected environment and appends them as a structured comment or metadata block on the incident, giving responders immediate context for root cause analysis.
Steps:
- Trigger when a new incident is created or updated in Datadog Incident Management
- Query LaunchDarkly for all flags active in the affected environment, filtering by recent changes
- Append formatted flag state summary to the Datadog incident as a timeline entry
Connectors Used: LaunchDarkly, Datadog
Template
Progressive Rollout Monitor with Auto-Pause Workflow
Orchestrates a safe progressive rollout by connecting LaunchDarkly percentage-based rollouts with real-time Datadog metric evaluation. As the rollout advances through defined stages, this template checks error rate and latency thresholds and pauses the rollout automatically if any guardrail metric is violated.
Steps:
- Schedule periodic metric queries to Datadog during an active LaunchDarkly rollout
- Evaluate error rate and latency metrics against configurable thresholds
- Pause LaunchDarkly rollout percentage and send alert notification if thresholds are exceeded
Connectors Used: LaunchDarkly, Datadog
Template
Stream LaunchDarkly Audit Logs to Datadog Log Management
Continuously streams LaunchDarkly audit log entries into Datadog Log Management, parsing and enriching each log with standardized fields for flag key, project, environment, user, and change type to enable searching, alerting, and compliance reporting.
Steps:
- Poll LaunchDarkly Audit Log API on a scheduled interval for new entries
- Transform and normalize each audit entry into Datadog log format with relevant tags
- Send enriched log events to Datadog Logs API and trigger alerts on sensitive change types
Connectors Used: LaunchDarkly, Datadog
Template
Auto-Provision Datadog Dashboard on LaunchDarkly Experiment Launch
When a new experiment is created and activated in LaunchDarkly, this template automatically generates a scoped Datadog dashboard pre-configured with the relevant metrics, time range, and experiment metadata, so every experiment has consistent observability coverage from day one.
Steps:
- Detect new experiment activation event from LaunchDarkly webhook
- Build a Datadog dashboard configuration using experiment name, start date, and key metrics template
- Create the dashboard via Datadog API and share the URL with the experiment owner via email or Slack
Connectors Used: LaunchDarkly, Datadog