Connectors / Integration
Connect AWS CloudWatch and Datadog for Full-Stack Observability
Automate monitoring workflows between AWS CloudWatch and Datadog to eliminate blind spots and speed up incident response.
AWS CloudWatch + Datadog integration
AWS CloudWatch and Datadog are two of the most widely used monitoring platforms in cloud infrastructure, and teams that rely on both often struggle to correlate metrics, logs, and alerts across each system by hand. By integrating AWS CloudWatch with Datadog through tray.ai, engineering and DevOps teams can unify telemetry data, synchronize alerting pipelines, and build automated remediation workflows without writing custom glue code. Whether you're centralizing multi-cloud visibility or tightening on-call escalations, connecting these two platforms gives you a single pane of glass across your entire observability stack.
AWS CloudWatch provides deep native visibility into AWS services — capturing EC2 metrics, Lambda invocations, RDS performance data, and VPC flow logs — while Datadog excels at aggregating that data alongside metrics from hundreds of other services, with richer dashboards, anomaly detection, and collaborative incident management. Without an automated integration, teams end up manually exporting CloudWatch alarms, re-entering thresholds in Datadog, or context-switching between consoles during high-pressure incidents. Connecting the two platforms via tray.ai means CloudWatch events and metric breaches automatically trigger Datadog monitors, incidents, and alerts, so your entire on-call team is working from the same real-time data. That cuts mean time to detect (MTTD), eliminates duplicated alerting configuration, and gives SRE and DevOps teams the automation backbone to enforce consistent observability standards across all AWS environments.
Automate & integrate AWS CloudWatch + Datadog
Automating AWS CloudWatch and Datadog business processes or integrating data is made easy with Tray.ai.
Use case
Sync CloudWatch Alarms to Datadog as Monitor Alerts
When an AWS CloudWatch alarm transitions to an ALARM state — elevated CPU utilization on an EC2 instance or a Lambda error rate spike, for example — tray.ai automatically creates or updates a corresponding monitor alert in Datadog. Your Datadog incident timeline reflects the full picture of AWS infrastructure health in real time. On-call engineers no longer need to check both consoles simultaneously during an active incident.
- Eliminate manual re-entry of CloudWatch alarm states into Datadog
- Surface AWS alarms in Datadog dashboards instantly to cut mean time to detect
- Keep Datadog incident timelines accurate with native AWS alarm context
Use case
Forward CloudWatch Log Insights to Datadog Log Management
Automatically export filtered CloudWatch Logs Insights query results or log group events into Datadog's Log Management platform for enriched analysis, correlation, and long-term retention. tray.ai triggers on new log events matching specific patterns and pushes structured log entries to Datadog with custom tags and metadata attached. This connects AWS-native log storage directly to Datadog's log parsing and alerting engine.
- Centralize AWS application and infrastructure logs inside Datadog without manual exports
- Enrich forwarded logs with environment tags, service names, and team ownership metadata
- Enable cross-service log correlation in Datadog between AWS and non-AWS sources
Use case
Trigger Datadog Incidents from CloudWatch Composite Alarms
AWS CloudWatch Composite Alarms evaluate multiple underlying alarms to represent the overall health of a complex service, making them ideal triggers for Datadog incident creation. With tray.ai, a Composite Alarm breach can automatically open a Datadog incident, populate it with the contributing alarm details, assign it to the relevant service team, and post a notification to your Slack channel. No critical infrastructure event gets missed.
- Automatically escalate multi-alarm AWS events into structured Datadog incidents
- Populate incident fields with CloudWatch alarm metadata for faster triage
- Reduce alert fatigue by only creating Datadog incidents for compound, high-confidence failures
Use case
Push CloudWatch Custom Metrics to Datadog for Unified Dashboards
Teams that track business or application-level metrics via CloudWatch custom namespaces can use tray.ai to periodically push those metric data points into Datadog, enabling side-by-side visualization alongside APM traces, infrastructure metrics, and third-party service data. This is especially useful for teams who use CloudWatch for AWS-specific KPIs like SQS queue depth or DynamoDB consumed capacity but want all metrics in a single Datadog dashboard. Scheduled tray.ai workflows poll CloudWatch at defined intervals and submit metric payloads to the Datadog Metrics API.
- Visualize AWS custom metrics alongside application performance data in Datadog
- Eliminate the need to build and maintain separate CloudWatch-to-Datadog metric pipelines
- Track cross-service SLOs using both CloudWatch and Datadog data sources
Use case
Auto-Resolve Datadog Monitors When CloudWatch Alarms Clear
When a CloudWatch alarm returns to an OK state, tray.ai automatically sends a recovery signal to the corresponding Datadog monitor, resolving the active alert and annotating the incident timeline with the clearance timestamp. This two-way sync prevents stale open alerts in Datadog that can desensitize on-call engineers and distort incident metrics like MTTR. Keeping both systems in sync reduces noise and improves the reliability of your alerting data.
- Prevent ghost alerts in Datadog when underlying AWS issues have already resolved
- Maintain accurate MTTR and incident duration metrics across both platforms
- Reduce on-call engineer fatigue caused by stale or unresolved alert notifications
Use case
Enrich Datadog Events with CloudWatch Anomaly Detection Findings
AWS CloudWatch Anomaly Detection uses machine learning to flag unusual metric behavior. tray.ai captures those anomaly findings and forwards them as enriched events to Datadog's event stream, letting teams correlate CloudWatch anomaly windows with deployment markers, APM traces, and RUM sessions inside Datadog to quickly identify root cause. The workflow can also automatically add Datadog tags corresponding to the anomalous CloudWatch metric dimension for rapid filtering.
- Surface AWS ML-detected anomalies within Datadog's unified event stream
- Correlate CloudWatch anomaly windows with code deployments and APM traces in Datadog
- Speed up root cause analysis by co-locating AWS and application-layer signals
Challenges Tray.ai solves
Common obstacles when integrating AWS CloudWatch and Datadog — and how Tray.ai handles them.
Challenge
Handling High-Volume CloudWatch Metric and Log Throughput
AWS CloudWatch can emit thousands of metric data points and log events per minute across large infrastructure footprints. Naive polling approaches to Datadog sync are inefficient, costly, and prone to data gaps or API rate limit errors on the Datadog side.
How Tray.ai helps
tray.ai workflows support configurable batching, pagination, and scheduled polling intervals that respect both the CloudWatch GetMetricData API quotas and the Datadog Metrics API rate limits. Built-in retry logic and error handling ensure no data points are silently dropped during high-throughput sync windows.
Challenge
Mapping CloudWatch Alarm Severities to Datadog Priority Levels
CloudWatch alarms use a simple OK/ALARM/INSUFFICIENT_DATA state model, while Datadog monitors support a richer P1-P5 priority scale and warn/critical threshold tiers. Without careful mapping logic, integrations can misclassify low-severity CloudWatch alarms as critical Datadog incidents — or miss critical AWS events entirely.
How Tray.ai helps
tray.ai's visual workflow builder lets teams define custom conditional branching logic that maps CloudWatch alarm metadata — threshold values, namespace, or alarm description tags — to the appropriate Datadog monitor priority and notification channel, so severity is represented accurately without manual intervention.
Challenge
Maintaining Two-Way State Consistency Between Both Platforms
When alerts are resolved, muted, or acknowledged in one platform, teams expect the other to reflect that change promptly. Without two-way sync, Datadog can show open alerts for AWS issues that resolved hours ago, undermining on-call trust in the alerting system and inflating MTTR statistics.
How Tray.ai helps
tray.ai supports event-driven workflows in both directions — listening for CloudWatch alarm OK transitions to resolve Datadog monitors, and optionally consuming Datadog webhooks to annotate CloudWatch dashboards with acknowledgment and mute events. Both platforms stay in sync throughout the full incident lifecycle.
Templates
Pre-built workflows for AWS CloudWatch and Datadog you can deploy in minutes.
CloudWatch Alarm to Datadog Monitor Alert
AWS CloudWatch 
Datadog This template listens for CloudWatch alarm state change events via Amazon EventBridge and automatically creates or updates a Datadog monitor alert with the alarm name, state, threshold, and affected resource. It maps CloudWatch alarm severity levels to Datadog monitor priority tiers and posts a summary notification to a configured Datadog event stream.
Scheduled CloudWatch Metrics Sync to Datadog
AWS CloudWatch Datadog On a configurable schedule, this template queries specified CloudWatch metric namespaces and dimensions, retrieves recent data points, and submits them to the Datadog Metrics API with custom tags for environment, region, and service. It's designed for teams who need AWS custom metrics visible in Datadog dashboards without configuring the native AWS integration for every metric namespace.
CloudWatch Composite Alarm to Datadog Incident Creator
AWS CloudWatch Datadog When a CloudWatch Composite Alarm enters an ALARM state, this template automatically opens a new Datadog incident, populates it with contributing alarm details and affected service metadata, assigns it to the appropriate responder team, and sends a Datadog notification. When the Composite Alarm resolves, the workflow marks the Datadog incident as resolved.
CloudWatch Log Events to Datadog Log Forwarder
AWS CloudWatch Datadog This template subscribes to one or more CloudWatch Log Groups and forwards matching log events to Datadog Log Management in real time. Each forwarded log entry is enriched with custom tags for AWS account ID, region, log group name, and service, making the logs immediately searchable and alertable within Datadog.
Datadog Monitor Recovery Sync Back to CloudWatch Dashboard
AWS CloudWatch Datadog This two-way template listens for Datadog monitor state changes — particularly recoveries and mutes — and pushes corresponding annotations to a CloudWatch dashboard widget, so AWS-focused stakeholders can see incident lifecycle events without logging into Datadog directly. It also optionally creates a CloudWatch custom metric data point to track Datadog alert frequency over time.
CloudWatch Anomaly Detection Event to Datadog Event Stream
AWS CloudWatch Datadog This template captures CloudWatch Anomaly Detection band breaches from EventBridge and enriches them with metric dimension context before posting them to the Datadog event stream. The resulting Datadog events are tagged by AWS service, region, and anomaly severity, enabling correlation overlays on Datadog APM and infrastructure dashboards.
How Tray.ai makes this work
AWS CloudWatch + Datadog runs on the full Tray.ai platform
Intelligent iPaaS
Integrate and automate across 700+ connectors with visual workflows, error handling, and observability.
Learn more →Agent Builder
Build AI agents that read, write, and take action in AWS CloudWatch and Datadog — with guardrails, audit, and human-in-the-loop.
Learn more →Agent Gateway for MCP
Expose AWS CloudWatch + Datadog actions as governed MCP tools — observable, rate-limited, authenticated.
Learn more →Ship your AWS CloudWatch + Datadog integration.
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