AWS CloudWatch connector
Automate AWS CloudWatch Monitoring, Alerting, and Incident Response
Connect CloudWatch metrics, logs, and alarms to your entire tech stack for real-time observability and automated incident management.
What can you do with the AWS CloudWatch connector?
AWS CloudWatch is how teams running workloads on AWS keep tabs on what's happening — capturing metrics, logs, and traces across hundreds of services. Integrating CloudWatch with tray.ai lets you tear down the wall between your monitoring data and the tools your team actually uses: Slack, PagerDuty, Jira, and beyond. Instead of manually triaging alarms or copy-pasting log data, you can build automated workflows that detect anomalies, route incidents to the right people, and trigger remediation actions the moment something goes wrong.
Automate & integrate AWS CloudWatch
Automating AWS CloudWatch business process or integrating AWS CloudWatch data is made easy with tray.ai
Use case
Automated Incident Alerting and Escalation
When a CloudWatch alarm breaches a threshold — CPU spikes, error rates climbing, latency degrading — tray.ai can immediately route a structured alert to Slack, PagerDuty, or OpsGenie with full context attached. If the incident isn't acknowledged within a configurable time window, the workflow automatically escalates to the on-call manager or creates a high-priority ticket in Jira. This eliminates the lag between detection and response that costs teams uptime.
Use case
Log Anomaly Detection and Ticket Creation
CloudWatch Logs Insights queries can surface error patterns, security anomalies, or unusual application behavior on a scheduled basis. tray.ai workflows can run these queries periodically, evaluate the results against defined thresholds, and automatically open Jira, ServiceNow, or GitHub Issues tickets when anomalies are detected. Teams get a proactive bug and security ticket backlog without anyone manually scanning logs.
Use case
Infrastructure Cost and Usage Reporting
CloudWatch metrics like EC2 CPU utilization, RDS connections, and Lambda invocation counts are useful for rightsizing and cost optimization. tray.ai can aggregate these metrics on a daily or weekly schedule and push formatted reports to Slack, email, or a Google Sheet, giving engineering and finance teams visibility into resource usage trends without AWS console access. Dashboards in tools like Google Looker Studio or Notion stay automatically up to date.
Use case
Automated Remediation and Self-Healing Workflows
When CloudWatch detects a specific failure pattern — an EC2 instance with sustained high CPU, a Lambda function with an elevated error rate, or an RDS instance approaching max connections — tray.ai can trigger automated remediation actions via AWS APIs. This could mean restarting a service, invoking an AWS Lambda function, scaling an Auto Scaling Group, or posting a runbook link to the incident channel. Self-healing workflows dramatically reduce the blast radius of common failure modes.
Use case
CI/CD Pipeline Health Monitoring
CodeBuild, CodePipeline, and ECS deployments all emit metrics and logs to CloudWatch that matter for understanding build and deployment health. tray.ai can monitor these metrics and notify engineering teams in Slack or Microsoft Teams when a deployment fails, a build time exceeds a threshold, or error rates spike post-deploy. Correlating deployment events with CloudWatch metric changes helps teams catch bad releases within minutes.
Use case
Security and Compliance Event Routing
CloudWatch can ingest CloudTrail logs and VPC Flow Logs to surface unauthorized API calls, unusual login patterns, and suspicious network activity. tray.ai workflows can evaluate these log events against security rules and automatically open tickets in your SIEM, notify the security team in Slack, or trigger quarantine actions via AWS IAM or Security Hub. This closes the gap between detection and response for compliance-sensitive environments.
Use case
Cross-Account Observability Aggregation
Organizations running multi-account AWS environments often struggle to get a unified view of CloudWatch metrics and alarms across accounts. tray.ai can pull metrics and alarm states from multiple AWS accounts using different credential sets and consolidate them into a single Slack digest, Airtable dashboard, or data warehouse table. Platform engineering teams get organization-wide visibility without standing up additional AWS monitoring infrastructure.
Build AWS CloudWatch Agents
Give agents secure and governed access to AWS CloudWatch through Agent Builder and Agent Gateway for MCP.
Data Source
Query Metrics Data
Retrieve time-series metrics from CloudWatch for any AWS resource, including CPU utilization, memory usage, or request counts. An agent can use this data to assess system health and feed insights into automated decision-making workflows.
Data Source
Fetch Alarm States
Pull the current state and history of CloudWatch alarms to see which resources are in an OK, ALARM, or INSUFFICIENT_DATA state. An agent can use this to prioritize incident response or escalate issues automatically.
Data Source
Retrieve Log Events
Search and fetch log events from CloudWatch Logs log groups and streams to surface errors, warnings, or specific patterns. An agent can analyze these logs to diagnose root causes or spot anomalous behavior.
Data Source
Describe Log Groups and Streams
List available log groups and streams within CloudWatch Logs to understand what logging data exists across an AWS environment. This helps an agent navigate and target the right data sources for deeper investigation.
Data Source
Run Logs Insights Queries
Execute CloudWatch Logs Insights queries to aggregate and analyze large volumes of log data using structured queries. An agent can use this to generate operational summaries, detect trends, or find specific error patterns at scale.
Data Source
Get Dashboard Data
Retrieve existing CloudWatch dashboard configurations and their associated metrics to understand the current monitoring setup. An agent can use this to compile status reports or pull performance indicators for stakeholders.
Agent Tool
Create or Update Alarms
Programmatically create or modify CloudWatch metric alarms with specific thresholds, evaluation periods, and notification actions. An agent can adjust alerting configurations as infrastructure or business requirements change.
Agent Tool
Publish Custom Metrics
Send custom metric data points to CloudWatch from external systems or business processes. An agent can use this to instrument non-AWS services and bring their operational data into a single monitoring environment.
Agent Tool
Set Alarm State
Manually override the state of a CloudWatch alarm to trigger or suppress notifications and automated actions. An agent can use this during maintenance windows or incident simulations to control alarm behavior without touching thresholds.
Agent Tool
Create or Update Dashboards
Build or update CloudWatch dashboards to visualize metrics and logs for a specific service or incident. An agent can automatically generate dashboards when new workloads are deployed or during active incidents so teams have immediate situational awareness.
Agent Tool
Delete Alarms
Remove outdated or redundant CloudWatch alarms to keep monitoring configurations clean and relevant. An agent can automate alarm lifecycle management as AWS resources are deprovisioned or reorganized.
Agent Tool
Create Log Groups and Streams
Provision new CloudWatch log groups and streams as part of infrastructure setup workflows. An agent can make sure logging is in place whenever new services or environments are spun up.
Get started with our AWS CloudWatch connector today
If you would like to get started with the tray.ai AWS CloudWatch connector today then speak to one of our team.
AWS CloudWatch Challenges
What challenges are there when working with AWS CloudWatch and how will using Tray.ai help?
Challenge
Translating Raw CloudWatch Alarms into Actionable Context
CloudWatch alarms fire with minimal context — just a metric name, threshold, and state. On-call engineers receiving bare alarm notifications often spend precious minutes manually pulling up dashboards, querying logs, and tracking down the service owner before they can even start responding.
How Tray.ai Can Help:
tray.ai workflows automatically enrich alarm events the moment they fire — fetching metric history, querying related log groups, identifying the owning team from resource tags, and attaching all of it to the Slack message or PagerDuty incident. Responders get a complete picture before they even open the AWS console.
Challenge
Alert Fatigue from High-Volume Alarm Notifications
Busy AWS environments can generate hundreds of CloudWatch alarm state changes per day, many of them transient or low-severity. When every alarm fires an unfiltered notification to Slack or PagerDuty, engineers quickly start ignoring them — including the critical ones.
How Tray.ai Can Help:
tray.ai workflows support conditional logic, deduplication, and alarm state tracking so you can filter out transient flaps, suppress known maintenance windows, group related alarms, and only page the on-call engineer when a genuine, sustained problem is detected. Your alert channels stay signal-rich and worth reading.
Challenge
No Native Integration Between CloudWatch and Business Tools
CloudWatch is purpose-built for AWS observability, with no built-in connectors to tools like Jira, ServiceNow, Confluence, Notion, or Salesforce. Teams that need to turn monitoring data into tickets, reports, or stakeholder communications end up building and maintaining custom Lambda functions or scripts.
How Tray.ai Can Help:
tray.ai's CloudWatch connector works natively alongside 600+ other connectors, so you're not writing or maintaining custom integration code. Workflows that route alarms to Jira, push metrics to Google Sheets, or update Confluence runbooks can be built visually and changed without filing an engineering ticket.
Challenge
Multi-Account and Multi-Region Observability Gaps
Enterprise AWS environments typically span multiple accounts and regions, but CloudWatch metrics and alarms are scoped to individual account-region pairs. Getting a unified view of system health requires either AWS-native tooling investments or significant custom development.
How Tray.ai Can Help:
tray.ai workflows can be configured with multiple sets of AWS credentials and iterate across accounts and regions in a single workflow execution. Teams can aggregate CloudWatch data organization-wide into Slack digests, dashboards, or data warehouses without additional AWS infrastructure investment.
Challenge
Keeping Incident Management Tools in Sync with CloudWatch Alarm State
Many teams have sophisticated incident management processes in tools like PagerDuty, OpsGenie, or ServiceNow that don't automatically stay in sync with CloudWatch alarm state changes. The result: stale incidents, duplicate pages, and unresolved tickets when alarms self-recover.
How Tray.ai Can Help:
tray.ai workflows handle the full alarm lifecycle — creating incidents when alarms fire, updating them as the situation evolves, and automatically resolving or closing them when CloudWatch reports the alarm has returned to OK. Your incident management tooling stays in sync with the actual state of your AWS infrastructure.
Talk to our team to learn how to connect AWS CloudWatch with your stack
Find the tray.ai connector with one of the 700+ other connectors in the tray.ai connector library to integrate your stack.
Integrate AWS CloudWatch With Your Stack
The Tray.ai connector library can help you integrate AWS CloudWatch with the rest of your stack. See what Tray.ai can help you integrate AWS CloudWatch with.
+AWS Lambda & AWS CloudWatch
Learn how to integrate AWS Lambda and AWS CloudWatch together
+PagerDuty & AWS CloudWatch
Learn how to integrate PagerDuty and AWS CloudWatch together
+Slack & AWS CloudWatch
Learn how to integrate Slack and AWS CloudWatch together
+Datadog & AWS CloudWatch
Learn how to integrate Datadog and AWS CloudWatch together
+AWS Kinesis & AWS CloudWatch
Learn how to integrate AWS Kinesis and AWS CloudWatch together
+AWS SQS & AWS CloudWatch
Learn how to integrate AWS SQS and AWS CloudWatch together
Start using our pre-built AWS CloudWatch templates today
Start from scratch or use one of our pre-built AWS CloudWatch templates to quickly solve your most common use cases.
AWS CloudWatch Templates
Find pre-built AWS CloudWatch solutions for common use cases
Template
CloudWatch Alarm to Slack and PagerDuty Incident
Automatically creates a PagerDuty incident and posts a rich Slack message with metric context whenever a CloudWatch alarm enters the ALARM state, and resolves both when the alarm returns to OK.
Steps:
- Poll CloudWatch for alarms in ALARM state on a 1-minute schedule or receive alarm events via SNS webhook
- Enrich the alarm data by fetching the associated metric history from CloudWatch for the past 30 minutes
- Create a PagerDuty incident with severity mapped from alarm priority and post a formatted Slack message with metric graph link
- Monitor for alarm state returning to OK and automatically resolve the PagerDuty incident and update the Slack thread
Connectors Used: AWS CloudWatch, PagerDuty, Slack
Template
Scheduled CloudWatch Logs Insights Report to Slack
Runs a CloudWatch Logs Insights query on a defined schedule, formats the results, and posts a digest to a Slack channel — useful for daily error summaries, API latency reports, or Lambda cold start tracking.
Steps:
- Trigger workflow on a daily or weekly cron schedule
- Execute a parameterized Logs Insights query against the target CloudWatch log group
- Format query results into a readable Slack Block Kit message with metrics called out clearly
- Post the report to the designated Slack channel and optionally append results to a Google Sheet for trending
Connectors Used: AWS CloudWatch, Slack
Template
CloudWatch Alarm to Jira Bug Ticket
When a CloudWatch alarm fires indicating an application error rate or latency breach, automatically create a Jira bug ticket pre-populated with metric data, affected service, and a link to the CloudWatch dashboard.
Steps:
- Receive CloudWatch alarm state change event via SNS or polling
- Deduplicate against open Jira tickets to avoid creating duplicate issues for the same alarm
- Create a Jira bug with auto-populated summary, description containing metric values, and correct project and priority fields
- Post a Slack notification to the engineering channel with a link to the newly created Jira ticket
Connectors Used: AWS CloudWatch, Jira, Slack
Template
EC2 High CPU Auto-Remediation Workflow
Detects sustained EC2 high CPU utilization via CloudWatch, attempts automated remediation by notifying the application team and optionally triggering an Auto Scaling action, then logs the event for audit purposes.
Steps:
- Trigger on a CloudWatch alarm for EC2 CPUUtilization exceeding 85% for 5 consecutive minutes
- Fetch instance metadata including tags, Auto Scaling Group membership, and recent CloudWatch metrics
- Post a Slack alert to the owning team channel with instance details and a prompt to acknowledge or auto-remediate
- If not acknowledged within 10 minutes, trigger an Auto Scaling scale-out action and log the event to a Google Sheet audit trail
Connectors Used: AWS CloudWatch, AWS EC2, Slack, Google Sheets
Template
Multi-Account CloudWatch Health Digest
Aggregates CloudWatch alarm states across multiple AWS accounts and posts a consolidated morning health report to a Slack channel, giving platform teams organization-wide visibility in one place.
Steps:
- Trigger on a daily morning schedule and iterate over a list of configured AWS account credentials stored securely in tray.ai
- Query each account's CloudWatch for alarms currently in ALARM or INSUFFICIENT_DATA state
- Aggregate all results and group by account, service, and severity
- Post a structured Slack summary with per-account health status and append raw data to a Google Sheet for historical tracking
Connectors Used: AWS CloudWatch, Slack, Google Sheets
Template
Post-Deployment Error Rate Monitor with Automatic Rollback Alert
After a deployment event is detected, monitors CloudWatch error rate metrics for a configurable window and triggers a rollback alert or ticket in Jira if error rates spike above a safe threshold.
Steps:
- Trigger workflow from a GitHub deployment event webhook or CodePipeline state change notification
- Begin polling the application's CloudWatch error rate metric every 60 seconds for a 15-minute observation window
- If error rate exceeds a configurable threshold, immediately post a Slack alert with a rollback recommendation and open a critical Jira ticket
- If metrics remain healthy for the full observation window, post a deployment success confirmation to Slack
Connectors Used: AWS CloudWatch, Jira, Slack, GitHub