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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - 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Introducing our AWS 1-click integration
2020-06-02 · via Datadog | The Monitor blog

Datadog’s AWS integration brings you deep visibility into key AWS services like EC2 and Lambda. We’re excited to announce that we’ve simplified the process for installing the AWS integration. If you’re not already monitoring AWS with Datadog, or if you need to monitor additional AWS accounts, our 1-click integration lets you get started in minutes.

Datadog's out-of-the-box AWS dashboard shows metrics and events from the ELB, EC2, EBS, and ECS services.

To configure Datadog to monitor AWS, you need to create an IAM role and an associated policy, associate that role with an external AWS account, and deploy a Lambda function. The new 1-click installation automates this configuration using CloudFormation, so you can quickly start to gather metrics, logs, and traces from your AWS services.

Serverless visibility with the Datadog Forwarder

AWS Lambda makes serverless functions easy to deploy and update. Now, our AWS 1-click integration makes them easy to monitor by automatically including the Datadog Forwarder. The Datadog Forwarder is a Lambda function that brings your serverless metrics, logs, and traces into Datadog so you can explore, analyze, and alert on them.

Datadog collects standard Lambda metrics like invocation count, error rates, and total execution time. You can also collect custom metrics and enable Lambda enhanced metrics to extend your serverless visibility even further.

Datadog's Lambda enhanced metrics dashboard shows toplists and graphs visualizing function invocations, execution time, errors, cold starts, and memory usage.

The screenshot above shows Datadog’s pre-built Lambda enhanced metrics dashboard, which visualizes duration, cost, memory usage, cold starts, and other enhanced metrics from Lambda. It’s easy to add graphs to your AWS dashboards so you can display custom metrics and key business metrics. Of course, you can also create alerts based on any of your Lambda metrics to automatically notify your team of issues as they arise.

To bring context to the serverless metrics you see on your dashboards, the Datadog Forwarder collects logs from each Lambda function’s CloudWatch log group and automatically ships them to Datadog. The AWS 1-click integration process automatically sets up Log Rehydration™, so even your archived logs are available if you need to search and analyze them.

The AWS 1-click integration also collects traces from your serverless functions, via AWS X-Ray and through Datadog APM’s native support for Lambda tracing. This means you can visualize which Lambda functions—and even non-Lambda services—were involved in fulfilling any request.

App Analytics allows you to search and filter traces by tag—custom application tags you’ve applied (e.g., customer-id) as well as infrastructure-level tags that are applied automatically (e.g., availability-zone). Applying the same tags to your metrics and logs helps you understand user experience by seeing the full context of any single trace.

Quick visibility into the wider AWS landscape

Monitoring your Lambda functions is only part of understanding the overall health and performance of your AWS-based workloads. Our 1-click integration brings visibility to the AWS services that power your applications, including RDS, EC2, Elasticache, and more. You can view built-in dashboards for many of these services, so you have instant insight into your AWS environment.

Amazon RDS is one of the many AWS services with built-in dashboards that display metrics as soon as you install the AWS 1-click integration.
Datadog's AWS RDS dashboard shows graphs visualizing RDS connections, replication, latency, and resource utilization.
Amazon RDS is one of the many AWS services with built-in dashboards that display metrics as soon as you install the AWS 1-click integration.

In addition to Lambda, the Datadog Forwarder will also immediately begin collecting logs from any AWS service you’ve configured to log to CloudWatch or S3. In the screenshot below, the graph shows CPU utilization across a group of RDS instances and illustrates how to pivot to related logs by clicking on a point on the graph.

A graph visualizes the CPU usage of the ten RDS instances with the highest utilization. A context menu shows the View related logs item highlighted.

To learn how to enable logging on various AWS services, see our documentation.

Click here

To begin monitoring AWS with Datadog (or to configure additional AWS accounts to monitor), navigate to the Configuration tab on the AWS integration tile. Choose either Install Integration (if you’re not already monitoring AWS with Datadog) or Add another account (if you’re setting up an additional AWS account to monitor). In the Account: New Account form (shown below), click the Automatically Using CloudFormation button.

Datadog's New Account form. To proceed with AWS 1-click integration from here, click Automatically Using CloudFormation.

On the next screen, you can customize your setup so that your CloudFormation template includes the correct parameters to configure the integration to collect your AWS data. First, select the Datadog products to integrate with your AWS account and the AWS region in which to create the CloudFormation stack. Next, select an API key from your Datadog account—or create a new one—to send AWS data to Datadog. Then click Launch CloudFormation Template.

A setup form includes buttons for four Datadog products: infrastructure monitoring, log management, serverless monitoring, and cloud security posture management. A drop-down menu lists the AWS regions, and the API key to be used with the integration is shown.

In a new tab, you’ll be taken to the CloudFormation page to create your stack. The values in this form, including the Datadog API key, are filled in for you based on the information you provided in the previous step.

The CloudFormation Quick Create Stack form shows fields for the required, optional, and advanced parameters used to create the stack.

Once you submit this form, AWS will automatically create the necessary resources. When AWS has completed this process, return to Datadog’s AWS integration tile in your original tab and click the Refresh to Check Status button. When the installation has completed, you’ll see your account information and a list of AWS services you can monitor.

Datadog's AWS integration tile shows a list of all AWS services you can monitor, with a checkbox for each. The AWS Account ID and AWS Role fields are in the AWS accounts section of the page.

You should see metrics from your AWS account begin to flow into your Datadog dashboards within a few minutes.

Look into the cloud

With Datadog’s 1-click AWS integration and rich out-of-the-box dashboards, you can start monitoring dozens of AWS services—plus more than 1,000 other technologies—in just a few minutes. See the documentation for guidance on setting up the AWS integration. If you’re not already using Datadog, start today with a 14-day free trial.