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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 - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Monitor Amazon ECS Anywhere with Datadog
2021-05-27 · via Datadog | The Monitor blog

Amazon Elastic Container Service (ECS) is a managed compute platform for containers that was designed to be simple to configure, with opinionated defaults to help users get started quickly. ECS customers can run containerized workloads on either Amazon EC2 instances or the serverless Fargate platform without having to maintain a control plane—and can easily integrate ECS with other AWS resources, like Network Load Balancers, to architect their infrastructure.

AWS introduced ECS Anywhere so customers with on-premise data centers can take advantage of the ECS-managed control plane to run containerized environments using their existing infrastructure investments, ensure data security and compliance, and run containerized applications on edge devices. In an ECS Anywhere deployment, an ECS Anywhere Agent can run on any compute infrastructure (such as bare-metal servers or VMs) and communicates with the local Docker daemon as well as an AWS API. This makes it possible for ECS to orchestrate tasks and services using on-premise resources as well as in the cloud on EC2 and Fargate.

Datadog is proud to be a launch partner for ECS Anywhere. Using Datadog, you can get comprehensive visibility into your containerized applications—wherever they are deployed—so you can:

The architecture of a Datadog deployment within ECS Anywhere.

Modernize your on-prem workloads

Since ECS provides a managed control plane, ECS Anywhere is particularly useful for migrating on-premise workloads to an orchestrated, containerized environment. Datadog can help you track this migration to ensure that your newly deployed containers are configured correctly for your underlying resources—and that not-yet-containerized processes continue to run as expected.

Datadog’s APM and Distributed Tracing gives you deep insight into the performance of your applications, so you can easily tell when a service deployed using a new compute platform falls short of expectations. Our tracing libraries let you tag your applications by ECS task, task family, service, and cluster, so you can compare performance between your containerized and host-based versions of the same application.

In the example below, we’re using Datadog’s Live Analytics view to compare request durations between applications running in our ECS Anywhere cluster and those running without container-based process isolation. If our containerized applications show a higher-than-expected response latency or error rate, we will know to check for misconfigurations in our ECS setup (such as an underprovisioned task size).

Datadog's Live Analytics view helps you understand trends in the performance of your ECS Anywhere applications.

As you monitor application performance, you’ll also want to track the health and resource utilization of your workloads to ensure they run as expected on your infrastructure as you migrate to containers. Using Datadog’s Live Processes view, you can get down-to-the-second visibility into the health and performance of your running executables, both inside and outside of containers.

In this example, we have created a dashboard to visualize live process data for both containerized (ecs_cluster:scanner-iot-fleet) and non-containerized (ecs_cluster:none) versions of the same application. Since all processes within our infrastructure are using low levels of CPU and memory, we know that we have adequate resources to schedule further ECS Anywhere containers during sudden increases in demand.

Custom dashboard visualizing processes across infrastructure types.

You can also use Datadog’s Live Container view to get a closer look at the health and performance of our ECS Anywhere containers. Datadog pulls metadata from ECS as well as Docker, so you can easily group and filter your data by cluster, task, task family, and service, making it simple to know where availability or resource utilization issues are occurring in your infrastructure. For example, by showing resource utilization in our scanner-iot-fleet cluster, we can see that the scanner-hub container may be at risk of CPU saturation.

The Live Container view filtered by ECS Anywhere tags.

Keep an eye on devices at the edge

ECS Anywhere is well suited to running lightweight, containerized applications on devices at the edge of your on-premise network, such as IoT devices. As long as a device runs the ECS Anywhere Agent and has adequate resources, ECS can schedule your application containers there automatically—and execute locally loaded configurations temporarily during a network outage. Datadog helps ensure that your ECS-based edge applications are running as expected and makes it easier to spot unreliable network connectivity.

Using Datadog’s Network Map, you can get a full overview of the topology of your edge network and identify any devices affected by connectivity losses. You can use tags to have nodes in the Network Map represent ECS tasks, services, or containers, so you can quickly tell if edge applications have lost connectivity upstream—or if the ECS Agent has stopped communicating with the control plane. You can then use the Network Page to track network flows to and from a specific edge device over time, helping you determine when an issue took place and providing crucial context for your investigations. In this example, we are using the Network Page to monitor traffic between each ECS Agent container in our infrastructure and the AWS backend.

The Datadog Network Page showing an ECS Anywhere deployment.

To tell if ECS Anywhere Agents on edge devices are having trouble fetching configurations from the ECS backend, you can use Datadog’s Log Explorer to track your ECS Agent logs over time. The Datadog Agent can fetch logs from each ECS Agent container that runs on your host, meaning that you can quickly identify issues with connecting to the ECS API or scheduling tasks. Here we’re using the patterns aggregation in the Log Explorer to view common logs associated with the ECS Agent.

ECS Agent logs in the Log Explorer.

ECS is anywhere—and so is Datadog

Now that Datadog integrates with ECS Anywhere, you can get full visibility into your ECS clusters, no matter where you have deployed them. And with 1,000+ integrations, Datadog can help you monitor your entire on-premise or hybrid infrastructure, including your VMware vSphere VMs, Google Anthos applications, and Cisco Meraki devices.

To get started monitoring ECS Anywhere, follow these steps, which will run the Datadog Agent on one host in your infrastructure:

  1. Download the ECS task definition for the Datadog Agent, datadog-agent-ecs.json.

  2. Add the following field to your Datadog Agent task definition, which instructs ECS to deploy the Datadog Agent on your ECS Anywhere infrastructure (rather than on EC2 or Fargate):

"requiresCompatibilities": [

"EXTERNAL"

]

  1. Replace <YOUR_DATADOG_API_KEY> with your Datadog API key in datadog-agent-ecs.json.

  2. Run the following commands to register the Datadog Agent task definition, launch the task on ECS Anywhere, and verify that the task is running:

export CLUSTER_NAME=<YOUR_ECS_CLUSTER_NAME>

# Register the task definition

aws ecs register-task-definition --cli-input-json file://datadog-agent-ecs.json

# Run the task

aws ecs run-task --cluster $CLUSTER_NAME --launch-type EXTERNAL --task-definition datadog-agent-task

# Get the Task ID

TEST_TASKID=$(aws ecs list-tasks --cluster $CLUSTER_NAME | jq -r '.taskArns[0]')

# Verify Task is Running

aws ecs describe-tasks --cluster $CLUSTER_NAME --tasks $TEST_TASKID

You can also add your Datadog Agent task to an ECS daemon service to ensure that it runs on each host in your ECS Anywhere infrastructure. For more information, consult the documentation on our ECS integration.

If you don’t have a Datadog account, you can sign up for a free trial.