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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 ELB performance with Datadog
Jean-Mathieu Saponaro · 2015-10-01 · via Datadog | The Monitor blog

This post is the last of a 3-part series on monitoring Amazon ELB. Part 1 explores its key performance metrics, and Part 2 explains how to collect these metrics.

Note: The metrics referenced in this article pertain to classic ELB load balancers. If you want to learn how to monitor Application Load Balancer metrics, read this blog post.

If you’ve already read our post on collecting Elastic Load Balancing metrics, you’ve seen that you can visualize their recent evolution and set up simple alerts using the AWS Management Console’s web interface. For a more dynamic and comprehensive view, you can connect ELB to Datadog.

Datadog lets you collect and view ELB metrics, access their historical evolution, and slice and dice them using any combination of properties or custom tags. Crucially, you can also correlate ELB metrics with metrics from any other part of your infrastructure for better insight—especially native metrics from your backend instances. And with more than 100 supported integrations, you can create and send advanced alerts to your team using collaboration tools such as PagerDuty and Slack.

In this post we’ll show you how to get started with the ELB integration, and how to correlate your load balancer performance metrics with your backend instance metrics.

ELB metrics graphs on Datadog
ELB metrics graphs
ELB metrics graphs on Datadog

Integrate Datadog and ELB

To start monitoring ELB metrics, you only need to configure our integration with AWS CloudWatch by setting up role delegation in AWS IAM. You’ll need to create a role for Datadog and attach a policy that grants read-only access to your AWS services, by following the steps listed in our documentation.

Once these credentials are configured within AWS, follow the simple steps on the AWS integration tile on Datadog to start pulling ELB data.

Note that if, in addition to ELB, you are using RDS, SES, SNS, or other AWS products, you may need to grant additional permissions to the role. See here for the complete list of permissions required to take full advantage of the Datadog–AWS integration.

Keep an eye on all key ELB metrics

Once you have successfully integrated Datadog with ELB, you will see a default dashboard called “AWS-Elastic Load Balancers” in your list of integration dashboards. The ELB dashboard displays all of the key metrics highlighted in Part 1 of this series: requests per second, latency, surge queue length, spillover count, healthy and unhealthy hosts counts, HTTP code returned, and more.

ELB default dashboard on Datadog
ELB default dashboard on Datadog
ELB default dashboard on Datadog

Customize your dashboards

Once you are capturing metrics from Elastic Load Balancing in Datadog, you can build on the default dashboard and edit or add additional graphs of metrics from ELB or even from other parts of your infrastructure. To start building a custom screenboard, clone the default ELB dashboard by clicking on the gear on the upper right of the default dashboard.

Collect, visualize, and alert on Amazon ELB metrics in minutes with Datadog.

You can also create timeboards, which are interactive Datadog dashboards displaying the evolution of multiple metrics across any timeframe.

Correlate ELB with EC2 metrics

As explained in Part 1, CloudWatch’s ELB-related metrics inform you about your load balancers’ health and performance. ELB also provides backend-related metrics reflecting your backend instances health and performance. However, to fully monitor your backend instances, you should consider collecting these backend metrics directly from EC2 as well for better insight. By correlating ELB with EC2 metrics, you will be able to quickly investigate whether, for example, the high number of requests being queued by your load balancers is due to resource saturation on your backend instances (memory usage, CPU utilization, etc.).

Thanks to our integration with CloudWatch and the permissions you set up, you can already access EC2 metrics on Datadog. Here is your default dashboard for EC2.

Default EC2 dashboard on Datadog

You can add graphs to your custom dashboards and view side by side ELB and EC2 metrics. Correlating peaks in two different metrics to see if they are linked is very easy.

You can also, for example, display a host map to spot at a glance if all your backend instances have a reasonable CPU utilization:

Default EC2 dashboard on Datadog

Native metrics for more precision

In addition to pulling in EC2 metrics via CloudWatch, Datadog also allows you to monitor your EC2 instances’ performance with higher resolution by installing the Datadog Agent to pull native metrics directly from the servers. The Agent is open source software that collects and reports metrics from your individual hosts so you can view, monitor and correlate them on the Datadog platform. Installing the Agent usually requires just a single command. Installation instructions for different operating systems are available here.

By using the Datadog Agent, you can collect backend instance metrics with a higher granularity for a better view of their health and performance. The Agent reports metrics directly, at rapid intervals, and does not rely on polling an intermediary (such as CloudWatch), so you can access metrics more frequently without being limited by the provider’s monitoring API.

The Agent provides higher-resolution views of all key system metrics, such as CPU utilization or memory consumption by process.

Once you have set up the Agent, correlating native metrics from your EC2 instances with ELB’s CloudWatch metrics is a piece of cake (as explained above), and will give you a full and precise picture of your infrastructure’s performance.

The Agent can also collect application metrics so that you can correlate your application’s performance with the host-level metrics from your compute layer. The Agent integrates seamlessly with applications such as MySQL, NGINX, Cassandra, and many more. It can also collect custom application metrics as well.

To install the Datadog Agent, follow the instructions here depending on the OS your EC2 machines are running.

Conclusion

In this post we’ve walked you through integrating Elastic Load Balancing with Datadog so you can visualize and alert on its key metrics. You can also visualize EC2 metrics to keep tab on your backend instances, to improve performance, and to save costs.

Monitoring ELB with Datadog gives you critical visibility into what’s happening with your load balancers and applications. You can easily create automated alerts on any metric across any group of instances, with triggers tailored precisely to your infrastructure and usage patterns.

If you don’t yet have a Datadog account, you can sign up for a free trial and start monitoring your cloud infrastructure, applications, and services.