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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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Monitor your AWS Trusted Advisor Service Limit Checks with Datadog
Abril Loya McCloud · 2016-07-08 · via Datadog | The Monitor blog

AWS Trusted Advisor is a valuable component newly introduced into the AWS Management Console for all AWS users. It analyzes your usage across services and provides best practice recommendations to help you optimize your cost, performance, security, and fault tolerance through four core checks.

Trusted Advisor’s Service Limits Check gives AWS customers access to granular resource consumption data for their different AWS services, which makes it the most actionable of the core checks. Monitoring your resource consumption data with the help of Trusted Advisor and Datadog takes the guesswork out of optimizing and scaling your AWS infrastructure.

AWS Trusted Advisor + Datadog

Datadog integrates with AWS Trusted Advisor to automatically take all of your Service Limit Check metrics and display them on an out-of-the-box dashboard for monitoring at a glance. With Datadog, you can quickly see how the custom optimization recommendations from Trusted Advisor affect your resource allocation and consumption.

monitor aws trusted advisor image

Once you have configured AWS Trusted Advisor, Datadog will automatically pull Service Limit data from all of your AWS services as three metrics: aws.advisor.servicelimit.usage, aws.advisor.servicelimit.max, and aws.advisor.servicelimit.usageratio. You can further drill down by looking at these three metrics by their automatically tagged service and limit names. For example, aws.advisor.servicelimit.usage when tagged with limitname:activeloadbalancers and service_name:elb allows you to quickly see the resource consumption rates of your active ELB instances.

Monitoring your Service Limit Check metrics allows you to set up custom monitors and alerts within Datadog in case of unexpected increases or decreases in your resource consumption. These custom monitors provide actionable insight into your AWS performance and usage while also providing more precise control over your services’ resource consumption. Scaling your AWS environment also becomes much more manageable thanks to insight into real-time and historical resource consumption.

Try it

If you are already a Datadog customer using our AWS integration, setting up AWS Trusted Advisor takes just a minute. All you have to do is log into your AWS Management Console account and update your Datadog access policy to include the line support:* per our AWS documentation. If you’ve yet to try Datadog and would like to have more insight into your AWS consumption, you can sign up for a Datadog trial.

Once you’ve updated the role, Datadog will automatically crawl for your Service Limit Check metrics. In case you are unable to see certain metrics after configuration, we recommend you check your AWS subscription tier to ensure you have access to those metrics.