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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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Enable preconfigured alerts with Recommended Monitors for AWS
Candace Shamieh · 2023-06-12 · via Datadog | The Monitor blog

When you first onboard to a monitoring and security platform, it can be difficult to know where to start. Which services should you monitor? What thresholds should you set? How often should you alert your team, and where’s the best starting point for investigations? Datadog simplifies this process—providing more than 1,000 built-in integrations for the quick, convenient unification of all your systems, out-of-the-box dashboards for troubleshooting, and preconfigured alerts with Recommended Monitors for proactive monitoring.

Our Recommended Monitors provide you with alert queries and thresholds for a variety of services, allowing you to get started with confidence in minutes. Each Recommended Monitor is preconfigured based on the firsthand expertise and feedback of our own internal teams, technology partners, and thousands of customers, so you can be assured that any issues within your environment will be brought to your attention at the right time.

To expand our commitment to simplified alerting, we’re pleased to introduce our Recommended Monitors for AWS, created to help you thoroughly monitor the AWS cloud services that are most used by our customers. We currently provide Recommended Monitors for Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), AWS Lambda, and Amazon Simple Query Service (SQS). Our AWS Recommended Monitors adhere to the service monitoring guidelines dictated by AWS and our alerting best practices, meaning that each one will be preventative, contextual, and actionable.

In this post, we’ll discuss where to start with AWS Recommended Monitors, cover a few use cases that will help you understand their value, and explain how they can assist you in maintaining the health of your AWS environment.

Start monitoring your AWS resources at scale in minutes

Once you have installed any AWS integration that has a Recommended Monitor, you can enable each corresponding monitor within Datadog to immediately start receiving alerts that will inform you of AWS resource performance. For example, if you want to verify that your EC2 instances can adequately handle your workload demand, our CPU utilization monitor has prefilled queries and thresholds in place that can help you rebalance your workloads before an issue disrupts your environment.

Search Datadog Library for AWS Recommended Monitors

Use Monitor Summary widgets to add the AWS Recommended Monitors that you’re using to our out-of-the-box dashboards, allowing you to easily access a variety of metrics from one convenient location within Datadog. We have created out-of-the-box dashboards for many AWS cloud services, including EC2, RDS, and Lambda, so you can troubleshoot effectively with the insightful data that you need to resolve investigations, remediate issues, and prevent future disruptions to production.

In addition to threshold alerts, AWS Recommended Monitors can also include machine learning-powered anomaly detection, notifying you of activity that deviates from the normal pattern. For example, if you enable the anomaly detection monitor for RDS database connections, you’ll be notified any time the number of database connections fell outside of the normal pattern for a 15-minute time period. In this use case, it would be helpful to leverage our RDS out-of-the-box dashboard to see your historical workload patterns. Then, you can customize the monitor’s time period, alert and warning thresholds, escalation policies for notification, add additional queries and formulas, or modify other specifications of the monitor so that it properly fits your needs.

Create a new anomaly detection monitor with prefilled queries and thresholds

To keep track of the health and status of your AWS resources, you can enable AWS Recommended Monitors that will check if your workloads are performing well and available, and that processing time and utilization are meeting or exceeding your resource response time expectations. For a comprehensive list of AWS Recommended Monitors, visit the Datadog monitor library.

Get notified and take action today

With AWS Recommended Monitors, you’ll have out-of-the-box access to the best monitoring practices for the services in your system, so you can incorporate actionable alerts into your monitoring workflow within minutes of installing your AWS integrations. We design Recommended Monitors to save you time and effort, eliminate the complexities of configuration, and simplify the Datadog onboarding process so that you can focus your energy on the tasks that matter most.

Check out our documentation for more information about getting started. If you’re using Azure, check out our blog post about Recommended Monitors for Azure. And if you’re new to Datadog, you can get started with a 14-day free trial.