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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
Enable monitoring for enterprise-scale Azure environments in minutes with Datadog
Bowen Chen, Steve Harrington · 2023-05-19 · via Datadog | The Monitor blog

As enterprises build and scale business-critical applications on Azure, they need continuous visibility to understand the health and performance of their services. This can be a challenge, especially for enterprises with large-scale deployments that include an ever-increasing number of subscriptions, resources, and teams. We’re excited to announce a number of enhancements for Azure that will help enterprise customers ensure comprehensive observability and easily onboard new teams and applications to Datadog.

A foundational component of monitoring Azure environments in Datadog is our Azure integration. Once configured, this integration continuously collects metrics from all of your Azure services and enriches them with tags. This enables you to easily scope dashboards and monitors to the relevant resources, and seamlessly pivot across logs, metrics, and traces inside the Datadog platform. Our Azure integration also generates metrics that provide unique insights into your resource statuses, rate limits, quota usage, and more.

Whether you are a longtime Azure Monitor user or just starting to migrate to the cloud, we’ve enhanced our integration to make it easier and faster to start monitoring your enterprise-scale environment. Our streamlined onboarding processes enable visibility across your entire organization in just minutes—even if you’re managing hundreds or thousands of subscriptions.

In this post, we’ll cover the following updates:

  • Faster metric collection with the Azure Monitor Metrics Data plane API

  • Streamlined monitoring setup for multiple Azure subscriptions

  • Automatic custom metric collection from Application Insights

  • Recommended alerts for popular services with Azure recommended monitors

Faster metric collection with the Azure Monitor Metrics Data plane API

The Azure Monitor REST API provides telemetry that enables organizations to track the health and performance of their Azure resources. However, as environments scale, this API can become overloaded, resulting in throttling and excessive latency.

To address this challenge, Datadog partnered with Azure to develop and test the Azure Monitor Metrics Data plane API. This API supports increased querying limits and allows Datadog to ingest Azure metrics more efficiently. The result is that Datadog is able to ingest all Azure Monitor metrics with minimal latency, even for enterprise-scale environments. This highly efficient metric collection means that Datadog customers can track the health and performance of their Azure services in near-real time and get notified quickly when issues arise.

Set up monitoring for enterprise-scale environments in just a few clicks

If you’re overseeing hundreds or thousands of subscriptions, you need configuration options that scale accordingly. Our Azure Native integration (available for customers on Datadog’s US3 site) enables you to easily set up the Azure integration directly in the Azure portal. We’ve streamlined this process even further by making it possible to use just a single Datadog resource to configure monitoring for all of your subscriptions (as shown below). In just a few minutes, you can specify all the subscriptions you want to monitor with Datadog and configure the collection of Azure metrics and platform logs from across your entire environment, no matter how many subscriptions you are managing.

Select which Azure subscriptions you’d like to monitor with Datadog in the Azure portal.

We’ve also added enhancements to the onboarding experience for our standard Azure integration (available on all Datadog sites). Using the new templates launched from the Azure tile in Datadog, you can set up the integration with a simple, click-through workflow. These templates also make it easier than ever to grant Datadog access at the management group or tenant level. When you configure our standard integration this way, Datadog will automatically discover and monitor new subscriptions as they are created, ensuring seamless monitoring coverage as your Azure environment scales.

Configure Datadog for your Azure management groups using our new onboarding workflow.

Collect custom metrics directly from Azure Application Insights

Azure Application Insights enables you to collect custom metrics that provide unique insights into your application. In many cases, these contain some of the most business-critical information, such as revenue data or signals around how end users are interacting with your application.

Datadog’s Azure integration now enables the collection of your Application Insights custom metrics with a one-click setting. This is especially helpful if your organization is transitioning to Datadog from native Azure monitoring tools. We often see enterprises rely on centralized administrators to configure monitoring across many different workloads and applications owned by other teams. Since making changes to custom metrics requires code changes, getting these migrated over to Datadog can require a lot of coordination that becomes a real challenge as the number of teams grows. With the new option to enable Datadog to collect custom metrics directly from App Insights, admins can get all of their organization’s custom metrics populated in Datadog immediately.

Collect custom metrics directly from App insights with a single click.

Automated monitors help ensure that your team will get notified about critical issues, but configuring these monitors can be time-consuming, particularly in a large-scale environment. Understanding which metrics are available, how potential issues may be signaled in these metrics, and establishing the right alerting thresholds can involve a significant amount of research.

Azure recommended monitors are available out of the box and can serve as a starting point to help you quickly get alerts in place for common issues. Whether you’re new to the cloud or a longtime Azure expert, recommended monitors can help your organization detect potential issues in:

  • Azure App Service

  • Azure App Gateway

  • Azure quotas and rate limits

  • Azure Service Health events

  • Azure SQL DB

  • Azure VM

For example, the preconfigured Azure API rate limit monitor evaluates Datadog-generated usage metrics to notify you when you’re approaching global consumption limits. This enables you to take actions to remediate the issue, such as updating your retry logic to reset your connection after a certain number of attempts, before incidents occur.

Implement preconfigured alerts on common Azure issues with recommended monitors.

You can explore all of the available Azure recommended monitors in the Datadog app by creating a “New Monitor” and selecting the “Recommended” tab.

Start monitoring your enterprise Azure environment with Datadog

Datadog’s simplified onboarding options enable even large-scale organizations to configure monitoring across their Azure environment in just minutes. And new Azure recommended monitors and App Insights custom metric collection make it faster and easier than ever for teams to leverage Datadog’s powerful observability platform for their Azure workloads. To get started, install the Azure integration.

If you don’t already have a Datadog account, you can sign up for a free 14-day trial today.