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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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Accelerate your Azure integration setup with guided onboarding
2025-11-03 · via Datadog | The Monitor blog

Getting started with monitoring for Microsoft Azure environments can be a lengthy and manual process. Many tools require users to create app registrations, assign permissions, and enable log forwarding or telemetry data collection across multiple portals and scripts. These fragmented steps slow down onboarding and introduce opportunities for misconfiguration, making it harder for teams to quickly achieve full visibility.

The new onboarding flow for Datadog’s integration with Azure resolves these challenges by providing an automated, end-to-end setup experience directly within the Datadog platform. This unified process helps you get your Azure environment configured correctly the first time so that you can start monitoring your resources in minutes.

In this post, we’ll explore how the new Azure onboarding flow gives you the opportunity to:

Complete onboarding faster with less manual work

The new onboarding experience minimizes manual intervention while implementing full coverage of your Azure environment. Instead of jumping between the Azure portal, Terraform scripts, and the Datadog UI, you can now complete your setup through a single guided flow.

The process begins on the Datadog Azure Integrations page, where you’re prompted to connect your Azure account. From there, Datadog automates the essential steps of onboarding, including creating the app registration, assigning the correct permissions, and provisioning the resources required for monitoring. The flow also configures log forwarding to send platform logs directly from Azure to Datadog without additional steps.

Screenshot of the Azure integration tile in Datadog.

You retain full control over what’s included in your monitoring setup. During onboarding, you can specify which subscriptions, services, and resources to monitor. You can apply filters at the start so that Datadog collects only the metrics and logs that matter most to you. For example, you can select specific Azure resources or apply tag-based filtering to align data collection with your cost management and compliance policies.

With this new flow, a process that previously required manual steps and configuration edits can now be completed in just a few minutes. The increased automation reduces the likelihood of misconfigurations and helps your teams begin monitoring their Azure environments with Datadog faster.

Choose the setup method that best fits your workflow

Organizations have different requirements for managing cloud integrations, so Datadog provides three setup options to match your preferred workflow:

  • Quickstart (Azure CLI): For the fastest path to integration, you can choose the CLI option and run a single command in Azure Cloud Shell. This command automatically configures the app registration, permissions, telemetry data collection, and log forwarding based on your selections in the onboarding flow. It’s ideal for teams that want to connect Datadog to their environment in minutes without managing templates or scripts.
  • Terraform: For teams that use infrastructure as code (IaC), Datadog generates a Terraform template that reflects your chosen subscriptions, telemetry data types, and log forwarding settings. This template makes it simple to control versioning of your configuration and roll out consistent integrations across multiple environments. Because the Terraform script is pre-populated from your onboarding choices, you can copy the template and run it in your environment without further editing.
  • Existing app registration: If your organization already maintains its own Azure app registrations for Datadog or other integrations, you can use this option to connect your existing configuration. This method gives you flexibility to manage credentials and permissions in compliance with your internal governance standards, and you still benefit from the rest of Datadog’s guided onboarding.
Screenshot of the Azure setup methods available in the Datadog integration onboarding flow. The Terraform method is selected.

Start monitoring your Azure environment in minutes

With Datadog’s new guided onboarding flow for the Azure integration, your teams can connect to their Azure environments faster. Whether you deploy through the Azure CLI, Terraform, or an existing app registration, the process reduces the friction and potential errors that come with manual configuration. To learn more, check out our documentation for getting started with Azure.

If you’re new to Datadog, you can sign up for a 14-day free trial to get started.