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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
Recapping Microsoft Build 2024
2024-07-02 · via Datadog | The Monitor blog

Last month, Datadog traveled west to Seattle for Microsoft Build. Following a packed three days of keynotes, demos, breakouts, and meetings, we’ve collected the key announcements most relevant to the observability and security problems Datadog solves for Azure customers across on-prem, hybrid, and multi-cloud environments.

New large language models within Azure AI

As expected, new large language models (LLMs) and feature updates were announced, including the general availability of OpenAI’s latest frontier model, GPT-4o, via Azure OpenAI Service as an API. Microsoft also announced updates to its own small language models in Azure AI, with the general availability of its Phi-3 open models: Phi-3-small, Phi-3-mini, and Phi-3-medium. Through Datadog’s existing Azure integration, Datadog users can already monitor performance, usage, and costs of their Azure OpenAI API.

Beyond new frontier models, Microsoft announced the general availability of Azure AI Studio, a new platform for developing and embedding generative AI into application architectures. With the cost and complexity implications of generative AI, Datadog has been laser-focused on helping organizations deliver on these generative AI experiences in their production applications via our observability and security offerings. Datadog LLM Observability, now generally available, enables companies to continuously monitor and troubleshoot the AI-specific components of their stack in production. For best practices, see how Datadog monitors machine learning (ML) models in production and managed ML platforms.

Bringing AI capabilities to the database

In line with innovations across large and small language models, there were several updates focused on helping customers modernize their data estate to deliver better AI experiences.

A couple announcements in this area that stood out were the general availability of the Azure Database for PostgreSQL Azure AI extension and the preview of built-in vector database capabilities for Azure CosmosDB for noSQL. These managed Azure databases are enabling organizations to deliver low-latency, high-availability AI application experiences across relational and non-relational workloads. Datadog Database Monitoring is a critical product for Azure customers who need deep visibility into database performance across all on-prem and managed Azure database services. Database Monitoring supports self-hosted and managed cloud versions of PostgreSQL, MySQL, Oracle, and SQL Server.

And with Datadog Azure Monitoring, customers are already able to monitor performance and set up high-signal alerts for Azure CosmosDB and other critical managed database services in Azure.

Increasing developer productivity

To support developers in building new AI apps, Microsoft announced several new features across Azure App Service, Azure Kubernetes Service, Azure Container Apps, and Azure Functions. One update for Azure App Service was the preview of Sidecar patterns, which enables customers to add extra monitoring and logging capabilities to applications without having to make changes in the code. Before Build, we published a step-by-step guide with Microsoft explaining how to use Sidecars to integrate Datadog with your .NET applications hosted on Linux App Service.

As an Azure Native ISV Service, Datadog has continued to improve monitoring capabilities of these services, with customers today taking advantage of 40+ Datadog-generated metrics and dozens of new tags for their Azure services. For example, this year, we streamlined Azure container monitoring with our Datadog Azure Kubernetes Service cluster extension.

Datadog was featured in two Build breakout sessions relevant to developer productivity: “Accelerate DevOps & Incident Management workflows with Microsoft Teams” and “Using AI with App Service to deploy differentiated web apps and APIs”. Datadog was also mentioned during the day one keynote along with other companies solving problems for DevOps professionals.

See you next year

This was just a short summary of the new features and updates that excited us at Microsoft Build this year. For more announcements from Build, visit the Microsoft Build 2024 Book of News. And be sure to look out for more great content from us about how you can build on Azure. We hope to see you at Build next year.