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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
DASH 2024: Guide to Datadog's newest announcements
2024-06-26 · via Datadog | The Monitor blog

At this year’s DASH, we announced new products and features that enable your team to observe your environment, secure your infrastructure and workloads, and act to remediate problems before they affect customers. LLM Observability, which enables you to get deep visibility into your generative AI applications, is now generally available. The Datadog Agent now includes an embedded OTel Collector to provide native support for OpenTelemetry. With Data Security, you can discover sensitive data in your cloud data stores. You can ensure cost efficiency and workload performance with Datadog Kubernetes Autoscaling. And the next generation of Bits AI autonomously investigates incidents to help accelerate your remediation.

In this post, we recap these and other announcements from our keynote at DASH 2024. And read our other roundup posts to see what’s new from Datadog in the areas of:

Observe

Monitor, troubleshoot, and secure your generative AI applications with LLM Observability

The development of LLM agents and chain-based LLM application architectures that rely on pre-trained models like GPT, Claude, and Gemini has helped many organizations more effectively adapt generative AI for their use cases. But running these complex LLM workflows in production and at enterprise scale presents many challenges and risks, particularly when it comes to diagnosing errors, evaluating model performance, and security. Datadog LLM Observability enables users to trace their LLM apps in order to diagnose errors across every chain component, evaluate functional performance, identify drifts in prompt topics and responses, mitigate prompt injections and personally identifiable information (PII) leakage, find sources of latency, and more. LLM Observability is now generally available—to easily monitor your LLMs in production, you can add it to your Datadog account. To learn more, read our blog post.

LLM Observability trace view showing an error.

Unify your OpenTelemetry and Datadog experience with the embedded OTel Collector in the Agent

OpenTelemetry and Datadog are better together. That’s why the Datadog Agent now embeds a fully configurable OpenTelemetry (OTel) Collector, enabling users to take advantage of Datadog’s industry-leading observability solutions while accessing the complete capabilities of the OTel Collector. Users can also easily manage their fleet of embedded OTel Collectors with Datadog Fleet Automation and onboard faster with unified tagging. With Datadog’s enterprise-grade reliability and resources—including regular vulnerability scans, best practices, and prompt Agent updates—alongside community-managed OTel Collector releases, users can quickly troubleshoot configuration and software issues.

To learn more or request access, read our blog post or fill out this form.

Collect OTel data with the OTel Collector in the Datadog Agent

Take enhanced control of your log data with Log Workspaces

Delving into logs can be a matter of urgency for security, operations, and development teams, but it can also be a cumbersome task. Modern systems and applications churn out logs from countless sources, and these logs structure data in inconsistent and frequently unpredictable ways. As a result, when it comes to analysis, teams often turn to poorly integrated and highly specialized tooling. To help organizations take greater control of their logs, we’re pleased to introduce Datadog Log Workspaces. Building on the powerful capabilities offered by the Datadog Log Explorer, which helps teams swiftly navigate enormous volumes of log data, Log Workspaces enables anyone in your organization to parse, enrich, and analyze log data from any number of sources in clear and declarative terms using SQL, natural language, and Datadog’s visualizations. Log Workspaces is now in Preview. You can request access here, or learn more in our blog post.

Log Workspaces provides a suite of tools in a fluid, collaborative environment for delving deeper into your logs.

Fix production bugs efficiently with Datadog Live Debugging

Production bugs demand immediate attention and often force you to shift to an alternate set of tools and processes to investigate, disrupting your development flow. Now, Datadog Live Debugging lets you maintain your flow and fix bugs efficiently. Live Debugging brings production context into your IDE, so you can see the values of local variables, quickly reproduce bugs locally, and easily generate integration tests to prevent a regression. Read more in our blog post and request access to the Preview here.

A screenshot of the Live Debugging feature shows the summary of an error, a flame graph, and an automatically generated integration test.

Make data-driven UX design decisions with Product Analytics

To understand any aspect of user behavior—from adoption and conversion rates to usage patterns and flows—you need to ground your insights in real user data. With Datadog Product Analytics, you can easily dig into user data from across your application and tailor your analyses based on the scope of your projects. You can visualize data on user engagement and interactions through a variety of features, including Heatmaps, Sankey, and Session Replay, helping you quickly assess your UX from multiple angles. To learn more about Product Analytics, check out our blog post and request access to the Preview here.

The summary page in Product Analytics, with the sidebar expanded to show the other Product Analytics features.

Secure

Detect vulnerabilities in minutes with Agentless Scanning for Cloud Security Management

In order to improve the security posture of their infrastructure and achieve compliance, security teams need to scan their entire production environment for vulnerabilities. But having to deploy an agent-based solution brings challenges to getting started quickly and reaching full coverage. Agentless Scanning, now generally available, enables development, security, and operations teams to get started using Datadog Cloud Security Management (CSM) to detect and remediate vulnerabilities across their cloud infrastructure in minutes. Learn more about Agentless Scanning in our blog post.

Datadog CSM with Agentless Scanner findings

Discover sensitive data in your cloud data stores with Data Security

Securing personally identifiable information (PII) in the cloud—such as credit card numbers and login credentials—is essential for avoiding breaches and maintaining compliance standards. Datadog Data Security, now available in Preview, automatically pinpoints sensitive data in your AWS S3 buckets and RDS instances and helps you fix security issues affecting these cloud resources. By scanning your cloud environment for data that matches the rules determined by Sensitive Data Scanner, Data Security shows you which of your data stores contain PII and whether there are any security issues associated with these resources, so you can remediate them as soon as possible. Learn more in our blog post and request access to the Preview here.

Datadog Data Security

Quickly find and fix misconfigured cloud resources in one click with infrastructure-as-code remediation

Today, teams have to fix misconfigured cloud resources directly through the console, or go through a long process of creating a ticket and waiting for the underlying infrastructure-as-code (IaC) to be fixed by the engineering team. The first option creates drift, which makes the situation worse. The second option is ideal, but can take time, during which your environment remains vulnerable. Now, once Datadog Cloud Security Management detects a misconfiguration, you can deploy a remediation with Datadog’s one-click IaC remediation, all from a centralized platform. One-click IaC remediations are now available in Datadog CSM. See our documentation to get started.

Infrastructure-as-Code Remediation

Detect and fix code-level vulnerabilities in production with Datadog Code Security

For security, development, and operations teams struggling with application security visibility, complexity, and actionable insights into production systems, Datadog Code Security offers a seamless solution that detects real code vulnerabilities in production environments by continuously monitoring your applications at runtime. With a unique, production-ready interactive application security testing (IAST) approach, Datadog Code Security enables DevOps and security teams to identify and prioritize the most critical vulnerabilities before they become costly breaches, all while providing actionable insights and recommended fixes. For more details and to get started, see our blog post and documentation.

Datadog Code Security automatically detects code-level vulnerabilities in production

Automate risk reduction in your software supply chain with Datadog SCA

Modern cloud-native applications include a large proportion of open source code, which increases security risks. Manually implementing open source risk reduction practices is error-prone and can consume a large amount of time and resources. By using a combination of integrations that cover the entire software development lifecycle, Datadog SCA analyzes the open source and third-party components in your software applications to find vulnerabilities, malware, and other issues, including licensing and projects that follow poor hygiene. Now, customers will find any detected SCA risks in the Library Issues explorer and see all the attributes for each library in the ASM Library Catalog.

To learn more, check out our blog post and documentation.

Datadog SCA Library Risks

Act

Scale your Kubernetes workloads automatically from Datadog

The vast majority of Kubernetes workloads are overprovisioned—as a result, rightsizing your workloads has the potential to deliver significant savings. However, balancing cost efficiency with cluster performance can be challenging. Datadog Kubernetes Autoscaling provides multi-dimensional rightsizing for your applications without impacting stability, with automation to easily manage your entire footprint and visibility into the Datadog telemetry backing each recommendation. Check out our blog post to learn more and request access to the Preview here.

Optimization recommendations for a Kubernetes cluster displayed alongside cost and memory metrics.

Simplify incident response with Change Tracking on monitor status pages

Most incidents are triggered by changes. When a responder is troubleshooting an incident, one of the first questions they ask is, “Has anything changed recently?”

Datadog Change Tracking streamlines incident response by surfacing relevant changes and potential remediation steps from within the monitor status page. This experience, now in Preview, enables quick identification and resolution without leaving the monitor status page.

Change Tracking currently tracks:

  • Deployments
  • Feature-flag changes
  • Watchdog Insights (faulty deploys, errors, etc.)
  • Traffic anomalies
  • Database schema changes (for Database Monitoring customers)
  • Kubernetes pod crashes

For more information, see our documentation.

Accelerate incident remediation with autonomous investigations by Bits AI

Last year, we introduced Bits AI, a generative AI-based chat interface capable of answering your observability and security questions. Today, we’re excited to announce the next evolution of Bits AI. Bits AI can now autonomously perform complex operational tasks such as investigating alerts and coordinating incidents. This latest version of Bits AI runs alongside you, anticipates your needs, and takes steps without requiring you to constantly prompt it with questions. Bits AI’s autonomous investigation capabilities are now available in Preview. For more details, see our blog post.

Bits AI performing autonomous activity in case management

Enrich your on-call experience with observability data using Datadog On-Call

On-call engineers often need to navigate multiple tools and resources to effectively monitor and resolve high-stakes issues, which can quickly lead to burnout and inefficiency. Datadog On-Call seamlessly integrates monitoring, paging, and incident response onto one platform, enabling users to review pages alongside relevant observability data and important service and team ownership details to quickly triage alerts. With Datadog On-Call, organizations can also implement intuitive scheduling and escalation policies to easily manage on-call rotations and distribution of duties. And with detailed analytics, teams can access key page metrics such as the average response time for an alert to identify inefficiencies and ensure quicker time to resolution for future issues.

To learn more about Datadog On-Call, check out our blog, or request access to the Preview.

Enrich your on-call experience with observability data using Datadog On-Call