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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
This Month in Datadog - October 2024
2024-11-08 · via Datadog | The Monitor blog

On the October episode of This Month in Datadog, Jeremy Garcia (VP of Technical Community and Open Source) covers unified Error Tracking, Security Operational Metrics, and a new Datadog Serverless feature for retrying or redriving failed AWS Step Functions executions directly from Datadog. Later in the episode, Shri Subramanian (Group Product Manager) spotlights Datadog LLM Observability’s native integration with Google Gemini. Also featured are our blog posts Operator vs. Helm and How we used Datadog to save $17.5 million annually along with the 2024 State of Cloud Security report.

This Month in Datadog is a monthly update of the company’s latest features, product announcements, and more. Subscribe to our YouTube channel to get notifications about future episodes.

New features

View and search across RUM, APM, and Logs in one place with unified Error Tracking

Datadog Error Tracking groups similar errors together to reduce noise and help teams prioritize remediation efforts. Whereas users could search for browser, mobile, and backend issues separately, now unified Error Tracking enables you to view and search across RUM, APM, and Logs in one place. With this new feature, you get a single source of truth for errors across the front- and backend of your applications and services. Unified Error Tracking is available to all customers today. Check out the release note to learn more.

Assess a team’s effectiveness at responding to threats with Security Operational Metrics

Located on the pre-built Cloud SIEM Overview dashboard, Security Operational Metrics help you assess a team’s effectiveness at responding to threats by capturing their mean time to detect, acknowledge, and resolve. Additionally, you can create dashboards and monitors for these metrics and use tags to filter them by teams, sources, and environments. Check out Security Operational Metrics today on the Cloud SIEM overview dashboard and learn more by visiting our docs.

Retry or redrive failed Step Functions executions from Datadog

AWS Step Functions is a serverless orchestration service that enables you to create application workflows in the cloud. On top of using Datadog to monitor Step Functions, now you can retry executions or redrive them from failed steps from the Serverless UI. Not only does this feature reduce the need to manually document the Amazon Resource Names of failed executions, but this also reduces the time to remediate errors. Users who monitor Step Functions with Datadog can take advantage of this new feature today.

Datadog LLM Observability’s native integration with Google Gemini

Organizations today are racing to adopt LLMs and using a variety of models, such as Google Gemini and Azure OpenAI. Datadog LLM Observability’s native integration with Google Gemini automatically captures the LLM requests your application makes to Gemini’s model. And Datadog’s end-to-end tracing capabilities help you understand the behavior of LLM applications. This native integration is generally available today. Learn more about what Datadog LLM Observability can do by checking out the blog post.

Additional updates

  1. New App Builder blueprints for GitHub, Azure, AWS, and more
  2. Optimize Ruby garbage collection activity with Datadog
  3. Manage and monitor RUM apps with Service Catalog
  4. Apps Created by Datadog available in dashboards

See you next month

Check out our release notes for a full list of new features and updates. You can see these features and updates in action by logging onto the Datadog platform today or signing up for a 14-day free trial. We will see you next month.