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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
Datadog named Leader in 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring
2025-10-29 · via Datadog | The Monitor blog
Yanbing Li

Yanbing Li

We are thrilled to announce that, for the second consecutive year, Datadog has been named a Leader in the 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring. We believe that this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack.

Gartner, Magic Quadrant for Digital Experience Monitoring, Padraig Byrne, Pankaj Prasad, Martin Caren, D.B. Cummings, Matt Crossley, Tanmay Bisht, October 2025
Gartner, Magic Quadrant for Digital Experience Monitoring
Gartner, Magic Quadrant for Digital Experience Monitoring, Padraig Byrne, Pankaj Prasad, Martin Caren, D.B. Cummings, Matt Crossley, Tanmay Bisht, October 2025

Datadog’s unified observability and security platform breaks down silos across frontend, backend, and infrastructure—bringing business, DevOps, SRE, and security teams together to understand and optimize critical user journeys.

By providing deep visibility into user interactions and application performance, Datadog enables organizations to deliver consistent, reliable experiences across every touchpoint. As digital environments grow more complex, our AI-powered platform helps organizations stay ahead by automatically detecting anomalies, surfacing user friction through dead click tracking, and uncovering behavioral patterns with heatmaps. With integrated incident management and on-call workflows, teams can resolve issues faster and shift from reactive firefighting to proactive optimization to deliver digital experiences that strengthen customer satisfaction and business performance.

  • Real User Monitoring (RUM) provides full-fidelity visibility into every session, enabling accurate troubleshooting and performance insights while using dynamic retention filters to control costs.
  • Product Analytics empowers teams to make smarter product decisions by turning real user data, like adoption trends, user journeys, and in-app behavior, into quantitative insights that guide prioritization and improve digital experiences.
  • Session Replay captures user interactions and performance data across web and mobile, recreating sessions for end-to-end troubleshooting. Paired with RUM or Product Analytics, it connects technical performance to real user behavior, helping teams resolve issues faster and prioritize product decisions with richer insights.
  • Synthetic Testing and Monitoring integrates synthetic tests directly into your CI pipelines to catch issues early, ensuring your applications are issue-free before reaching end users.
  • Error Tracking automatically groups and surfaces critical errors to enable rapid issue resolution across your entire technology stack for more efficient troubleshooting.
  • Datadog’s AI engine provides automated alerts, insights, and root cause analyses that draw from observability data across the entire Datadog platform for faster resolution and improved user experiences.
  • Application Performance Monitoring and Software Catalog integrate seamlessly with RUM to provide a comprehensive view of your application, enhancing collaboration, incident management, and real-time insights with end-to-end service ownership.
  • Feature Flags give teams an integrated way to safely and confidently manage feature delivery, with real-time observability to track performance, detect regressions, and protect user experience. Sign up for the Preview now.

As we celebrate our second consecutive year as a Leader in the Gartner® Magic Quadrant™ for Digital Experience Monitoring, we remain committed to helping our customers stay ahead of complexity and deliver exceptional digital experiences. With AI-powered intelligence, scalable data collection, and built-in cost controls, Datadog DEM continues to evolve so your teams can innovate faster and with confidence.

“It has been a long time since we’ve seen truly insightful data from customers. Datadog gave us that lens again,” said Mahmoud Abdel Gawad, Co-CEO at Ibnsina Pharma.

“Datadog allows us to measure the business impact of changes and have a single point of truth across all teams,” said Zbigniew Kamiński, head of SRE at Booksy.

We believe that customer feedback is a core aspect of analyst reports, and we feel our recognition reflects the value that Datadog delivers to our users today along with our future investments across the platform. If you are a Datadog customer and would like to share your experience, you can share your review on Gartner Peer Insights™ here.

Complete the form to read a complimentary copy of the report.

Thank you!

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GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the US and internationally, and Peer Insights and MAGIC QUADRANT are registered trademarks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Datadog.

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