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Dynatrace Announces Enhanced Real User Monitoring to Unify Application Observability

Combines front-end telemetry with back-end context, empowering teams to better understand and optimize user experience

At Perform, its flagship annual user conference, Dynatrace announced next-generation Real User Monitoring (RUM) capabilities that deliver unified insights across modern web and mobile applications.

RUM has become essential to understanding how applications perform for real users. But as organizations shift to dynamic, cloud-native, and AI-driven architectures – including LLM-powered services – traditional RUM tools are struggling to keep pace. Legacy approaches often miss critical behaviors in single-page applications (SPAs), asynchronous rendering, and soft navigations, leaving gaps in understanding. These blind spots hinder developers, SREs, and application owners from analyzing user journeys, optimizing performance, and improving business outcomes.

The rapid growth of AI has also introduced new performance challenges, such as unpredictable workloads, latency spikes, and opaque model behavior, which legacy tools simply do not capture.

Dynatrace RUM is the only solution that unifies front-end telemetry with back-end context – logs, metrics, traces, topology, security events, and business data – within an agile platform powered by Grail™, Smartscape, and Dynatrace AI. This provides precise, end-to-end visibility, faster issue resolution, and smarter decision-making.

Key enhancements include:

  • Unified, Actionable Insights and Advanced Analytics: Query front-end performance and session data in the context of logs, metrics, spans, traces, and security events, all stored in Grail, an industry-leading unified data lakehouse that delivers precise, contextual insights from unified data. This connected view supports advanced use cases such as analyzing SPA rendering delays or AI-generated content performance.
  • Purpose-Built Apps for Developers: A dedicated interface that prioritizes grouped errors with end-to-end context, helping developers quickly identify trends and root causes. Apps like Error Inspector streamline troubleshooting and accelerate resolution.
  • Guided Observability Journeys: An intuitive user interface (UI) leads practitioners through problem flows, maintaining context across impacted entities, timeframes, and services. This reduces manual effort and speeds remediation.
  • Behavioral Analysis: Capture user interactions and soft navigations to understand how users move through AI-native and LLM‑enhanced This helps application owners surface experience issues even when backend performance appears healthy.
  • Extended Retention: Now in public preview, free-form analytics via DQL and retention of up to thirteen months enable deep investigations, compliance support, and historical trend analysis, surpassing competitive offerings.

“Capturing real user monitoring data and user interactions in the context of business data is a game-changer,” said Victoria Ruffo, Software Engineering Team Lead at FreedomPay. “Dynatrace RUM enables us to clearly see the performance and effectiveness of our most critical user journeys at the view level – not just pages and apps – so we can now act on insights that truly matter.”

“Dynatrace RUM allows customers to focus on what matters most, whether it’s degrading app performance for SREs, trending errors for developers, or abandoned sessions for support engineers,” said Steven Dickens, Founder and Principal Analyst at HyperFRAME Research. “By delivering RUM within a unified observability platform, Dynatrace eliminates the complexity of teams traversing multiple point solutions, and complements the experience with exploratory user journey analysis that includes out-of-the-box apps, notebooks and dashboards. This makes it easier than ever for teams to move from insight to action without switching tools.”

“Modern applications behave in highly dynamic and unpredictable ways, and teams need answers, not more manual analysis,” said Steve Tack, Chief Product Officer at Dynatrace. “Our next-generation RUM capabilities unify frontend experiences and backend context, automate insights, and help teams continuously validate and optimize how their applications perform for users. In the age of AI, success depends on intelligent automation and precise, real-time context, so teams can innovate more and deliver consistently great user experiences.”

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Dynatrace Announces Enhanced Real User Monitoring to Unify Application Observability

Combines front-end telemetry with back-end context, empowering teams to better understand and optimize user experience

At Perform, its flagship annual user conference, Dynatrace announced next-generation Real User Monitoring (RUM) capabilities that deliver unified insights across modern web and mobile applications.

RUM has become essential to understanding how applications perform for real users. But as organizations shift to dynamic, cloud-native, and AI-driven architectures – including LLM-powered services – traditional RUM tools are struggling to keep pace. Legacy approaches often miss critical behaviors in single-page applications (SPAs), asynchronous rendering, and soft navigations, leaving gaps in understanding. These blind spots hinder developers, SREs, and application owners from analyzing user journeys, optimizing performance, and improving business outcomes.

The rapid growth of AI has also introduced new performance challenges, such as unpredictable workloads, latency spikes, and opaque model behavior, which legacy tools simply do not capture.

Dynatrace RUM is the only solution that unifies front-end telemetry with back-end context – logs, metrics, traces, topology, security events, and business data – within an agile platform powered by Grail™, Smartscape, and Dynatrace AI. This provides precise, end-to-end visibility, faster issue resolution, and smarter decision-making.

Key enhancements include:

  • Unified, Actionable Insights and Advanced Analytics: Query front-end performance and session data in the context of logs, metrics, spans, traces, and security events, all stored in Grail, an industry-leading unified data lakehouse that delivers precise, contextual insights from unified data. This connected view supports advanced use cases such as analyzing SPA rendering delays or AI-generated content performance.
  • Purpose-Built Apps for Developers: A dedicated interface that prioritizes grouped errors with end-to-end context, helping developers quickly identify trends and root causes. Apps like Error Inspector streamline troubleshooting and accelerate resolution.
  • Guided Observability Journeys: An intuitive user interface (UI) leads practitioners through problem flows, maintaining context across impacted entities, timeframes, and services. This reduces manual effort and speeds remediation.
  • Behavioral Analysis: Capture user interactions and soft navigations to understand how users move through AI-native and LLM‑enhanced This helps application owners surface experience issues even when backend performance appears healthy.
  • Extended Retention: Now in public preview, free-form analytics via DQL and retention of up to thirteen months enable deep investigations, compliance support, and historical trend analysis, surpassing competitive offerings.

“Capturing real user monitoring data and user interactions in the context of business data is a game-changer,” said Victoria Ruffo, Software Engineering Team Lead at FreedomPay. “Dynatrace RUM enables us to clearly see the performance and effectiveness of our most critical user journeys at the view level – not just pages and apps – so we can now act on insights that truly matter.”

“Dynatrace RUM allows customers to focus on what matters most, whether it’s degrading app performance for SREs, trending errors for developers, or abandoned sessions for support engineers,” said Steven Dickens, Founder and Principal Analyst at HyperFRAME Research. “By delivering RUM within a unified observability platform, Dynatrace eliminates the complexity of teams traversing multiple point solutions, and complements the experience with exploratory user journey analysis that includes out-of-the-box apps, notebooks and dashboards. This makes it easier than ever for teams to move from insight to action without switching tools.”

“Modern applications behave in highly dynamic and unpredictable ways, and teams need answers, not more manual analysis,” said Steve Tack, Chief Product Officer at Dynatrace. “Our next-generation RUM capabilities unify frontend experiences and backend context, automate insights, and help teams continuously validate and optimize how their applications perform for users. In the age of AI, success depends on intelligent automation and precise, real-time context, so teams can innovate more and deliver consistently great user experiences.”

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Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...