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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Real user monitoring (RUM) measures how an application performs for people using it in real conditions. For websites, browser-side instrumentation can report performance, interactions, and errors from actual visits, helping teams see how experience varies across devices, browsers, networks, and locations.
What does real user monitoring measure?
RUM observes the user-facing side of an application. A browser agent or other client instrumentation gathers selected signals during real use and sends them for analysis. The exact fields depend on the product and its configuration; RUM does not automatically capture every possible user action or performance detail.
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Performance and rendering
Depending on the implementation, collected timings can include page navigation and resource loading, time to first byte (TTFB), and browser milestones such as domInteractive and domComplete. Browser APIs including Navigation Timing, Resource Timing, Paint Timing, and User Timing can provide additional measurements. Some RUM products also report Core Web Vitals such as Largest Contentful Paint (LCP) and Interaction to Next Paint (INP).
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Interactions, errors, and sessions
RUM may connect performance data to user interactions, network requests, client-side errors, or session-level views. This can help a team investigate whether a slow page or failed request is associated with a particular view or journey. Data models differ: for example, Splunk describes traces, spans, sessions, interactions, and users as entities in its browser RUM model. Its documentation says a trace can start with a user action and that identity mapping requires manual instrumentation.
How does RUM differ from synthetic monitoring?
RUM records what happens during actual use, so it reflects the mix of devices, browsers, networks, and locations that real users bring. Synthetic monitoring runs scripted checks in a controlled environment, where a team can specify variables such as browser, device, network, geography, and cache state.
| Approach | What it observes | Best suited to |
|---|---|---|
| Real user monitoring | Experience reported by actual users’ clients, with real-world variation. | Understanding field experience and longer-term trends. |
| Synthetic monitoring | Repeatable scripted journeys under defined conditions. | Regression checks and investigating short-term issues during development. |
The methods complement each other: RUM shows how the application behaves in the field, while synthetic checks make it possible to repeat a defined test consistently. Neither alone provides the whole picture.
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Why do teams use RUM?
- Find performance problems that affect particular pages, resources, or segments of actual traffic.
- See how experience varies across the browsers, devices, networks, and locations people use.
- Connect a reported problem to related interactions, requests, errors, or session context when the chosen tool collects those signals.
- Track user-experience measures over time rather than relying only on a controlled test environment.
What should teams check before enabling RUM?
Client telemetry can contain sensitive or identifying information depending on what is instrumented. URLs, session identifiers, interaction events, and approximate geographic information may be collected. Before rollout, review the agent’s configuration and data handling rather than assuming all RUM products collect or retain the same fields.
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- Are sensitive values excluded or masked, and how are identifiers created or mapped?
- What retention and access controls apply, and do they meet your privacy requirements?
- Can browser events be connected to backend traces, and what data is exported to other systems?
As one product-specific example, Splunk documents that its browser agent uses beacon-connection IP addresses to derive geography such as country or city, then drops those IP addresses within six hours. It also says identity data is not collected by default, though manual instrumentation can map traces to identifiers. These details describe Splunk’s documented behavior, not a universal RUM rule.
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Where does OpenTelemetry fit?
OpenTelemetry (OTel) is an open-source, vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry such as traces, metrics, and logs. It can be relevant when teams want interoperability across observability systems, but OTel does not guarantee that every RUM service supports the same browser signals, conventions, or exports. OpenTelemetry’s documentation update of August 29, 2025 reported support from more than 90 observability vendors; that is a dated project figure, not a current vendor count.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to compare when choosing a RUM approach
Compare implementations against the needs of your application, rather than assuming “RUM” means a fixed feature set. Useful questions include which timings, interactions, errors, and session details are captured; which web or mobile platforms are supported; how data can be segmented and diagnosed; and what privacy controls, backend trace links, export options, sampling limits, and pricing apply. Current pricing and limits vary by provider and plan.
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