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10 Points to Consider When Choosing a Real User Monitoring Tool

Larry Haig

Tools enabling Real User Monitoring, or RUM (also known as End User Experience, or EUE, and some similar things) have proliferated hugely over the last 10 years, to the point there are now countless hopeful vendors jostling for contention.

RUM certainly adds a powerful additional perspective to external monitoring, and whilst it does not replace "traditional" active (synthetic) testing – they are complimentary – it can dramatically improve some aspects of end-user performance visibility, particularly for companies with a broad international reach.

As always, a crowded marketplace can contain sheep as well as goats. I thought that it may be useful to share some musings on RUM product distinctions. The relative importance of each will depend on particular circumstances, and there is a degree of functional convergence occurring, at least among the players who are carving out significant market share (and who clearly wish to be around for the long-term). Hopefully these will be helpful to the uninitiated who are considering a purchase decision.

Ten points to consider about your potential RUM tool (in no particular order):

1. Sophistication/Coverage

Many RUM products are based on the standard W3C Navigation metrics. Although provision of standard page load metrics is one of the drivers of growth in tooling options, be aware that these are not supported in all browsers, primarily older versions and Safari. In certain cases, basic performance data is collected from these to supplement the core W3C metrics and offer more complete coverage.

Key aspects of sophistication include:

- Ability to record user journeys (logical transactions). Less evolved products act at individual page level only.

- Ability to capture and report individual session-level data – reporting on business relevant metrics such as transaction abandonment and shopping cart conversion by different categories of user.

- Detailed reporting – bounce rate, stickiness (time on site), etc. A tabular comparison between candidate products may be useful here.

- Ability to record "above the line" performance (browser fill aka perceived render time). This metric is rarely estimated by active monitoring tools (the only one of which I am aware is WebPageTest). As such, RUM tooling supporting this give a useful additional perspective into end-user satisfaction. Beware of such metrics as "time to paint" which, whilst conceptually similar are of much less value – the key understanding is the point at which a given user regards the page as having loaded (filled the browser viewport).

2. Standalone or Integrated

RUM tooling operates by instrumenting site pages with a JavaScript beacon that writes back collected data following the unload step. The JavaScript may be deployed in one of two ways:

- By manual instrumentation of the site. This requires insertion of the JavaScript as high in the page headers as possible (unlike behavioral tags, a performance tag must be triggered early in the page download in order to time subsequent steps accurately). Depending upon the number of pages to be instrumented, it may be more practical to insert via a simple cut and paste operation or via an include statement. Manual instrumentation has the disadvantage of introducing an ongoing maintenance overhead for upgrading versions etc.

- By dynamic injection. Some tools offer the option of dynamically injecting the tag from the application server.

Integrated products offer RUM functionality as part of "end to end" visibility of traffic as a component of an Application Performance Management (APM) tool set. While typically involving greater investment, such products often offer better overall value through their enhanced "root cause" isolation ability.

3. Real-Time Reporting

Tools vary in two principal ways with regard to data handling:

- The duration of storage of captured data. As with other monitoring, the problem for vendors storing customer data is that they rapidly become data storage rather than monitoring companies. However, the ability to view trend data over extended periods is extremely useful, so individual vendor strategies to manage that requirement are relevant. This problem is exacerbated (for the vendor) if object level metrics are captured. Understand the options in this area.

- The frequency of update of customer data. This can vary between 24 hours and less than 5 minutes. Near real-time updates are relevant to active operations management, while daily information has limited historic value only.

4. All Traffic or Traffic Sampling

As RUM data is inferential in nature, it is important to capture all visitor traffic rather than a sample. Some tooling offers the option of user defined sampling, often to reduce license costs. This is unlikely to be good practice except possibly in the case of extremely high traffic sites. Within Europe, this situation is exacerbated by EU legislation enabling individual users to opt for "do not send" headers which restrict the transmission of tag based data, further limiting the overall coverage.

5. API

RUM tooling will always provide some output charting, etc. Additional value can be derived from integration of RUM data with outputs from other tooling. This is particularly so for those RUM products that do not report on session level data such as conversion and abandonment rates. In such cases, it is beneficial to combine such data from web analytics with RUM-based performance metrics.

6. Page or Object Level

Although, theoretically, all products could be extended to capture object level rather than page delivery metrics, in practice this tends to be restricted to the capture of specific individual objects (often for reasons associated with data handling as mentioned above).

7. User Event Capture

The ability to record the time between 2 events (e.g. mouse clicks). Such sub-page instrumentation is of value in supporting design and development decisions.

8. Extensibility

The ability to capture and integrate non-performance user data. Examples include: associating user login details with session performance, and collecting details of originating application or database server.

9. Reporting

Extent, type and customizability of in-built reporting. Various aspects include:

- Standard, inbuilt reports – extent and ease of use / comprehensibility.

- Custom reporting functions.

- Ease of data export - for manipulation / integration / display by external (e.g. dashboard) tools.

10. Investment

License models vary. Most are based on some combination of extent of visitor traffic (monthly or annual) and number of domains to be monitored. Some APM vendors include the cost of the RUM component within the overall agent license investment.

Although the importance of any particular aspect will vary depending upon precise use case and the nature of the application, hopefully the above will provide a useful checklist for initial engagement. Happy hunting!

Larry Haig is Senior Consultant at Intechnica.

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10 Points to Consider When Choosing a Real User Monitoring Tool

Larry Haig

Tools enabling Real User Monitoring, or RUM (also known as End User Experience, or EUE, and some similar things) have proliferated hugely over the last 10 years, to the point there are now countless hopeful vendors jostling for contention.

RUM certainly adds a powerful additional perspective to external monitoring, and whilst it does not replace "traditional" active (synthetic) testing – they are complimentary – it can dramatically improve some aspects of end-user performance visibility, particularly for companies with a broad international reach.

As always, a crowded marketplace can contain sheep as well as goats. I thought that it may be useful to share some musings on RUM product distinctions. The relative importance of each will depend on particular circumstances, and there is a degree of functional convergence occurring, at least among the players who are carving out significant market share (and who clearly wish to be around for the long-term). Hopefully these will be helpful to the uninitiated who are considering a purchase decision.

Ten points to consider about your potential RUM tool (in no particular order):

1. Sophistication/Coverage

Many RUM products are based on the standard W3C Navigation metrics. Although provision of standard page load metrics is one of the drivers of growth in tooling options, be aware that these are not supported in all browsers, primarily older versions and Safari. In certain cases, basic performance data is collected from these to supplement the core W3C metrics and offer more complete coverage.

Key aspects of sophistication include:

- Ability to record user journeys (logical transactions). Less evolved products act at individual page level only.

- Ability to capture and report individual session-level data – reporting on business relevant metrics such as transaction abandonment and shopping cart conversion by different categories of user.

- Detailed reporting – bounce rate, stickiness (time on site), etc. A tabular comparison between candidate products may be useful here.

- Ability to record "above the line" performance (browser fill aka perceived render time). This metric is rarely estimated by active monitoring tools (the only one of which I am aware is WebPageTest). As such, RUM tooling supporting this give a useful additional perspective into end-user satisfaction. Beware of such metrics as "time to paint" which, whilst conceptually similar are of much less value – the key understanding is the point at which a given user regards the page as having loaded (filled the browser viewport).

2. Standalone or Integrated

RUM tooling operates by instrumenting site pages with a JavaScript beacon that writes back collected data following the unload step. The JavaScript may be deployed in one of two ways:

- By manual instrumentation of the site. This requires insertion of the JavaScript as high in the page headers as possible (unlike behavioral tags, a performance tag must be triggered early in the page download in order to time subsequent steps accurately). Depending upon the number of pages to be instrumented, it may be more practical to insert via a simple cut and paste operation or via an include statement. Manual instrumentation has the disadvantage of introducing an ongoing maintenance overhead for upgrading versions etc.

- By dynamic injection. Some tools offer the option of dynamically injecting the tag from the application server.

Integrated products offer RUM functionality as part of "end to end" visibility of traffic as a component of an Application Performance Management (APM) tool set. While typically involving greater investment, such products often offer better overall value through their enhanced "root cause" isolation ability.

3. Real-Time Reporting

Tools vary in two principal ways with regard to data handling:

- The duration of storage of captured data. As with other monitoring, the problem for vendors storing customer data is that they rapidly become data storage rather than monitoring companies. However, the ability to view trend data over extended periods is extremely useful, so individual vendor strategies to manage that requirement are relevant. This problem is exacerbated (for the vendor) if object level metrics are captured. Understand the options in this area.

- The frequency of update of customer data. This can vary between 24 hours and less than 5 minutes. Near real-time updates are relevant to active operations management, while daily information has limited historic value only.

4. All Traffic or Traffic Sampling

As RUM data is inferential in nature, it is important to capture all visitor traffic rather than a sample. Some tooling offers the option of user defined sampling, often to reduce license costs. This is unlikely to be good practice except possibly in the case of extremely high traffic sites. Within Europe, this situation is exacerbated by EU legislation enabling individual users to opt for "do not send" headers which restrict the transmission of tag based data, further limiting the overall coverage.

5. API

RUM tooling will always provide some output charting, etc. Additional value can be derived from integration of RUM data with outputs from other tooling. This is particularly so for those RUM products that do not report on session level data such as conversion and abandonment rates. In such cases, it is beneficial to combine such data from web analytics with RUM-based performance metrics.

6. Page or Object Level

Although, theoretically, all products could be extended to capture object level rather than page delivery metrics, in practice this tends to be restricted to the capture of specific individual objects (often for reasons associated with data handling as mentioned above).

7. User Event Capture

The ability to record the time between 2 events (e.g. mouse clicks). Such sub-page instrumentation is of value in supporting design and development decisions.

8. Extensibility

The ability to capture and integrate non-performance user data. Examples include: associating user login details with session performance, and collecting details of originating application or database server.

9. Reporting

Extent, type and customizability of in-built reporting. Various aspects include:

- Standard, inbuilt reports – extent and ease of use / comprehensibility.

- Custom reporting functions.

- Ease of data export - for manipulation / integration / display by external (e.g. dashboard) tools.

10. Investment

License models vary. Most are based on some combination of extent of visitor traffic (monthly or annual) and number of domains to be monitored. Some APM vendors include the cost of the RUM component within the overall agent license investment.

Although the importance of any particular aspect will vary depending upon precise use case and the nature of the application, hopefully the above will provide a useful checklist for initial engagement. Happy hunting!

Larry Haig is Senior Consultant at Intechnica.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...