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The Importance of Real and Synthetic End User Monitoring

Dennis Rietvink

Organizations have many ways of ensuring that their systems are functioning properly. One of the most important things to measure, when assessing the performance of a system, is the end user experience.

Can users access the system quickly? Do they experience errors while accessing the system? Can they easily interact with the system across all the available channels? For the IT department, the answers to these questions determine whether or not the system is functioning properly. For the organization, they reveal the most important thing – whether or not their customers are happy, and are likely to continue using their services.

There are two ways to monitor user transactions and interactions with your website:

Real User Monitoring

This method uses a passive monitoring system, documenting all actions of users as they interact with your website. The feedback, generated in real time, is automatically assessed against established benchmarks, to correctly measure the quality of delivered services.

Real user monitoring systems have many advantages – you get to know exactly how visitors to your website experience all its features and applications, and how the website is performing for your end users in various geographic locations. The biggest problem with this method is that you won’t know about any website issues until at least one user gets to experience an existing problem.

Synthetic User Monitoring

This method simulates user experience on your website. It works by scripting typical user actions, and then simulates user click at regular intervals, to ensure that your website is responsive.

This method enables you to proactively catch any existing problems before your end users get to experience slow or unresponsive applications, or encounter other errors.

The obvious downside is that this method requires you to spend time scripting typical user actions. In addition, if your website changes frequently, you’ll need to periodically update your scripted scenarios.

In addition to websites, synthetic transactions can be used to monitor databases and TCP ports.

Organizations need a solution that can help recognize potential system problems by categorizing and visually presenting information concerning end user behavior and website performance in real time. In addition, such solution should also offer a way to script common user transactions and monitor the system’s performance 24x7.

End user monitoring reflects end user health, but doesn’t tell you the root cause of a problem. Linking end user monitoring data with application and infrastructure monitoring data enables organizations to determine the impact of a problem, rank its priority and quickly navigate to the root cause.

Dennis Rietvink is Co-Founder and VP of Product Management at Savision

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Importance of Real and Synthetic End User Monitoring

Dennis Rietvink

Organizations have many ways of ensuring that their systems are functioning properly. One of the most important things to measure, when assessing the performance of a system, is the end user experience.

Can users access the system quickly? Do they experience errors while accessing the system? Can they easily interact with the system across all the available channels? For the IT department, the answers to these questions determine whether or not the system is functioning properly. For the organization, they reveal the most important thing – whether or not their customers are happy, and are likely to continue using their services.

There are two ways to monitor user transactions and interactions with your website:

Real User Monitoring

This method uses a passive monitoring system, documenting all actions of users as they interact with your website. The feedback, generated in real time, is automatically assessed against established benchmarks, to correctly measure the quality of delivered services.

Real user monitoring systems have many advantages – you get to know exactly how visitors to your website experience all its features and applications, and how the website is performing for your end users in various geographic locations. The biggest problem with this method is that you won’t know about any website issues until at least one user gets to experience an existing problem.

Synthetic User Monitoring

This method simulates user experience on your website. It works by scripting typical user actions, and then simulates user click at regular intervals, to ensure that your website is responsive.

This method enables you to proactively catch any existing problems before your end users get to experience slow or unresponsive applications, or encounter other errors.

The obvious downside is that this method requires you to spend time scripting typical user actions. In addition, if your website changes frequently, you’ll need to periodically update your scripted scenarios.

In addition to websites, synthetic transactions can be used to monitor databases and TCP ports.

Organizations need a solution that can help recognize potential system problems by categorizing and visually presenting information concerning end user behavior and website performance in real time. In addition, such solution should also offer a way to script common user transactions and monitor the system’s performance 24x7.

End user monitoring reflects end user health, but doesn’t tell you the root cause of a problem. Linking end user monitoring data with application and infrastructure monitoring data enables organizations to determine the impact of a problem, rank its priority and quickly navigate to the root cause.

Dennis Rietvink is Co-Founder and VP of Product Management at Savision

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...