Skip to main content

End User Monitoring 101: Tools - Part 1

Larry Haig

After establishing that End User Monitoring and Front End Optimization (FEO) are alive and well, I thought it would make sense to look at the current landscape of tools that can help in this area.

Application Performance Management (APM) tooling certainly has its place here, particularly for integrated, ongoing monitoring. However, it is probably useful to think of FEO as an extension activity, undertaken separately to the core KPI tracking and issue resolution supported by APM. I will reference APM tooling in the context of the various categories considered. I have split the tooling consideration into two posts: introduction & synthetic testing, and RUM (including mobile).

Let's start with a summary of available tool types (split into two parts), and then a structured FEO process. I am assuming an operations (rather than developer) centric approach. Certainly, the most robust approach to ensuring client side performance efficiency is to bake it in from inception, using established "Performance by Design" principles and cutting edge techniques. However, as in most cases "I wouldn't have started from here" is not exactly a productive recommendation, let's set the scene for approaches to understanding and optimizing the performance of existing web applications.

So, tooling. Any insights gained will start with the tools used. The choice will depend upon the technical characteristics of the target (e.g. traditional HTTP Website, Single Page Application, WebApp, Native Mobile App), and the primary objective of the test phase [the spectrum of (ongoing) Monitoring through to (point) Analysis].

The first hurdle is gaining appropriate visibility. However, it must be noted that any tool will produce data, the key is effective interpretation of the results. This is largely a function of knowledge and control of the test conditions.

So, what are the relevant categories of front end test tooling? The following does not seek to provide a blow-by-blow comparison of the multiplicity of competitors in each category – and in any case, the best choice for you will be determined by your own specific circumstances. Rather, it is a high level category guide. As a general rule of thumb, examples of each category will ideally be used to provide a broad insight into end user performance status and Front End Optimization. Modern APM tools increasingly tick many of these boxes, although some of the more arcane (but useful) details are yet to appear.

As we will see when considering process, FEO practice in Operations essentially consists of two aspects. One is understanding the outturn performance to external end points (usually end users). This is achieved through monitoring, that is, obtaining an objective understanding of transaction, page, or page component response from replicate tests in known conditions, or of site visitors over time.

Monitoring provides information relative to patterns of response of the target site or application, both absolute and relative to key competitors or other comparators.

The other aspect is Analysis of the various components delivered to the end user device. These components fall into three categories: static, dynamic, or logic (JavaScript code). Data for detailed analysis may be obtained as a by-product of monitoring, or from single or multiple point "snapshot" tests. Component analysis will be covered in a subsequent post.

Read End User Monitoring 101: Tools Part 2

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 ...

End User Monitoring 101: Tools - Part 1

Larry Haig

After establishing that End User Monitoring and Front End Optimization (FEO) are alive and well, I thought it would make sense to look at the current landscape of tools that can help in this area.

Application Performance Management (APM) tooling certainly has its place here, particularly for integrated, ongoing monitoring. However, it is probably useful to think of FEO as an extension activity, undertaken separately to the core KPI tracking and issue resolution supported by APM. I will reference APM tooling in the context of the various categories considered. I have split the tooling consideration into two posts: introduction & synthetic testing, and RUM (including mobile).

Let's start with a summary of available tool types (split into two parts), and then a structured FEO process. I am assuming an operations (rather than developer) centric approach. Certainly, the most robust approach to ensuring client side performance efficiency is to bake it in from inception, using established "Performance by Design" principles and cutting edge techniques. However, as in most cases "I wouldn't have started from here" is not exactly a productive recommendation, let's set the scene for approaches to understanding and optimizing the performance of existing web applications.

So, tooling. Any insights gained will start with the tools used. The choice will depend upon the technical characteristics of the target (e.g. traditional HTTP Website, Single Page Application, WebApp, Native Mobile App), and the primary objective of the test phase [the spectrum of (ongoing) Monitoring through to (point) Analysis].

The first hurdle is gaining appropriate visibility. However, it must be noted that any tool will produce data, the key is effective interpretation of the results. This is largely a function of knowledge and control of the test conditions.

So, what are the relevant categories of front end test tooling? The following does not seek to provide a blow-by-blow comparison of the multiplicity of competitors in each category – and in any case, the best choice for you will be determined by your own specific circumstances. Rather, it is a high level category guide. As a general rule of thumb, examples of each category will ideally be used to provide a broad insight into end user performance status and Front End Optimization. Modern APM tools increasingly tick many of these boxes, although some of the more arcane (but useful) details are yet to appear.

As we will see when considering process, FEO practice in Operations essentially consists of two aspects. One is understanding the outturn performance to external end points (usually end users). This is achieved through monitoring, that is, obtaining an objective understanding of transaction, page, or page component response from replicate tests in known conditions, or of site visitors over time.

Monitoring provides information relative to patterns of response of the target site or application, both absolute and relative to key competitors or other comparators.

The other aspect is Analysis of the various components delivered to the end user device. These components fall into three categories: static, dynamic, or logic (JavaScript code). Data for detailed analysis may be obtained as a by-product of monitoring, or from single or multiple point "snapshot" tests. Component analysis will be covered in a subsequent post.

Read End User Monitoring 101: Tools Part 2

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 ...