Skip to main content

Catchpoint Empowers IT Operations Teams with Advanced XLO Tracking for Superior User Experience

Catchpoint announced significant enhancements to its suite of Service Level Objective (SLO) capabilities, focusing on Experience Level Objectives (XLOs) monitoring. Unlike traditional SLOs that primarily measure system availability, the newly introduced XLOs prioritize metrics that gauge user experience and satisfaction. This innovative approach aims to provide IT operations teams with a more nuanced understanding of performance by aligning technical metrics with user-centric outcomes. 

Catchpoint has developed XLO monitoring to measure the following performance metrics:

 ■ Wait Time: The duration between the user’s request and the server’s initial response 

Response Time: The total time taken for the server to process a request and send back the complete response 

First Contentful Paint (FCP): The time it takes for the browser to render the first piece of content on the screen 

Largest Contentful Paint (LCP): Time when the largest content is visible within the browser 

Cumulative Layout Shift (CLS): A measure of how much the layout of the page shifts unexpectedly during loading 

Time to Interactive: The time it takes for a page to become fully interactive and responsive to user inputs 

Through the Catchpoint portal, companies can set specific performance objectives, such as ensuring the Largest Contentful Paint (LCP) does not exceed 2.5 seconds 90% of the time in a given month, which is imperative for maintaining a smooth user experience and reducing bounce rates. Catchpoint's platform tracks these objectives, showing weekly, monthly, and quarterly results. A “burndown” chart is also available to help you predict when you might exceed your XLO. This allows businesses to make informed decisions about their website's development and performance, ensuring they meet both availability and performance SLAs with their customers. By tracking changes over time, businesses can decide whether to release changes to their website based on their impact on Core Web Vitals. This proactive approach helps maintain high user satisfaction and ensures that performance objectives are met consistently.

The Latest

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

Catchpoint Empowers IT Operations Teams with Advanced XLO Tracking for Superior User Experience

Catchpoint announced significant enhancements to its suite of Service Level Objective (SLO) capabilities, focusing on Experience Level Objectives (XLOs) monitoring. Unlike traditional SLOs that primarily measure system availability, the newly introduced XLOs prioritize metrics that gauge user experience and satisfaction. This innovative approach aims to provide IT operations teams with a more nuanced understanding of performance by aligning technical metrics with user-centric outcomes. 

Catchpoint has developed XLO monitoring to measure the following performance metrics:

 ■ Wait Time: The duration between the user’s request and the server’s initial response 

Response Time: The total time taken for the server to process a request and send back the complete response 

First Contentful Paint (FCP): The time it takes for the browser to render the first piece of content on the screen 

Largest Contentful Paint (LCP): Time when the largest content is visible within the browser 

Cumulative Layout Shift (CLS): A measure of how much the layout of the page shifts unexpectedly during loading 

Time to Interactive: The time it takes for a page to become fully interactive and responsive to user inputs 

Through the Catchpoint portal, companies can set specific performance objectives, such as ensuring the Largest Contentful Paint (LCP) does not exceed 2.5 seconds 90% of the time in a given month, which is imperative for maintaining a smooth user experience and reducing bounce rates. Catchpoint's platform tracks these objectives, showing weekly, monthly, and quarterly results. A “burndown” chart is also available to help you predict when you might exceed your XLO. This allows businesses to make informed decisions about their website's development and performance, ensuring they meet both availability and performance SLAs with their customers. By tracking changes over time, businesses can decide whether to release changes to their website based on their impact on Core Web Vitals. This proactive approach helps maintain high user satisfaction and ensures that performance objectives are met consistently.

The Latest

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...