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
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...