
Catchpoint unveiled its fall 2024 update, introducing a suite of substantial new features within its Internet Performance Monitoring platform.
To help enterprises optimize their website performance, Catchpoint has introduced several key updates:
WebPageTest Integration: WebPageTest has now been seamlessly incorporated into the Catchpoint IPM Platform. Customers enjoy a unified view of real-time web performance, covering everything from front-end performance to end user experience with insights from worldwide locations.
■ With AI-powered Smartboards, users can schedule tests, identify trends, and receive alerts for WebPageTest metrics, correlating these insights with real user data (RUM) and incorporating web vitals into Experience Level Objective (XLO) tracking.
■ Opportunities and experiments provide web performance optimization recommendations and no-code experiments assess the real-world impact of suggested optimizations before implementing on your live site.
■ WebPageTest video recordings and Core Web Vital highlights not only show the entire video of your critical user journeys; they also pinpoint focus areas where components impact the different Core Web Vitals.
Real User Monitoring (RUM): Catchpoint has enhanced its Real User Monitoring (RUM) with new metrics such as error clicks, thrashed cursor, dead clicks and rage clicks in addition to others like Interaction to Next Paint (INP). These metrics are unified into a simple Smartboard view, providing comprehensive insights into actual user interactions and overall application performance.
"As the Internet Stack plays a vital role in delivering exceptional digital experiences created by your application, an increasing number of businesses are relying on Catchpoint’s instrumentation as their main tool for monitoring end-user experience," says Mehdi Daoudi, CEO of Catchpoint. "They require visibility from the user's perspective to the code level and need to comprehend the entire pathway of their third-party integrations and dependencies to identify—and rectify—any disruptions to those experiences," he further explains.
■ Internet Stack Map enables automatic creation of custom topology service maps of key Internet Stack services and dependencies with AI simplification and correlation of Internet Sonar data providing a live and detailed view of everything impacting an application. Catchpoint has also added the correlation of Ping and Traceroute test types, as well as Ping RTT and Packet Loss Metrics throughout Stack Map. This improves your visibility of how network problems impact your service performance.
■ Internet Sonar detects outages in major services across the internet and correlates them with the services you’re monitoring in your Internet Stack. When incidents occur, this helps you to quickly answer “Is it me or something else?” Catchpoint has added the ability to filter Sonar overview and custom dashboards to a Stack Map. This improvement lets you see outages only for services represented in the Stack Map. If your Stack Map includes region filters, those are also represented in the filtered Sonar view. We have also enhanced ease of use with additional incident sidebar, multiple filtering options and alerts.
■ Catchpoint has added vantage points in 41 cities and across 37 ISPs. This brings the depth and breadth of their coverage to over 100 countries and over 300 cities, approaching 3000 vantage points across backbone, last mile, wireless, and cloud nodes.
■ Enterprise Nodes can be placed on customers' premises extending the full feature/function set of the largest observability network to anywhere there is an internet connection. In this release Catchpoint is now adding a light version of these nodes. Designed to be installed in remote offices, retail stores, and other facilities, these agents are extremely lightweight, flexible, and support a wider range of environments including Raspberry Pi to provide enterprises a path to easily monitor from locations with limited or no direct IT support from behind your firewall.
■ Node-to-node testing offers high-frequency, bidirectional monitoring from more locations, such as between datacenters and cloud providers, or from one cloud provider to another. Customers may run node-to-node tests between enterprise nodes or between enterprise and public nodes with frequencies as low as one minute
Catchpoint also released new automated capabilities to further enable Internet Performance Monitoring as code. Catchpoint supports several different ways to integrate Monitoring as Code in a CI/CD pipeline. Users can now create test configurations, run ad-hoc tests on new releases, and trigger automated actions through Catchpoint's REST API. Enhanced browser automation and testing with Playwright and Puppeteer allow users to handle complex web interactions effortlessly, with support for multiple windows and custom certificates. For example, we have customers who create new test configurations as new features are released using the REST API, tag deployments, run ad-hoc tests on new release builds, notify stakeholders or trigger actions using alert webhook or emails, and send all data to their own data warehouse to combine with other data sources for business KPI reporting. Playwright and Puppeteer scripting now support the use of Personal Certificates for URLs that require them.
“Catchpoint has had an incredible year of innovation in the IPM market. This is due to the true partnership we have developed with our customers and our relentless drive to reduce MTTR. Our focus to accelerate early identification of incidents is significantly enhanced with AI-powered capabilities such as root-cause analysis,” added Daoudi.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
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.