Catchpoint Systems announced the addition of over 50 monitoring nodes in the last quarter, bringing its total to more than 500 worldwide. This expansion of new monitoring locations focuses on China, where the company has already invested heavily, as well as Africa and Iran.
Catchpoint’s node coverage, driven by client demand to analyze and optimize digital user experiences, has the broadest geographic distribution of monitoring locations worldwide. The network now includes 394 backbone nodes, more than 60 of which are IPv6.
- New China nodes in Chengdu, Luoyang, Harbin, Yangzhou, Chongqing and Ningbo, bring Catchpoint’s in-China total to an industry-leading 45 monitoring locations.
- New nodes in Africa expand Catchpoint’s coverage beyond Northern and South Africa to Nigeria, Ghana, Tanzania, Kenya, Uganda and Botswana.
- Catchpoint also added its first monitoring nodes in Tehran, Iran.
“Our growth is driven by customer demand, with most telling us they need more global coverage,” says Mehdi Daoudi, CEO and Co-Founder of Catchpoint Systems, Inc. “By building the world’s largest node network, we can help companies future-proof their infrastructure investments. We’ll continue to invest in our network as customers’ needs evolve.”
Catchpoint’s network includes 60 IPv6 nodes so customers can test, monitor and validate their IPv6 deployments and strategies. The recent expansion also adds visibility from IBM Softlayer, in addition to Microsoft Azure, Amazon AWS and Google Cloud.
This enables companies to evaluate and make informed decisions on architectures, cloud provider selection, and monitoring from cloud providers to their origin datacenters and offices.
Catchpoint Systems’ digital performance analytics allow organizations to strengthen brands and enhance revenues by delivering exceptional user experiences. Catchpoint has the broadest geographic distribution of monitoring locations worldwide, analyzing the speed, availability and reliability of e-commerce sites and other online services. By combining this information with advanced analytics, Catchpoint customers can quickly and accurately find and fix the source of problems, often before they become apparent to users.
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.