Catchpoint Systems announced Wireless 4G monitoring capabilities.
Deploying 25 new wireless nodes nationwide strengthens Catchpoint’s performance monitoring solution – enhancing its ability to meet organizations’ growing mobile performance needs.
“Our research shows that visibility into performance for mobile users is the key challenge for monitoring user experience for web applications. Catchpoint’s solution enables organizations to gain full visibility into web applications access through cellular networks and quickly identify a root cause of performance issues,” noted Bojan Simic, president and principal analyst, TRAC Research. “The solution also enables organizations to isolate the impact of carrier networks on performance and focus their efforts on areas that can improve the quality of user experience.”
Catchpoint empowers organizations to proactively monitor the end-to-end performance of their online and mobile services by delivering comprehensive, real-time analytics – improving speed, reliability and availability to boost customer satisfaction, lower the cost of quality management and protect revenue.
The wireless capabilities will allow organizations to better monitor mobile websites and web applications by giving them added visibility and control. After an intense testing period of nodes in major cities with AT&T and Verizon, Catchpoint now has live nodes with multiple carriers in Atlanta, Chicago, Dallas, Denver, Los Angeles, New York and Seattle, with nodes in Boston, San Francisco and Washington D.C. going live later this month.
In addition, organizations can now leverage Catchpoint’s wireless node capabilities to monitor non-mobile web applications, APIs used by mobile apps, mobile and digital outdoor advertising, mobile commerce and increasingly prominent machine-to-machine (M2M) technology.
“Consumers are increasingly relying on mobile devices as their application hubs or mobile hotspots for telecommuting, and millions of devices – from cars to vending machines – now wirelessly connect to internet networks to drive efficiency,” said Mehdi Daoudi, CEO and founder of Catchpoint. “With more than 130 backbone locations, 20 last-mile locations and now 25 4G nodes, we are committed to continually increasing our coverage capabilities and staying ahead of evolving industry needs so companies can keep their customers happy and the business running smoothly.”
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 ...