Catchpoint Systems announces the launch of the Catchpoint OnPrem Agent, a portable on-premises software solution that monitors the availability and performance of infrastructure and applications from any location within an organization.
The Catchpoint OnPrem Agent provides flexible and robust location-based monitoring for organizations with widely distributed locations. It has the unique ability to be deployed anywhere in multiple formats and with options tailored for specific uses.
Customizable Features: The Catchpoint OnPrem Agent can run from a NUC that can fit in your hand, a rackable server, or a virtual machine with Windows or Linux operating systems. It also allows multiple tests to be run simultaneously, and stores up to three years of raw data for historical analysis. The Data Center version of the OnPrem Agent runs all synthetic tests now available on Catchpoint’s backbone nodes.
Use Case Options: Catchpoint offers OnPrem Agent options to suit the needs and budgets of specific uses. These include:
• 1st Mile/Data Center – provides a clean view of application performance in the data center, free of Internet “noise.”
• Branch Offices – supports office productivity in each location by tracking performance and connectivity to first party and cloud applications.
• Point-of-Sale – ensures customer satisfaction from brick-and-mortar stores.
• Call Centers – monitors performance of private applications needed to serve customers who contact the call center.
• Development and QA – improves quality by discovering problems during continuous development and testing.
“Our customers with distributed environments have been clamoring for an inside-the-firewall agent because of the need for more detailed information about their data centers and the applications they utilize,” says Mehdi Daoudi, CEO and co-founder of Catchpoint Systems. “Most customers that have deployed OnPrem agents have grown tired of maintaining these monolithic monitoring systems that were never meant to be distributed. They have also gotten used to the flexibility of a SaaS monitoring solution. Our challenge was to bring the full Catchpoint advantage to OnPrem with a product line that runs all synthetic tests our backbone nodes can, is flexible enough to easily install on any system, and with options built for the specific use cases required.”
Catchpoint also cites the increased reliance on both public and private cloud applications that’s developed over the past several years. “With the growth of the cloud, there is a corresponding need to ensure that those systems are running properly,” continues Daoudi. “In doing so, companies can ensure that their employees can remain productive and that their customers are not affected by subpar performance of these systems.”
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.