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Catchpoint Introduces Cloud Nodes and Updates Enterprise Nodes

Catchpoint further expanded its network of monitoring location options with Cloud Nodes, which monitors in multiple regions of the six major cloud providers, and an updated Enterprise Nodes offering, for fast-start monitoring from any location.

These solutions address the challenges of ensuring stellar performance (speed, reachability, reliability, and availability) of digital services when cloud migrations, agile and continuous development are the norm. They can be used standalone or as part of a holistic approach utilizing Catchpoint’s internet backbone, broadband, last mile, and wireless monitoring infrastructure, now comprising more than 700 vantage points worldwide, the largest and most diverse monitoring network in its category.

Catchpoint now has 111 Cloud Nodes deployed on the six major cloud providers: 16 in AWS, 28 in Azure, 17 in Google, 20 in IBM; plus in China, 15 in Alibaba and 15 in Tencent. Cloud Nodes now cover 62 cities in 25 countries worldwide, the largest regional cloud provider coverage available.

“A huge and growing amount of internet traffic flows to, from, and between cloud service providers, making the cloud a pivotal element in the delivery chain,” says Mehdi Daoudi, CEO of Catchpoint. “Our expansive Cloud Nodes service measures the speed and availability of digital services from the vantage point of the cloud, supplementing our backbone and broadband monitoring which canvases the broader internet infrastructure. Catchpoint helps IT operations correlate their monitoring goals with business results by focusing on end-user experience and customer journeys. These actionable insights help our customers detect suboptimal experiences from anywhere in real time.”

Catchpoint’s Enterprise Nodes offering has been updated to include a new Linux distribution and a consumption-based option. This product is available as either agentless software or as a portable device which can be installed in a datacenter, office, or literally anywhere customers are located. Utilizing Catchpoint’s advanced synthetic monitoring, with over 20 pre-packaged monitor types, Enterprise Nodes are designed to accelerate resolution of problems inside the firewall. A node can also be deployed within a datacenter or a cloud environment to monitor network connectivity and application performance.

Enterprise Nodes include a new custom monitor feature which allows organizations to deploy their own monitoring code and reuse existing scripts to monitor virtually any type of application or service (VoIP, SNMP, Databases, LDAP, Twitter Sentiment, etc.). Enterprise Nodes include robust security features and can be installed and fully functional in minutes.

“Close proximity to end-users is critical for accurate monitoring of their digital experiences. Catchpoint’s monitoring infrastructure is the industry’s broadest in terms of sheer number, geographic expanse, amount of data generated (synthetic and real user), and node types,” says Daoudi. “Our customers have an unsurpassed range of flexibility and options to meet their monitoring needs, including our unique ability to offer insight into cloud or other third-party elements, moving them toward optimal availability and performance.”

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 ...

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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 ...

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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.

Catchpoint Introduces Cloud Nodes and Updates Enterprise Nodes

Catchpoint further expanded its network of monitoring location options with Cloud Nodes, which monitors in multiple regions of the six major cloud providers, and an updated Enterprise Nodes offering, for fast-start monitoring from any location.

These solutions address the challenges of ensuring stellar performance (speed, reachability, reliability, and availability) of digital services when cloud migrations, agile and continuous development are the norm. They can be used standalone or as part of a holistic approach utilizing Catchpoint’s internet backbone, broadband, last mile, and wireless monitoring infrastructure, now comprising more than 700 vantage points worldwide, the largest and most diverse monitoring network in its category.

Catchpoint now has 111 Cloud Nodes deployed on the six major cloud providers: 16 in AWS, 28 in Azure, 17 in Google, 20 in IBM; plus in China, 15 in Alibaba and 15 in Tencent. Cloud Nodes now cover 62 cities in 25 countries worldwide, the largest regional cloud provider coverage available.

“A huge and growing amount of internet traffic flows to, from, and between cloud service providers, making the cloud a pivotal element in the delivery chain,” says Mehdi Daoudi, CEO of Catchpoint. “Our expansive Cloud Nodes service measures the speed and availability of digital services from the vantage point of the cloud, supplementing our backbone and broadband monitoring which canvases the broader internet infrastructure. Catchpoint helps IT operations correlate their monitoring goals with business results by focusing on end-user experience and customer journeys. These actionable insights help our customers detect suboptimal experiences from anywhere in real time.”

Catchpoint’s Enterprise Nodes offering has been updated to include a new Linux distribution and a consumption-based option. This product is available as either agentless software or as a portable device which can be installed in a datacenter, office, or literally anywhere customers are located. Utilizing Catchpoint’s advanced synthetic monitoring, with over 20 pre-packaged monitor types, Enterprise Nodes are designed to accelerate resolution of problems inside the firewall. A node can also be deployed within a datacenter or a cloud environment to monitor network connectivity and application performance.

Enterprise Nodes include a new custom monitor feature which allows organizations to deploy their own monitoring code and reuse existing scripts to monitor virtually any type of application or service (VoIP, SNMP, Databases, LDAP, Twitter Sentiment, etc.). Enterprise Nodes include robust security features and can be installed and fully functional in minutes.

“Close proximity to end-users is critical for accurate monitoring of their digital experiences. Catchpoint’s monitoring infrastructure is the industry’s broadest in terms of sheer number, geographic expanse, amount of data generated (synthetic and real user), and node types,” says Daoudi. “Our customers have an unsurpassed range of flexibility and options to meet their monitoring needs, including our unique ability to offer insight into cloud or other third-party elements, moving them toward optimal availability and performance.”

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.