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cPacket Extends Ultra-Low-Latency Monitoring and Brokering Solution for 100Gbps Network Observability

cPacket Networks announced two new products designed to deliver ultra-low-latency, high-performance, high-density 100Gbps network observability in support of the latest enterprise automation, data center consolidation, and high-performance computing requirements.

Building on cPacket’s existing 100Gbps portfolio, the new cVu® 32100 and cVu 32100E network packet broker+ allow enterprises to acquire, aggregate, observe, and reliably deliver network packet data to IT performance and security tools. Equipped with advanced features with the right economics and scale, these new products provide the best solution for ultra-low-latency monitoring and packet brokering at the same time. This results in faster transaction velocity, better user experience, lower mean-time-to-resolution, and reduced customer churn.

“As we announced the new cStor® 100 appliance a few weeks ago, our financial services and other customers expect a complementing monitoring and brokering solution to meet their demands during the most challenging times. The new cVu 32100/E completes the solution and addresses key requirements in the most challenging environments across the most demanding industries,” said Brendan O’Flaherty, CEO of cPacket Networks.

Together, these products give organizations the deep, real-time, and actionable insights they need to align with industry trends around 100Gbps migration, high-performance but low-latency computing, data center consolidation, and digital transformation.

The cVu 32100/32100E packet brokers extend the role of the existing flagship cVu 16100NG packet broker+ in a scalable 2-tier monitoring fabric architecture by offering 32 ports of 100/40Gbps or 128 ports of 25/10Gbps in a compact 1RU size. The cVu 32100E (enhanced) is specifically designed for ultra-low-latency monitoring applications, delivering nanosecond timestamping with integrated real-time analytics, combined with wire-speed ingress and egress processing at each physical port for unrivaled accuracy, performance, and reliability.

- Built for high-performance computing, financial services, and other intensive workloads. Just like the cVu 16100NG packet broker+, the cVu 32100E packet broker+ is a unique 2-in-1 solution. It is a performance monitoring tool as well as a data delivery broker and reduces the costs and complexity of monitoring architectures. It includes specific additional features for performance monitoring of ultra-low-latency applications such as high-frequency trading, market data gap detection, medical digital imaging, 3D and video animation, healthcare, oil and gas exploration, pharmaceuticals, and other high-performance computing (HPC) applications. Those features include:

* Monitoring with “cBurst” microburst characterization for application-based network utilization, bandwidth capacity planning, and tool utilization

* High-resolution counters that provide full 1-second snapshots of throughput with millisecond resolution

* High-precision timestamping for consistent metrics analysis and fastest transaction velocity

* Cost-effective TAP/SPAN aggregation and multi-speed tool distribution, and key brokering features in a 2-tier architecture with the existing NG-series.

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I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

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Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

cPacket Extends Ultra-Low-Latency Monitoring and Brokering Solution for 100Gbps Network Observability

cPacket Networks announced two new products designed to deliver ultra-low-latency, high-performance, high-density 100Gbps network observability in support of the latest enterprise automation, data center consolidation, and high-performance computing requirements.

Building on cPacket’s existing 100Gbps portfolio, the new cVu® 32100 and cVu 32100E network packet broker+ allow enterprises to acquire, aggregate, observe, and reliably deliver network packet data to IT performance and security tools. Equipped with advanced features with the right economics and scale, these new products provide the best solution for ultra-low-latency monitoring and packet brokering at the same time. This results in faster transaction velocity, better user experience, lower mean-time-to-resolution, and reduced customer churn.

“As we announced the new cStor® 100 appliance a few weeks ago, our financial services and other customers expect a complementing monitoring and brokering solution to meet their demands during the most challenging times. The new cVu 32100/E completes the solution and addresses key requirements in the most challenging environments across the most demanding industries,” said Brendan O’Flaherty, CEO of cPacket Networks.

Together, these products give organizations the deep, real-time, and actionable insights they need to align with industry trends around 100Gbps migration, high-performance but low-latency computing, data center consolidation, and digital transformation.

The cVu 32100/32100E packet brokers extend the role of the existing flagship cVu 16100NG packet broker+ in a scalable 2-tier monitoring fabric architecture by offering 32 ports of 100/40Gbps or 128 ports of 25/10Gbps in a compact 1RU size. The cVu 32100E (enhanced) is specifically designed for ultra-low-latency monitoring applications, delivering nanosecond timestamping with integrated real-time analytics, combined with wire-speed ingress and egress processing at each physical port for unrivaled accuracy, performance, and reliability.

- Built for high-performance computing, financial services, and other intensive workloads. Just like the cVu 16100NG packet broker+, the cVu 32100E packet broker+ is a unique 2-in-1 solution. It is a performance monitoring tool as well as a data delivery broker and reduces the costs and complexity of monitoring architectures. It includes specific additional features for performance monitoring of ultra-low-latency applications such as high-frequency trading, market data gap detection, medical digital imaging, 3D and video animation, healthcare, oil and gas exploration, pharmaceuticals, and other high-performance computing (HPC) applications. Those features include:

* Monitoring with “cBurst” microburst characterization for application-based network utilization, bandwidth capacity planning, and tool utilization

* High-resolution counters that provide full 1-second snapshots of throughput with millisecond resolution

* High-precision timestamping for consistent metrics analysis and fastest transaction velocity

* Cost-effective TAP/SPAN aggregation and multi-speed tool distribution, and key brokering features in a 2-tier architecture with the existing NG-series.

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...