cPacket Networks announced a network monitoring solution that provides wirespeed network monitoring at 100Gbps (100 gigabits per second) under all traffic conditions.
The new cPacket Cx4100 solution also monitors the network at the wire, virtually eliminating the risk of false positives or negatives, or of dropped packets — issues that afflict legacy systems, such as packet brokers and packet capture NICs, which both require backhauling of network traffic for analysis.
The Cx4100 benefits from cPacket’s distributed architecture design, which brings monitoring to multiple points throughout the network — where monitoring is most critical — unlike solutions that provide centralized monitoring.
The Cx4100 solution is geared to the needs of monitoring for compliance, capacity planning, and troubleshooting on high-speed networks. In fact, research by Infonetics predicts that 100Gbps will account for approximately two thirds of all bandwidth deployed in service provider networks by 2018. For service providers and enterprise data centers alike, engineers and network architects today need to monitor at 100Gbps for capacity planning, utilization, or regulatory compliance. Financial service firms, and firms in other highly regulated industries, also are under pressure to ensure regulatory compliance.
cPacket’s Cx4100 answers these industry challenges with a next-generation monitoring architecture that outperforms centralized legacy monitoring products by bringing intelligence directly to the wire, reducing latency, and eliminating false positives and negatives. Network engineers can now truly monitor 100G links at millisecond resolution, enabling true capacity planning and wirespeed packet KPIs, as well as meeting regulatory monitoring requirements. What’s more, the Cx4100 provides users distributed, scalable capture of selected packets of interest, speeding troubleshooting and further decreasing time-to-resolution.
In addition, cPacket’s Cx4100 provides:
- High-performance spike detection (millisecond speeds at up to a thousand feeds) even at 100Gbps through hardware analytics
- Four 100Gbps interfaces with all interfaces provide monitoring at wirespeed
- Nanosecond-precision timestamping, which is critical in regulated environments
- Automated load balancing from 100Gbps down to 10Gbps
“Wirespeed network visibility is becoming an increasing challenge both for enterprises and service providers,” said Jim Duffy, Senior Analyst, 451 Research. “100G networks are fast becoming the new norm, and existing monitoring systems can’t keep up. cPacket’s Cx4100 pinpoints the urgency for a fundamental shift away from tools that backhaul data in order to perform diagnostics on it.”
The Cx4100 is the newest member of cPacket’s Integrated Monitoring Fabric, which includes cStor forensic storage for online storage and analytics; and includes the SPIFEE management dashboard, which provides a single-pain-of-glass design for monitoring and managing 1Gbps up through 100Gbps networks. The dashboard allows users to scale monitoring without re-architecting the networking or requiring retraining. In addition, the Cx4100 incorporates RESTful APIs, which ensure quick integration with third-party automation solutions.
“There’s effectively a new acid test for managing high-speed networks,” said Brendan O’Flaherty, CEO of cPacket Networks, "and that is the need to monitor network behavior without questioning the integrity of the data. Industry has reached a juncture where existing solutions can’t deliver on that guarantee. That’s because backhauling traffic to monitor and diagnose issues would have to defy the laws of physics to deliver real-time performance.”
The cPacket Cx4100 will be available in the second quarter of 2017.
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.