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cPacket Unveils High-Performance Packet Delivery

cPacket Networks introduces new packet delivery and capture platforms, designed to meet the evolving needs of AI-powered network observability and security monitoring, both on-premise and in hybrid cloud environments.

The cVu 32400AG strengthens cPacket's packet delivery offerings by addressing customers' growing requirements for peak performance, analytics and AI-powered network observability.

Mark Grodzinsky, Chief Product and Marketing Officer of cPacket, said, "Our zero-downtime enterprise customers require solutions that are not only powerful and efficient but also cost-effective and easy to manage. With our new packet delivery system, we're delivering on those promises, enabling customers to harness the full potential of their network infrastructure while reducing overhead and unlocking advanced capabilities like 400G."

The cVu 32400AG expands on the AG product line to include 400G support, allowing customers to enable packet brokering at line-rate with high port density in a small footprint. This is the first product in the cPacket portfolio to support 400G, helping to round out cPacket’s portfolio with network observability solutions that are both efficient and future-proof.

The cVu 32400AG offers advanced microburst detection capabilities and allows NetOps teams to detect and address network congestion issues in real-time, ensuring the reliability and performance of mission-critical applications. For instance, applications requiring high performance and low latency, such as high-frequency trading and AI processing, often encounter millisecond bursts that impact immediate operations and are critical considerations for future network capacity planning and AI compute efficiency.

In addition to new releases in the cVu family, cPacket has expanded its partnership with AWS to include the cStor-V platform on AWS Marketplace. The cStor-V virtual appliance runs as a native EC2 instance in AWS to deliver high-performance cloud packet capture and network analytics. It integrates seamlessly with native AWS VPC Traffic Mirroring services to easily point out-of-band, replicated packet streams to cStor-V for continuous and on-demand capture scenarios. cStor-V can also generate enterprise grade network analytics to quickly troubleshoot network issues, identify security incidents, and examine key protocol performance.

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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 Unveils High-Performance Packet Delivery

cPacket Networks introduces new packet delivery and capture platforms, designed to meet the evolving needs of AI-powered network observability and security monitoring, both on-premise and in hybrid cloud environments.

The cVu 32400AG strengthens cPacket's packet delivery offerings by addressing customers' growing requirements for peak performance, analytics and AI-powered network observability.

Mark Grodzinsky, Chief Product and Marketing Officer of cPacket, said, "Our zero-downtime enterprise customers require solutions that are not only powerful and efficient but also cost-effective and easy to manage. With our new packet delivery system, we're delivering on those promises, enabling customers to harness the full potential of their network infrastructure while reducing overhead and unlocking advanced capabilities like 400G."

The cVu 32400AG expands on the AG product line to include 400G support, allowing customers to enable packet brokering at line-rate with high port density in a small footprint. This is the first product in the cPacket portfolio to support 400G, helping to round out cPacket’s portfolio with network observability solutions that are both efficient and future-proof.

The cVu 32400AG offers advanced microburst detection capabilities and allows NetOps teams to detect and address network congestion issues in real-time, ensuring the reliability and performance of mission-critical applications. For instance, applications requiring high performance and low latency, such as high-frequency trading and AI processing, often encounter millisecond bursts that impact immediate operations and are critical considerations for future network capacity planning and AI compute efficiency.

In addition to new releases in the cVu family, cPacket has expanded its partnership with AWS to include the cStor-V platform on AWS Marketplace. The cStor-V virtual appliance runs as a native EC2 instance in AWS to deliver high-performance cloud packet capture and network analytics. It integrates seamlessly with native AWS VPC Traffic Mirroring services to easily point out-of-band, replicated packet streams to cStor-V for continuous and on-demand capture scenarios. cStor-V can also generate enterprise grade network analytics to quickly troubleshoot network issues, identify security incidents, and examine key protocol performance.

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 ...