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cPacket cCloud Visibility Suite Available in Microsoft Azure Marketplace

The cCloud Visibility Suite from cPacket Networks is now available in the Microsoft Azure Marketplace.

The cPacket cCloud Visibility Suite is a feature-rich, multi-cloud solution that enables enterprises and cloud providers to build highly secure, reliable, and scalable hybrid networks. cCloud Visibility Suite provides a range of network packet-data-based services for end-to-end observability and security, giving always-on network intelligence for the IT network and security operations teams.

The proprietary cCloud solution has advanced processing features which capture, pre-process, and deliver accurate network packet data in real-time to security, performance management, analytics, and AIOps solutions, simplifying overall network observability. This integrated solution provides several benefits for organizations looking to accelerate their cloud migration by enabling faster deployment of virtual appliances, increased scalability, service agility, enhanced user experience, and strengthened security and compliance postures.

“A cloud-smart strategy is crucial to enterprises as they drive their digital transformation and AI initiatives. I am excited that cPacket Networks and Microsoft have taken our partnership forward. Through the Microsoft Azure Marketplace, our customers around the globe can easily access and acquire cCloud with increased scalability, streamlined licensing, and unparalleled visibility of their cloud network infrastructure,” Dennis Carpio, Head of Business Development and Alliances, cPacket Networks.

The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Microsoft Azure. All cPacket hybrid-cloud observability products are orderable today in the Azure marketplace and in production across many high-profile customer environments in the financial services, technology, government, and healthcare sectors. You can also use qualified MACC (Microsoft Azure Consumption Commitment) dollars to purchase cCloud for an even more streamlined process.

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 ...

cPacket cCloud Visibility Suite Available in Microsoft Azure Marketplace

The cCloud Visibility Suite from cPacket Networks is now available in the Microsoft Azure Marketplace.

The cPacket cCloud Visibility Suite is a feature-rich, multi-cloud solution that enables enterprises and cloud providers to build highly secure, reliable, and scalable hybrid networks. cCloud Visibility Suite provides a range of network packet-data-based services for end-to-end observability and security, giving always-on network intelligence for the IT network and security operations teams.

The proprietary cCloud solution has advanced processing features which capture, pre-process, and deliver accurate network packet data in real-time to security, performance management, analytics, and AIOps solutions, simplifying overall network observability. This integrated solution provides several benefits for organizations looking to accelerate their cloud migration by enabling faster deployment of virtual appliances, increased scalability, service agility, enhanced user experience, and strengthened security and compliance postures.

“A cloud-smart strategy is crucial to enterprises as they drive their digital transformation and AI initiatives. I am excited that cPacket Networks and Microsoft have taken our partnership forward. Through the Microsoft Azure Marketplace, our customers around the globe can easily access and acquire cCloud with increased scalability, streamlined licensing, and unparalleled visibility of their cloud network infrastructure,” Dennis Carpio, Head of Business Development and Alliances, cPacket Networks.

The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Microsoft Azure. All cPacket hybrid-cloud observability products are orderable today in the Azure marketplace and in production across many high-profile customer environments in the financial services, technology, government, and healthcare sectors. You can also use qualified MACC (Microsoft Azure Consumption Commitment) dollars to purchase cCloud for an even more streamlined process.

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 ...