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Virtana Acquires Zenoss

Virtana has acquired Zenoss, a provider of real-time IT service monitoring and AI-powered event intelligence. 

This strategic acquisition aims to bridge the gap between service visibility and infrastructure control in hybrid environments. The increased scale of Virtana and Zenoss will enable greater opportunity for accelerated investment and innovation.

The combined entity introduces a unified observability platform, empowering IT and risk management teams to trace incidents from business-service impact to underlying infrastructure behavior through a single, AI-powered lens. By correlating real-time user impact, service topology, and hybrid infrastructure telemetry, this integration enables customers to quickly pinpoint failures, performance issues, and cost drivers, eliminating blind spots and accelerating resolution.

"This acquisition isn't just an expansion of our capabilities, it's a strategic shift toward end-to-end control over hybrid infrastructure performance, cost, and risk — closing existing gaps in observability," said Paul Appleby, CEO and president of Virtana. "By bringing Zenoss' service-centric event intelligence together with Virtana Platform's deep infrastructure and cost analytics, we're eliminating the blind spots left by legacy monitoring tools. We're excited to join forces with Zenoss to continue Virtana's mission in driving innovation within observability."

Greg Stock, CEO of Zenoss, added, "Large enterprises, government institutions, and managed service providers rely on Zenoss to keep mission-critical services running. The combination of Zenoss and Virtana gives our customers and partners access to broader visibility, deeper infrastructure analytics, and a unified strategy for improving resilience, performance, and operational efficiency. This is a huge day for Zenoss — we're thrilled to be joining the Virtana team."

Why the Combination Matters:

  • Unified Observability - The combination of Virtana and Zenoss creates the first platform to provide Full-Stack Observability from top-level services to the deepest level infrastructure. Virtana offers deep insights from over 16,000+ metrics for infrastructure and container observability, while Zenoss provides enhanced event intelligence for hundreds of out-of-the-box infrastructure and application services.
  • AI-Driven Resilience - Combines Zenoss' model-informed AIOps with real-time IT service mapping, event correlation, and real-time topology visualization with Virtana's agentic AI and machine-learning models for capacity and cost optimization. These capabilities expand intelligent automation and accelerate predictive root-cause analysis and remediation.
  • Proactive Prevention - While some system disruptions are caused by external cyberattacks, most result from internal failures—misconfigurations, capacity limits, cascading service degradation, or delayed detection. The combined platform helps IT and operations teams surface issues earlier, understand their impact faster, and take action before they disrupt business continuity.
  • Operational Efficiency - Customers have achieved up to 95% faster MTTR and 99.999% uptime by combining early warning signals with AI-driven event intelligence that accelerates root-cause analysis, enabling IT leaders to fix problems faster, forecast risk, prioritize investments, and justify spend with data-backed precision.
  • Market Position - A hybrid observability platform for on-premises infrastructure, legacy data center workloads, containerized microservices, and multicloud resources, eliminating tool sprawl and simplifying platform consolidation and modernization strategies.
  • Accelerated Innovation Road Map - Combined R&D capabilities fast track forthcoming features, including automated remediation and optimization for AI and ephemeral workloads, all within the Virtana Platform.

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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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 ...

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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.

Virtana Acquires Zenoss

Virtana has acquired Zenoss, a provider of real-time IT service monitoring and AI-powered event intelligence. 

This strategic acquisition aims to bridge the gap between service visibility and infrastructure control in hybrid environments. The increased scale of Virtana and Zenoss will enable greater opportunity for accelerated investment and innovation.

The combined entity introduces a unified observability platform, empowering IT and risk management teams to trace incidents from business-service impact to underlying infrastructure behavior through a single, AI-powered lens. By correlating real-time user impact, service topology, and hybrid infrastructure telemetry, this integration enables customers to quickly pinpoint failures, performance issues, and cost drivers, eliminating blind spots and accelerating resolution.

"This acquisition isn't just an expansion of our capabilities, it's a strategic shift toward end-to-end control over hybrid infrastructure performance, cost, and risk — closing existing gaps in observability," said Paul Appleby, CEO and president of Virtana. "By bringing Zenoss' service-centric event intelligence together with Virtana Platform's deep infrastructure and cost analytics, we're eliminating the blind spots left by legacy monitoring tools. We're excited to join forces with Zenoss to continue Virtana's mission in driving innovation within observability."

Greg Stock, CEO of Zenoss, added, "Large enterprises, government institutions, and managed service providers rely on Zenoss to keep mission-critical services running. The combination of Zenoss and Virtana gives our customers and partners access to broader visibility, deeper infrastructure analytics, and a unified strategy for improving resilience, performance, and operational efficiency. This is a huge day for Zenoss — we're thrilled to be joining the Virtana team."

Why the Combination Matters:

  • Unified Observability - The combination of Virtana and Zenoss creates the first platform to provide Full-Stack Observability from top-level services to the deepest level infrastructure. Virtana offers deep insights from over 16,000+ metrics for infrastructure and container observability, while Zenoss provides enhanced event intelligence for hundreds of out-of-the-box infrastructure and application services.
  • AI-Driven Resilience - Combines Zenoss' model-informed AIOps with real-time IT service mapping, event correlation, and real-time topology visualization with Virtana's agentic AI and machine-learning models for capacity and cost optimization. These capabilities expand intelligent automation and accelerate predictive root-cause analysis and remediation.
  • Proactive Prevention - While some system disruptions are caused by external cyberattacks, most result from internal failures—misconfigurations, capacity limits, cascading service degradation, or delayed detection. The combined platform helps IT and operations teams surface issues earlier, understand their impact faster, and take action before they disrupt business continuity.
  • Operational Efficiency - Customers have achieved up to 95% faster MTTR and 99.999% uptime by combining early warning signals with AI-driven event intelligence that accelerates root-cause analysis, enabling IT leaders to fix problems faster, forecast risk, prioritize investments, and justify spend with data-backed precision.
  • Market Position - A hybrid observability platform for on-premises infrastructure, legacy data center workloads, containerized microservices, and multicloud resources, eliminating tool sprawl and simplifying platform consolidation and modernization strategies.
  • Accelerated Innovation Road Map - Combined R&D capabilities fast track forthcoming features, including automated remediation and optimization for AI and ephemeral workloads, all within the Virtana Platform.

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.