
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
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...