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Virtual Instruments Becomes Virtana and Introduces CloudWisdom

Virtual Instruments has become Virtana, a company focused on providing customers with robust AI-powered hybrid IT infrastructure management platforms for mission-critical workloads.

Underscoring this commitment to helping enterprises manage their complex multi-cloud environments, Virtana introduced CloudWisdom, a SaaS-based cloud cost optimization and monitoring platform.

CloudWisdom helps customers plan, analyze and optimize their cloud workloads, services and resources. Along with the new name, the company has advanced its business model to include self-service SaaS and downloadable trial versions of its solutions.

Virtana has re-invented hybrid IT infrastructure management around a core understanding of applications and their relationship to the infrastructure – from the data center to the public cloud. Virtana’s VirtualWisdom, WorkloadWisdom and new CloudWisdom platforms form the foundation of next generation hybrid infrastructure management. Virtana platforms enable dramatically accelerated problem resolution; efficient management of capacity and cost; and intelligent automated workload placement.

“Connecting the dots between the health and performance of individual data center and public cloud resources with their exact impact on application SLAs and performance is the holy grail for enterprises embarking on their digital transformation journey,” said Torsten Volk, Senior Analyst, Enterprise Management Associates. “Virtana is combining its heritage of highly granular real-time application infrastructure monitoring with advanced machine learning and analytics capabilities from the Metricly (formerly Netuitive) acquisition, to ultimately offer comprehensive monitoring and policy-driven capacity optimization and automated problem resolution. This approach is becoming more and more relevant in the light of hybrid applications consisting of traditional monolithic enterprise apps that are extended through modern microservices.”

With the introduction of the CloudWisdom platform for cloud monitoring and cost analysis, Virtana’s hybrid IT infrastructure management portfolio encompasses the full range of cloud deployment models – including public, private, hybrid and multi-cloud environments. Tightly integrated with VirtualWisdom, the CloudWisdom platform features full-stack, cloud-native monitoring from a single pane of glass. CloudWisdom monitors middleware including databases, messaging platforms, microservices, and containers, as well as dozens of leading cloud infrastructure services. The platform includes machine intelligence-based capacity and performance analysis combined with continuous cost analysis and optimization based on workload demands.

CloudWisdom complements Virtana’s Cloud Migration Readiness (CMR) service, which provides enterprises with vital performance and cost insights into the workload behavior of applications targeted for public cloud migration by leveraging both workload analysis and workload simulation in the cloud.

VirtualWisdom, for the first time, is now available in a 30-day free trial version. The trial version offers 30 days of collection and 30 days of analysis and includes a Configuration Wizard; automated infrastructure and application discovery; the full portfolio of integrations for compute, network and storage, as well as installation documents and access to training videos.

“The future of IT operations is autonomous and hybrid. By providing a next generation, AI-powered hybrid infrastructure management platform, we are enabling IT operations to be more efficient and automated than ever,” said Philippe Vincent, CEO of Virtana. “Virtana’s goal is to help customers modernize and grow by embracing a new approach to monitoring, analysis and automation. By providing customers with deep infrastructure visibility through an app-centric, real-time approach, we’re delivering the foundation for intelligent automation and helping customers de-risk their journey to the hybrid cloud.”

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Virtual Instruments Becomes Virtana and Introduces CloudWisdom

Virtual Instruments has become Virtana, a company focused on providing customers with robust AI-powered hybrid IT infrastructure management platforms for mission-critical workloads.

Underscoring this commitment to helping enterprises manage their complex multi-cloud environments, Virtana introduced CloudWisdom, a SaaS-based cloud cost optimization and monitoring platform.

CloudWisdom helps customers plan, analyze and optimize their cloud workloads, services and resources. Along with the new name, the company has advanced its business model to include self-service SaaS and downloadable trial versions of its solutions.

Virtana has re-invented hybrid IT infrastructure management around a core understanding of applications and their relationship to the infrastructure – from the data center to the public cloud. Virtana’s VirtualWisdom, WorkloadWisdom and new CloudWisdom platforms form the foundation of next generation hybrid infrastructure management. Virtana platforms enable dramatically accelerated problem resolution; efficient management of capacity and cost; and intelligent automated workload placement.

“Connecting the dots between the health and performance of individual data center and public cloud resources with their exact impact on application SLAs and performance is the holy grail for enterprises embarking on their digital transformation journey,” said Torsten Volk, Senior Analyst, Enterprise Management Associates. “Virtana is combining its heritage of highly granular real-time application infrastructure monitoring with advanced machine learning and analytics capabilities from the Metricly (formerly Netuitive) acquisition, to ultimately offer comprehensive monitoring and policy-driven capacity optimization and automated problem resolution. This approach is becoming more and more relevant in the light of hybrid applications consisting of traditional monolithic enterprise apps that are extended through modern microservices.”

With the introduction of the CloudWisdom platform for cloud monitoring and cost analysis, Virtana’s hybrid IT infrastructure management portfolio encompasses the full range of cloud deployment models – including public, private, hybrid and multi-cloud environments. Tightly integrated with VirtualWisdom, the CloudWisdom platform features full-stack, cloud-native monitoring from a single pane of glass. CloudWisdom monitors middleware including databases, messaging platforms, microservices, and containers, as well as dozens of leading cloud infrastructure services. The platform includes machine intelligence-based capacity and performance analysis combined with continuous cost analysis and optimization based on workload demands.

CloudWisdom complements Virtana’s Cloud Migration Readiness (CMR) service, which provides enterprises with vital performance and cost insights into the workload behavior of applications targeted for public cloud migration by leveraging both workload analysis and workload simulation in the cloud.

VirtualWisdom, for the first time, is now available in a 30-day free trial version. The trial version offers 30 days of collection and 30 days of analysis and includes a Configuration Wizard; automated infrastructure and application discovery; the full portfolio of integrations for compute, network and storage, as well as installation documents and access to training videos.

“The future of IT operations is autonomous and hybrid. By providing a next generation, AI-powered hybrid infrastructure management platform, we are enabling IT operations to be more efficient and automated than ever,” said Philippe Vincent, CEO of Virtana. “Virtana’s goal is to help customers modernize and grow by embracing a new approach to monitoring, analysis and automation. By providing customers with deep infrastructure visibility through an app-centric, real-time approach, we’re delivering the foundation for intelligent automation and helping customers de-risk their journey to the hybrid cloud.”

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...