Virtual Instruments announced VirtualWisdom 5.4, the latest version of the company’s infrastructure performance monitoring and analytics platform.
By visualizing the infrastructure in the context of the application, VirtualWisdom enables organizations to accelerate digital transformation, improve business agility and proactively manage the cost and performance of their enterprise data centers.
The modern enterprise data center offers the promise of improved business agility in addition to providing a scalable foundation for an enterprise’s business-critical applications. However, the reality is that the scale and complexity associated with these highly virtualized, multi-vendor environments is beyond human comprehension. Legacy silo-centric monitoring tools only provide limited visibility into the various components of the underlying infrastructure. These tools have no understanding of how applications relate to infrastructure or the relative business value of the applications running on the infrastructure. As a result, application owners and line-of-business (LOB) managers aren’t aligned with the infrastructure teams on how to proactively assure application performance, control costs and reduce risk within their constantly changing data centers.
The solution lies in managing infrastructure from an application-centric point of view. The landmark new release of VirtualWisdom achieves this by holistically monitoring, analyzing and optimizing the performance, utilization and health of IT infrastructure within the context of the application. By discovering and mapping applications to the infrastructure, associating their business critically and applying self-learning-based analytics, VirtualWisdom enables enterprises to guarantee performance-based service level agreements (SLAs) for key stakeholders within the organization, including application owners, LOB owners and IT operations teams.
The new VirtualWisdom app-centric IPM platform is comprised of three key capabilities: Application Service Assurance; Workload and Capacity Optimization; and Problem Resolution and Avoidance. These are enabled by Virtual Instruments’ highly scalable wire and machine data instrumentation and app-centric analytics. The integrated capabilities provide deep infrastructure insights to every team relying upon the performance and availability of business-critical applications. As a result, the new release of VirtualWisdom enables proactive performance management and signals the beginning of the app-centric IPM era by establishing it as the best approach to managing the next generation data center.
“The research we’ve conducted indicates increasing infrastructure complexity is the primary inhibitor to enabling comprehensive, application-focused IT service delivery,” noted Steve Brasen, Research Director with IT industry analyst firm Enterprise Management Associates. “To meet rapidly evolving requirements for highly available and optimally performing IT services, organizations require holistic visibility across their entire IT ecosystems that analytically maps application performance directly to the underlying infrastructure and enables the dynamic placement of workloads. This latest release of VirtualWisdom enhances the platform’s app-centric IPM approach to deliver what is, to date, the most comprehensive IT infrastructure visibility attainable from a single pane of glass.”
Features and benefits of the new version of VirtualWisdom include:
Application Service Assurance analytics align infrastructure performance with application requirements by:
- Providing executive and LOB visibility through easy-to-use executive and application-level dashboards
- Enabling Tiered Service Level policies to assure the performance of business-critical applications running on shared infrastructure
- Discovering and mapping application usage of dynamic and virtualized infrastructure
Workload and Capacity Optimization analytics proactively manage workloads and capacity from the VM to the storage array by:
- Optimizing end-to-end workload placement across VM, network and storage
- Proactively detecting potential performance issues and optimization opportunities through seasonal behavior analytics
Problem Resolution and Avoidance analytics enable IT teams to proactively collaborate, troubleshoot and diagnose complex performance issues by:
- Offering Investigation Runbooks that provide guided analytics to identify and resolve issues for every alarm type, while enabling chat-ops to improve cross-team collaboration
- Detecting anomalies, and automatically comparing to performance baselines to detect and correlate potential root causes of issues
Scalable Instrumentation enables deep wire and machine data collection across the data center in real-time by:
- Adding deeper visibility of software defined data centers and Hyper-converged infrastructure including VX:Rails, Nutanix, Simplivity, vSAN, ScaleIO and Netflow
- Expanding high fidelity wire data support for NAS and SAN to include SMB and FCoE protocols, respectively
“As the leading provider of real-time monitoring solutions, we have an intimate understanding of the enormous challenges created when an enterprise lacks insight into the infrastructure supporting their business applications,” said Philippe Vincent, CEO of Virtual Instruments. “With the new release of VirtualWisdom, we’re able to remove the anxiety our customers felt by ‘flying blind’ with their business-critical applications. By leveraging VirtualWisdom to take an app-centric approach to the management of their infrastructures, IT operations and architecture teams can collaboratively work with their application owners and business unit executives to proactively optimize the performance and cost of the supporting infrastructure. This increases business agility and the overall value of the infrastructure to the business.”
VirtualWisdom 5.4 is available at the end of December.
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 ...