Netuitive announced its latest release, Netuitive 5.5, the leading predictive analytics software that forecasts, identifies and resolves IT issues before they impact quality of service.
Built for large enterprises, Netuitive 5.5 is a robust performance management platform for mission-critical applications running in physical, virtual and cloud infrastructures. New product features help IT organizations eliminate virtual server sprawl, optimize server consolidation ratios, enable long term capacity trending, and improve the manageability of virtualized applications.
Key 5.5 additions include:
Flexible One-View Dashboard – Provides a unified performance view of the virtualized data center. Visualizes Workload, Health and Capacity of the virtual data center to provide rich cross-platform insight in a single screen.
Chargeback (and “Showback”) Reports – Enables IT to provide detailed application utilization and cost analysis reports to business managers. Aligns new pay-per-use models with the financial systems and decision processes of the business.
Support for Multi-hypervisor environments – New integration with Microsoft Hyper-V increases flexibility and choice. (VMware support already available).
Netuitive 5.5 reduces the risk of enterprise-level virtualization by automating and simplifying the complex task of monitoring and managing mission-critical applications running in heterogeneous IT environments. Netuitive 5.5 reduces IT management costs, increases flexibility for changing business needs, and enables more efficient management of application workloads in physical, virtual and cloud infrastructures.
Powered by its patented Behavior Learning Engine, Netuitive’s predictive analytics software is a major advancement in managing the performance of applications and their underlying infrastructures. Netuitive eliminates manual, rules-based approaches with advanced mathematics and predictive analytics that automatically correlates and self-learns the operational behavior of systems and applications across an entire IT environment. By taking this holistic approach, it improves visibility across platforms and vendors, and because its learning is adaptive, it excels in dynamic, virtualized environments.
Other new features and enhancements in 5.5 include:
Expanded Storage Support – New integration with EMC Celerra adds to existing NAS storage monitoring options such as NetApp.
Expanded Virtualized Data Center Reports – (sample highlights)
- Storage Utilization and Forecast Report - Projects storage utilization trends 30-60-90 days out
- Inventory Summary Report -- Clusters, hosts, VMs, VM/Host ratio
- Enhanced Cluster Forecast – Capacity index added to trend workload
“In preparation for deploying a private cloud, organizations must get the management of their virtual data center under control,” says Donna Scott, Gartner vice president and distinguished analyst. “Predictive analytics enabled by Behavior Learning technology is critical for real-time performance visibility and diagnosis required to optimize the user experience. Predictive analytics should be part of any risk mitigation strategy as organizations evolve from virtualization management to cloud computing and real-time infrastructure.”
“Netuitive 5.5 further increases our focus on managing performance of mission-critical applications by ensuring performance of the underlying IT infrastructure -- be it physical, virtual or cloud,” says Nicola Sanna, CEO of Netuitive. “By maximizing visibility across platforms and silos we are empowering application and business owners to maximize value from virtual infrastructures while increasing confidence in managing their mission-critical applications.”
Netuitive’s customers include eight of the world’s 10 largest banks and several global telecommunications firms. They rely on Netuitive to predict degradations and avoid outages for their most critical applications. One global telco reported in a Gartner case study that it is using Netuitive to analyze more than a million metrics simultaneously allowing it to eliminate 3,480 hours annually in service degradation representing a business savings of $18 million.
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 ...