CloudPhysics launched a new release of its SaaS offering, significantly expanding its analytics solution so that VMware users can preempt and eliminate operational hazards that threaten to disrupt IT operations and applications.
“In spite of the abundance of tools available, many IT teams continue to wrestle with siloed or fragmented perspectives in managing and optimizing changes to their virtual infrastructures,” said Dennis Drogseth, VP at Enterprise Management Associates. “CloudPhysics has introduced a new, data-driven approach directed at vSphere environments for proactively planning and effecting changes across virtualized environments with an eye to minimizing risks and maximizing both IT and infrastructure efficiencies.”
With the new features, CloudPhysics delivers unique, meaningful insights, giving vSphere teams the confidence to act boldly to reduce risk and waste without compromising safety of the virtual infrastructure or the applications it supports. Building on its ease of use, intuitive user interface and deep visibility across multiple vCenters, CloudPhysics now:
- Reduces disruption and incidents with always-on diagnostics that surface hot spots and emerging problems, enabling admins to get ahead of nascent performance problems
- Improves mean-time-to-resolution (MTTR) with directed exploration, enabling admins to zero in on root cause and resolve application disruptions more quickly
- Generates insights for realigning misconfigured infrastructure to prevent future performance and availability issues and improve efficiency
Included in this release are:
- Always-on diagnostics: Continuous diagnosis of infrastructure with changes continuously captured, recorded and reflected. Unique data derivations, correlations, mashups and filters reduce “noise” and identify true hazards.
- Configurable dashboards: Rich contextual views expose hot spots and potential risks before problems form and impact operations. Trending analysis consolidates multiple objects and views, enabling multi-dimensional correlation.
- Groundbreaking exploration capabilities: Interactive ability to analyze changes over time through easily manipulated exploration mode, using time slices with zoom in/out capabilities to evaluate correlations and causation. Users can “correlate in context” to troubleshoot application disruptions with data drawn from VM performance/resource consumption; change/event log; configuration history; and known issues associated with operational hazards and best practices.
- 20+ new analytics for managing health and preempting hazards, available in our extensive library of “cards.”
- Platform innovation: Time series data is uniquely handled by the CloudPhysics platform to enable a user to analyze multiple dimensions of the infrastructure around the same time axis.
“Dealing with unforeseen system disruptions kills IT productivity, and the most common ‘cure’ – to add more hardware – simply adds costs and masks the underlying problem,” said John Blumenthal, CloudPhysics VP of Product Management. “Our unique insights and exploration capabilities are the fastest, easiest way for IT to get ahead of potential risk in their infrastructure, using an intuitive solution that uses predictive analytics to spot trouble early, guide to the root cause, and ultimately tune your infrastructure to prevent recurring hazards.”
All new features are included in CloudPhysics Premium Edition for vSphere, available on an annual subscription basis. All features are now available except exploration mode, which is currently in limited release and will be generally available later this year.
Users can get started with CloudPhysics Free Edition, which includes select Premium features.
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.