Cloupia, a cloud and data center management and automation provider, announced the general availability of a new module of the Cloupia Unified Infrastructure Controller (CUIC) that now supports the Cisco and NetApp FlexPod architecture.
Cloupia is a member of the NetApp Alliance Partner Program as a Cloud Management Partner.
Cisco and NetApp validated the CUIC for the FlexPod. The Cisco and NetApp FlexPod is a pre-tested shared infrastructure that combines Cisco networking, computing and NetApp storage to help channel partners accelerate and simplify their customers’ transition to the cloud. CUIC empowers enterprises and service providers to build and transform data center PODs into clouds. The CUIC simplifies and streamlines management by providing unified interface across all components.
“The Cloupia solution helps our customers to manage FlexPod as a holistic system by helping to reduce the complexity of provisioning TCO, OpEx and time to deliver services,” said Raju Penmetsa, CTO for Cloupia.
Cloupia Unified Infrastructure Controller provides comprehensive management and automation solution that integrates with the FlexPod management system with features that include: provisioning, monitoring and management for physical, virtual and cloud environments.
* Single-click infrastructure provisioning, including bare-metal state-less compute blade provisioning, allocating and mapping of shared storage (LUNs and volumes), and advanced network provisioning (ZONES) to support both physical and virtual workloads.
* Easy to install and implement a truly integrated out-of-box solution to begin resource deployments, monitoring/analytics and management of FlexPod within a few hours; alleviating the need for multiple tools, disparate bolt-on technologies and professional services.
* Automated and non-disruptive scaling of FlexPod– adding more compute, storage and network resources to meet the dynamic demands of the datacenter without disrupting current workloads.
* Management and real-time monitoring from a single pane of glass for each infrastructure component in FlexPod which including Cisco UCS, Cisco Nexus 5000, and NetApp FAS storage systems.
* Integrated cloud automation capabilities that include: end-user self-service portal, design component-level service catalog, and chargeback management that avoids additional tools
* Cloupia’s model based orchestrator enables IT administrators to customize and automate many infrastructure administrative and operational workflows with an easy to use workflow UI designer. Cloupia provides a rich built-in task library and out-of-box workflows that admins can further extend and customize.
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 ...