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ControlUp Insights Introduces Collective Analytics for Hybrid Clouds

ControlUp announced the release of ControlUp Insights, a new SaaS offering that provides collective analytics, interactive historical reports and control of hybrid cloud applications.

ControlUp’s collective analytics enables IT administrators, to not only compare their IT operations historically for their own baselines, but also to compare to similar IT infrastructures. This functionality is critical to help IT departments set realistic goals and contain costs.

“ControlUp Insights was designed to provide clear visibility and powerful control of hybrid cloud operations,” explained Asaf Ganot, Founder and CEO of ControlUp. “CIOs want to know how their infrastructure’s KPIs compare to similar organizations. Collective analytics are enormously helpful when it comes to setting goals and expectations, making informed purchasing decisions and knowing that operations are running optimally, as benchmarked against industry norms.”

ControlUp Real-time is used by more than 500 enterprise customers today to monitor, analyze and directly remediate problems with powerful dashboards, root cause analysis and an extensive library of community-sourced scripts that solve common issues IT administrators encounter.

With the launch of ControlUp Insights, the company now offers a platform that adds historical, interactive reports that empower IT personnel. Combined with collective analytics metrics, these reports ensure that IT investments are optimized, SLAs are met, and end users are kept fully productive.

ControlUp Insights capabilities include:

- Collective Analytics – Collective analytics provide IT administrators with the ability to compare their performance, availability and asset utilization with thousands of similar IT infrastructures to guide them in setting goals and expectations, making purchasing decisions and knowing that operations are running optimally.

- Live Reports - Simple to install, configure and use without needing professional services or a team of expert data scientists, relevant reports on hybrid cloud operations provide actionable insights right away. In its innovative GUI, hovering over a peak in a live report displays the top applications or top users of that resource to understand why the peak happened.

- Hybrid Clouds - ControlUp helps IT administrators know when it is time to move workloads out to the cloud and when they should be brought back in again. It works with applications that are run on physical Windows servers, in virtual machines or in private, public or hybrid clouds.

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.

ControlUp Insights Introduces Collective Analytics for Hybrid Clouds

ControlUp announced the release of ControlUp Insights, a new SaaS offering that provides collective analytics, interactive historical reports and control of hybrid cloud applications.

ControlUp’s collective analytics enables IT administrators, to not only compare their IT operations historically for their own baselines, but also to compare to similar IT infrastructures. This functionality is critical to help IT departments set realistic goals and contain costs.

“ControlUp Insights was designed to provide clear visibility and powerful control of hybrid cloud operations,” explained Asaf Ganot, Founder and CEO of ControlUp. “CIOs want to know how their infrastructure’s KPIs compare to similar organizations. Collective analytics are enormously helpful when it comes to setting goals and expectations, making informed purchasing decisions and knowing that operations are running optimally, as benchmarked against industry norms.”

ControlUp Real-time is used by more than 500 enterprise customers today to monitor, analyze and directly remediate problems with powerful dashboards, root cause analysis and an extensive library of community-sourced scripts that solve common issues IT administrators encounter.

With the launch of ControlUp Insights, the company now offers a platform that adds historical, interactive reports that empower IT personnel. Combined with collective analytics metrics, these reports ensure that IT investments are optimized, SLAs are met, and end users are kept fully productive.

ControlUp Insights capabilities include:

- Collective Analytics – Collective analytics provide IT administrators with the ability to compare their performance, availability and asset utilization with thousands of similar IT infrastructures to guide them in setting goals and expectations, making purchasing decisions and knowing that operations are running optimally.

- Live Reports - Simple to install, configure and use without needing professional services or a team of expert data scientists, relevant reports on hybrid cloud operations provide actionable insights right away. In its innovative GUI, hovering over a peak in a live report displays the top applications or top users of that resource to understand why the peak happened.

- Hybrid Clouds - ControlUp helps IT administrators know when it is time to move workloads out to the cloud and when they should be brought back in again. It works with applications that are run on physical Windows servers, in virtual machines or in private, public or hybrid clouds.

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.