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Netuitive Previews Predictive Analytics Platform For End-to-End Cloud Management

Industry-First Open Integration Hub for the Cloud

Netuitive announced plans for the industry’s first predictive IT analytics platform for end-to-end analysis of performance and capacity management in the cloud.

Underpinning the new platform, Netuitive is preparing to roll out Integration Studio, an open development environment that enables customers to plug in data streams from any monitoring source. Data is collected and normalized in Netuitive’s integration hub, analyzed by Netuitive’s predictive IT analytics software powered by Behavior Learning technology, with actionable outputs delivered based on the analysis.

Central to this are APIs enabling integration with configuration management databases, incident management, and programmatic administration of users security and policies.

Integration Studio will complement Netuitive’s broad portfolio of standard integrations with leading monitoring tools from companies such as VMware, BMC, CA, Compuware, HP, IBM, Microsoft, NetApp, NetIQ, Oracle, TeamQuest, Infovista, and others.

Netuitive’s automated collection and analysis, when combined with unlimited data integration possibilities, will be the industry’s first and most comprehensive IT analytics platform for end-to-end cloud management. Key capabilities currently in the platform along with those being added progressively include:

* Integration Studio [Q4] – Input real-time performance and capacity data from any system or application

* Trusted Triggers [Q3] - Intelligence for autonomic computing (dynamic resource provisioning and right-sizing of infrastructure - cloud service governor)

* Enhanced Virtual Data Center Dashboard [Q2] - Gain real time, multi-tier visibility into infrastructure performance

* Capacity Analysis – Understand and optimize utilization of existing resources

* Automated Diagnostics – Correlate behavior and isolate root cause across IT silos

* Predictive Analytics - Forecast problems 2+ hours before users are impacted

Using its patented Behavior Learning Engine, Netuitive software replaces manual, rules-based approaches with statistical analysis that automatically correlates and self-learns the operational behavior of IT systems and applications. It then forecasts issues before they impact performance and isolates root cause wherever a problem occurs.

According to Gartner analyst Donna Scott,“The inherent nature of virtualized infrastructure and private cloud solutions is dynamic and complex creating a significant management challenge. This makes manual rules-based approaches no longer viable requiring a move to an analytics-based approach that provides a real-time behavioral understanding of how the IT infrastructure is performing and being used.”

“The private cloud demands a new breed of intelligence to monitor and manage it,” said Nicola Sanna, president and CEO of Netuitive. “Our large enterprise customers are coming to us with complexity issues that are orders of magnitude larger than just a few years ago. Netuitive is committed to delivering foundational elements, such as Integration Studio and Trusted Triggers, to create the industry’s first predictive analytics-based approach to cloud management that crosses platforms, data sources, vendors and users.”

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 ...

Netuitive Previews Predictive Analytics Platform For End-to-End Cloud Management

Industry-First Open Integration Hub for the Cloud

Netuitive announced plans for the industry’s first predictive IT analytics platform for end-to-end analysis of performance and capacity management in the cloud.

Underpinning the new platform, Netuitive is preparing to roll out Integration Studio, an open development environment that enables customers to plug in data streams from any monitoring source. Data is collected and normalized in Netuitive’s integration hub, analyzed by Netuitive’s predictive IT analytics software powered by Behavior Learning technology, with actionable outputs delivered based on the analysis.

Central to this are APIs enabling integration with configuration management databases, incident management, and programmatic administration of users security and policies.

Integration Studio will complement Netuitive’s broad portfolio of standard integrations with leading monitoring tools from companies such as VMware, BMC, CA, Compuware, HP, IBM, Microsoft, NetApp, NetIQ, Oracle, TeamQuest, Infovista, and others.

Netuitive’s automated collection and analysis, when combined with unlimited data integration possibilities, will be the industry’s first and most comprehensive IT analytics platform for end-to-end cloud management. Key capabilities currently in the platform along with those being added progressively include:

* Integration Studio [Q4] – Input real-time performance and capacity data from any system or application

* Trusted Triggers [Q3] - Intelligence for autonomic computing (dynamic resource provisioning and right-sizing of infrastructure - cloud service governor)

* Enhanced Virtual Data Center Dashboard [Q2] - Gain real time, multi-tier visibility into infrastructure performance

* Capacity Analysis – Understand and optimize utilization of existing resources

* Automated Diagnostics – Correlate behavior and isolate root cause across IT silos

* Predictive Analytics - Forecast problems 2+ hours before users are impacted

Using its patented Behavior Learning Engine, Netuitive software replaces manual, rules-based approaches with statistical analysis that automatically correlates and self-learns the operational behavior of IT systems and applications. It then forecasts issues before they impact performance and isolates root cause wherever a problem occurs.

According to Gartner analyst Donna Scott,“The inherent nature of virtualized infrastructure and private cloud solutions is dynamic and complex creating a significant management challenge. This makes manual rules-based approaches no longer viable requiring a move to an analytics-based approach that provides a real-time behavioral understanding of how the IT infrastructure is performing and being used.”

“The private cloud demands a new breed of intelligence to monitor and manage it,” said Nicola Sanna, president and CEO of Netuitive. “Our large enterprise customers are coming to us with complexity issues that are orders of magnitude larger than just a few years ago. Netuitive is committed to delivering foundational elements, such as Integration Studio and Trusted Triggers, to create the industry’s first predictive analytics-based approach to cloud management that crosses platforms, data sources, vendors and users.”

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 ...