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Ruxit Artificial Intelligence-Based Performance Monitoring and Analytics Solution Launched

Ruxit, an artificial intelligence (AI)-based performance monitoring and analytics solution, is launching today to show cloud companies how to manage and solve operational application problems better and faster.

As applications get more complex, the ability to solve problems is limited to how quickly the operations people can process information. ruxit multiplies that processing power by analyzing hundreds of millions of application dependencies instantly. The solution monitors everything in real-time -- from the infrastructure and cloud layer, all the way up to real users' browser interactions. Using an AI technology called smartscape, ruxit gives its users a full view of how applications, processes and servers work together. Instead of getting just another alert when there's an application problem, ruxit users learn how to fix it by viewing a real-time map of the problem's progress since its origin, as well as a suggested resolution.

"We wanted to build an intuitive application monitoring solution that uses human-like reasoning to solve and analyze problems, without the manual work and time constraints that result when people diagnose these problems themselves," said Bernd Greifeneder, the lead behind ruxit and successful application performance entrepreneur. "ruxit is built to scale for the complexity of public, private or hybrid cloud environments. It automatically monitors every individual application component, quickly finds the root cause of problems and offers a solution, which saves companies valuable time and improves production quality."

ruxit has more than 100 engineers, AI specialists and mathematicians, and is incubated within Compuware. The team focuses specifically on cloud and SaaS environments, and has built the only monitoring solution that uses AI to solve problems, instead of serving up more charts and big data that teams can manage. Think of ruxit as an added layer of context, reasoning and intelligence that goes beyond basic analytics that only identify whether or not an application is working.

Here's how ruxit is different:

- Smartscape Technology: A dynamic map shows the causal dependencies between users, applications, services, processes and hosts, providing real-time discovery and insight into the problem's impact.

- Visual Problem Resolution: An instant replay video of a problem that demonstrates the root cause of an issue and all key processes that are linked to the affected server or application.

- Full-Stack Visibility: Users can see everything with real-user, network and infrastructure monitoring, applications well as cloud visibility.

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 ...

Ruxit Artificial Intelligence-Based Performance Monitoring and Analytics Solution Launched

Ruxit, an artificial intelligence (AI)-based performance monitoring and analytics solution, is launching today to show cloud companies how to manage and solve operational application problems better and faster.

As applications get more complex, the ability to solve problems is limited to how quickly the operations people can process information. ruxit multiplies that processing power by analyzing hundreds of millions of application dependencies instantly. The solution monitors everything in real-time -- from the infrastructure and cloud layer, all the way up to real users' browser interactions. Using an AI technology called smartscape, ruxit gives its users a full view of how applications, processes and servers work together. Instead of getting just another alert when there's an application problem, ruxit users learn how to fix it by viewing a real-time map of the problem's progress since its origin, as well as a suggested resolution.

"We wanted to build an intuitive application monitoring solution that uses human-like reasoning to solve and analyze problems, without the manual work and time constraints that result when people diagnose these problems themselves," said Bernd Greifeneder, the lead behind ruxit and successful application performance entrepreneur. "ruxit is built to scale for the complexity of public, private or hybrid cloud environments. It automatically monitors every individual application component, quickly finds the root cause of problems and offers a solution, which saves companies valuable time and improves production quality."

ruxit has more than 100 engineers, AI specialists and mathematicians, and is incubated within Compuware. The team focuses specifically on cloud and SaaS environments, and has built the only monitoring solution that uses AI to solve problems, instead of serving up more charts and big data that teams can manage. Think of ruxit as an added layer of context, reasoning and intelligence that goes beyond basic analytics that only identify whether or not an application is working.

Here's how ruxit is different:

- Smartscape Technology: A dynamic map shows the causal dependencies between users, applications, services, processes and hosts, providing real-time discovery and insight into the problem's impact.

- Visual Problem Resolution: An instant replay video of a problem that demonstrates the root cause of an issue and all key processes that are linked to the affected server or application.

- Full-Stack Visibility: Users can see everything with real-user, network and infrastructure monitoring, applications well as cloud visibility.

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 ...