Skip to main content

Moogsoft AIOps Platform Integrates into Rackspace Fabric

Rackspace Technology announced the latest release of Rackspace Fabric™ now includes the Moogsoft AIOps Platform, which applies artificial intelligence (AI) and machine learning (ML) to log, metric, trace and alert data to help resolve IT incidents faster and more effectively.

The integration into Rackspace Fabric means Rackspace customers will experience increased uptime and fewer incident alerts by utilizing machine learning to solve IT problems.

Rackspace Fabric is a comprehensive platform that provides a unified approach to administrative tasks across multicloud environments. Many companies struggle to manage complex cloud environments with disjointed authentication systems, unreliable support, and disparate management capabilities. Rackspace Fabric simplifies the administration of Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure, and VMware environments by providing consistent administrative and support capabilities across these technologies.

Rackspace Fabric provides common billing, governance, support, monitoring, incident and change management services while still embracing the distinctive features of each cloud. Processing over a billion automated actions per month, Rackspace Fabric delivers a faster, more consistent approach to consuming and managing cloud resources from multiple providers, enabling customers to realize the transformational capabilities of the cloud much faster.

“Rackspace Fabric takes everything we’ve learned over the last 20 years about operating mission critical applications, combines it with the latest cloud technologies, and weaves it all together into one platform — one fabric — which we use as the foundation for our service delivery,” said Tolga Tarhan, CTO at Rackspace Technology. “The integration of Moogsoft AIOps incorporates comprehensive AI and ML capabilities enabling cross-services visibility to enhance incident response and increase uptime across our customers’ cloud environments. As these cloud environments become increasingly self-healing, the pace of innovation increases, and customers are freed to focus more resources to build and deliver more quickly for their end-users.”

“Digital transformation introduces unprecedented scale and complexity as organizations deploy new services into multicloud environments, and this requires the assistance of AI and ML to complement IT operations’ capacity to innovate,” said Moogsoft Founder and CEO Phil Tee. “This is precisely what Rackspace Technology has done by integrating Moogsoft AIOps into Rackspace Fabric. With AIOps, Rackspace Technology customers can focus more of their time on developing the digital services that drive today’s business, and less on fixing them.”

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...

Moogsoft AIOps Platform Integrates into Rackspace Fabric

Rackspace Technology announced the latest release of Rackspace Fabric™ now includes the Moogsoft AIOps Platform, which applies artificial intelligence (AI) and machine learning (ML) to log, metric, trace and alert data to help resolve IT incidents faster and more effectively.

The integration into Rackspace Fabric means Rackspace customers will experience increased uptime and fewer incident alerts by utilizing machine learning to solve IT problems.

Rackspace Fabric is a comprehensive platform that provides a unified approach to administrative tasks across multicloud environments. Many companies struggle to manage complex cloud environments with disjointed authentication systems, unreliable support, and disparate management capabilities. Rackspace Fabric simplifies the administration of Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure, and VMware environments by providing consistent administrative and support capabilities across these technologies.

Rackspace Fabric provides common billing, governance, support, monitoring, incident and change management services while still embracing the distinctive features of each cloud. Processing over a billion automated actions per month, Rackspace Fabric delivers a faster, more consistent approach to consuming and managing cloud resources from multiple providers, enabling customers to realize the transformational capabilities of the cloud much faster.

“Rackspace Fabric takes everything we’ve learned over the last 20 years about operating mission critical applications, combines it with the latest cloud technologies, and weaves it all together into one platform — one fabric — which we use as the foundation for our service delivery,” said Tolga Tarhan, CTO at Rackspace Technology. “The integration of Moogsoft AIOps incorporates comprehensive AI and ML capabilities enabling cross-services visibility to enhance incident response and increase uptime across our customers’ cloud environments. As these cloud environments become increasingly self-healing, the pace of innovation increases, and customers are freed to focus more resources to build and deliver more quickly for their end-users.”

“Digital transformation introduces unprecedented scale and complexity as organizations deploy new services into multicloud environments, and this requires the assistance of AI and ML to complement IT operations’ capacity to innovate,” said Moogsoft Founder and CEO Phil Tee. “This is precisely what Rackspace Technology has done by integrating Moogsoft AIOps into Rackspace Fabric. With AIOps, Rackspace Technology customers can focus more of their time on developing the digital services that drive today’s business, and less on fixing them.”

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...