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CA Technologies Named a Leader in Gartner MQ for APM

Gartner has named CA Technologies a Leader in the Gartner Magic Quadrant for Application Performance Monitoring Suites.

CA Technologies offers CA Digital Experience Insights, a monitoring and AIOps platform that includes integrated services for application performance management, and user experience and infrastructure monitoring.

“We believe the recognition is validation of our strong vision and delivery of a full-stack AIOps solution that addresses the need of every modern software factory to continuously deliver amazing app experiences, especially in today’s digital economy,” said Ali Siddiqui, GM, Agile Operations, CA Technologies. “Using artificial intelligence and machine learning, CA Digital Experience Insights uses an open AIOps architecture that helps businesses understand how each link in the complex mix of endpoints, shared resources and hybrid environments impacts user experience, giving organizations a deeper understanding of their customers’ complete digital experience.”

According to Gartner, “For most enterprises, APM is now seen an essential element of application-centric IT operations and a DevOps-enabling bridge between production and development on one side and IT and digital business on the other.”

CA Digital Experience Insights, helps DevOps teams to support key business outcomes, deliver an exceptional digital user experience, speed app issue resolution even in the most complex digital environments – such as Kubernetes, container, AWS and Azure monitoring – and optimize for the future. This industry leading solution provides a fully correlated and consistent view of what helps or hurts digital experience.

Gartner defines APM suites as one or more software and/or hardware components that facilitate monitoring to meet three main functional dimensions:

- Digital experience monitoring

- Application discovery, tracing and diagnostics

- Artificial intelligence for IT Operations (AIOps) for applications

“Traditional approaches to APM focus on monitoring individual app components in post- development production settings. We believe this recognition acknowledges our work to help customers ‘shift monitoring left’ in the software development lifecycle so defects are remedied quickly to deliver a flawless user experience,” continued Siddiqui.

A new release of CA Application Performance Management, focused on cloud and container monitoring and application to infrastructure monitoring and correlation, provides new and enhanced monitoring capabilities for OpenShift, Kubernetes, Docker and VMware environments.


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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 ...

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CA Technologies Named a Leader in Gartner MQ for APM

Gartner has named CA Technologies a Leader in the Gartner Magic Quadrant for Application Performance Monitoring Suites.

CA Technologies offers CA Digital Experience Insights, a monitoring and AIOps platform that includes integrated services for application performance management, and user experience and infrastructure monitoring.

“We believe the recognition is validation of our strong vision and delivery of a full-stack AIOps solution that addresses the need of every modern software factory to continuously deliver amazing app experiences, especially in today’s digital economy,” said Ali Siddiqui, GM, Agile Operations, CA Technologies. “Using artificial intelligence and machine learning, CA Digital Experience Insights uses an open AIOps architecture that helps businesses understand how each link in the complex mix of endpoints, shared resources and hybrid environments impacts user experience, giving organizations a deeper understanding of their customers’ complete digital experience.”

According to Gartner, “For most enterprises, APM is now seen an essential element of application-centric IT operations and a DevOps-enabling bridge between production and development on one side and IT and digital business on the other.”

CA Digital Experience Insights, helps DevOps teams to support key business outcomes, deliver an exceptional digital user experience, speed app issue resolution even in the most complex digital environments – such as Kubernetes, container, AWS and Azure monitoring – and optimize for the future. This industry leading solution provides a fully correlated and consistent view of what helps or hurts digital experience.

Gartner defines APM suites as one or more software and/or hardware components that facilitate monitoring to meet three main functional dimensions:

- Digital experience monitoring

- Application discovery, tracing and diagnostics

- Artificial intelligence for IT Operations (AIOps) for applications

“Traditional approaches to APM focus on monitoring individual app components in post- development production settings. We believe this recognition acknowledges our work to help customers ‘shift monitoring left’ in the software development lifecycle so defects are remedied quickly to deliver a flawless user experience,” continued Siddiqui.

A new release of CA Application Performance Management, focused on cloud and container monitoring and application to infrastructure monitoring and correlation, provides new and enhanced monitoring capabilities for OpenShift, Kubernetes, Docker and VMware environments.


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.