Skip to main content

AppDynamics Extends Collaboration with Red Hat

AppDynamics has strengthened its collaboration with Red Hat.

AppDynamics’ agreement with Red Hat enables enterprises to use OpenShift by Red Hat and Red Hat JBoss Middleware technologies, and build in application intelligence from AppDynamics to help customers develop high performing applications faster and with more confidence. DevOps teams and line-of-business managers can work in tandem to accelerate the delivery of performance-optimized applications created using Red Hat software throughout the application lifecycle.

The Red Hat-AppDynamics collaboration is also aimed at enabling more efficient migration of existing enterprise applications to OpenShift, Red Hat's award-winning Platform-as-a-Service (PaaS) solution, and private clouds, while enabling apps to consistently perform as they did in their original environment.

“Our integration with AppDynamics enables us to provide developers using Red Hat software with application intelligence throughout the entire development process,” said Rob Cardwell, VP, Application Platforms, Red Hat. “Whether developers choose OpenShift or Red Hat JBoss Middleware, they can quickly develop and deploy dependable applications across the hybrid cloud.”

AppDynamics’ intelligent agents are instrumented into container images that streamline the development of applications for private clouds using OpenShift by Red Hat. This enables enterprises to auto-discover, monitor, and manage business transactions from end to end for optimized application and business performance. Developers using earlier versions of OpenShift Enterprise can enjoy similar benefits thanks to AppDynamics' intelligent agents injected into cartridges that contain libraries, source code, build mechanisms, and other critical development components.

Enterprises that rely on the Red Hat JBoss Enterprise Application Platform or the entire Red Hat JBoss Middleware portfolio also benefit from AppDynamics’ built-in application intelligence. AppDynamics injects bytecode into applications to capture key metrics on the health of JBoss EAP servers and OpenShift PaaS environments—in addition to tracking memory usage by Java virtual machines — with minimal system overhead.

AppDynamics and Red Hat are working closely together on co-engineering, co-marketing, and co-sales efforts. The goal is to better address digital businesses that are using custom applications for new consumer touch-points. The collaboration is aimed at streamlining Red Hat developer efforts to create reliable, high-performing applications, and opens the door to new opportunities in an increasingly digital age.

“We are excited to be able to integrate our application intelligence with Red Hat solutions for customers that choose to develop and manage their private environments with Red Hat software,” stated Matthew Polly, VP of Worldwide Alliances and Business Development for AppDynamics. “Our objective is to make it faster and easier to bring high performance to every phase of the software development and management lifecycle, so businesses can perform their best.”

AppDynamics is committed to continued work with Red Hat to help enterprises and developers deliver higher application performance and superior consumer experiences using applications developed with Red Hat software.

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.

AppDynamics Extends Collaboration with Red Hat

AppDynamics has strengthened its collaboration with Red Hat.

AppDynamics’ agreement with Red Hat enables enterprises to use OpenShift by Red Hat and Red Hat JBoss Middleware technologies, and build in application intelligence from AppDynamics to help customers develop high performing applications faster and with more confidence. DevOps teams and line-of-business managers can work in tandem to accelerate the delivery of performance-optimized applications created using Red Hat software throughout the application lifecycle.

The Red Hat-AppDynamics collaboration is also aimed at enabling more efficient migration of existing enterprise applications to OpenShift, Red Hat's award-winning Platform-as-a-Service (PaaS) solution, and private clouds, while enabling apps to consistently perform as they did in their original environment.

“Our integration with AppDynamics enables us to provide developers using Red Hat software with application intelligence throughout the entire development process,” said Rob Cardwell, VP, Application Platforms, Red Hat. “Whether developers choose OpenShift or Red Hat JBoss Middleware, they can quickly develop and deploy dependable applications across the hybrid cloud.”

AppDynamics’ intelligent agents are instrumented into container images that streamline the development of applications for private clouds using OpenShift by Red Hat. This enables enterprises to auto-discover, monitor, and manage business transactions from end to end for optimized application and business performance. Developers using earlier versions of OpenShift Enterprise can enjoy similar benefits thanks to AppDynamics' intelligent agents injected into cartridges that contain libraries, source code, build mechanisms, and other critical development components.

Enterprises that rely on the Red Hat JBoss Enterprise Application Platform or the entire Red Hat JBoss Middleware portfolio also benefit from AppDynamics’ built-in application intelligence. AppDynamics injects bytecode into applications to capture key metrics on the health of JBoss EAP servers and OpenShift PaaS environments—in addition to tracking memory usage by Java virtual machines — with minimal system overhead.

AppDynamics and Red Hat are working closely together on co-engineering, co-marketing, and co-sales efforts. The goal is to better address digital businesses that are using custom applications for new consumer touch-points. The collaboration is aimed at streamlining Red Hat developer efforts to create reliable, high-performing applications, and opens the door to new opportunities in an increasingly digital age.

“We are excited to be able to integrate our application intelligence with Red Hat solutions for customers that choose to develop and manage their private environments with Red Hat software,” stated Matthew Polly, VP of Worldwide Alliances and Business Development for AppDynamics. “Our objective is to make it faster and easier to bring high performance to every phase of the software development and management lifecycle, so businesses can perform their best.”

AppDynamics is committed to continued work with Red Hat to help enterprises and developers deliver higher application performance and superior consumer experiences using applications developed with Red Hat software.

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.