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AppDynamics Introduces Performance Monitoring and Analytics for Docker Container Environments

Visibility into Docker containers and applications running in them optimizes continuous delivery process, and enhances application quality and performance

AppDynamics announced the availability of comprehensive monitoring for Docker containers and the applications running in them.

“Dockerized” applications now can benefit from the deep visibility, rapid issue resolution, and analytics capabilities of the AppDynamics Application Intelligence Platform, which is able to visualize complex, distributed applications and follow transactional data across software application environments and IT infrastructure. With application architectures increasingly incorporating microservices and Docker containerization, the deep visibility that the AppDynamics platform provides across application environments is increasingly critical, enabling quick issue resolution both in the application and the container itself.

Docker is an open platform for developers and operations teams to build, ship, and run distributed applications. A “Dockerized” application is portable and can be run anywhere, locally or in the cloud. It addresses the complications of large, distributed applications running across complex environments. Docker is finding favor as a solution for supporting and incorporating microservices into application architectures.

The AppDynamics Docker monitoring solution provides an “application-centric” view inside and across Docker containers, so performance of business transactions can be tagged, traced and monitored even as they transverse multiple containers, while also providing data and insights into the performance of the Docker container itself. The monitoring solution automatically generates a customizable dashboard that displays key metrics such as total containers versus running containers, CPU and memory utilization, and network activity. All of the functionality of the AppDynamics Application Intelligence Platform — including automatic dynamic baselining, alerting, analytics, and more — is available for Docker containers and applications.

“AppDynamics delivers advanced topology visualization to manage these new and increasingly complex architectures,” said Jonah Kowall, AppDynamics’ vice president of market development and insights. “It’s imperative to be able to trace interactions from the user through all the various application service calls and infrastructure, including processes that happen inside Docker containers. Our platform delivers visibility into Docker containers just as it does into any other application or infrastructure component. If you’re going to manage effectively, ‘end-to-end’ visibility really has to mean ‘end-to-end.’”

Bhaskar Sunkara, AppDynamics’ CTO and senior vice president of product management, said, “Docker and AppDynamics are a nearly perfect match for optimizing continuous application delivery. Docker containers bring new levels of flexibility, agility and portability to the integration process, and the AppDynamics platform enables teams to accelerate their troubleshooting and maintain the highest levels of application performance at every step, from daily integrations through delivery and deployment.”

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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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AppDynamics Introduces Performance Monitoring and Analytics for Docker Container Environments

Visibility into Docker containers and applications running in them optimizes continuous delivery process, and enhances application quality and performance

AppDynamics announced the availability of comprehensive monitoring for Docker containers and the applications running in them.

“Dockerized” applications now can benefit from the deep visibility, rapid issue resolution, and analytics capabilities of the AppDynamics Application Intelligence Platform, which is able to visualize complex, distributed applications and follow transactional data across software application environments and IT infrastructure. With application architectures increasingly incorporating microservices and Docker containerization, the deep visibility that the AppDynamics platform provides across application environments is increasingly critical, enabling quick issue resolution both in the application and the container itself.

Docker is an open platform for developers and operations teams to build, ship, and run distributed applications. A “Dockerized” application is portable and can be run anywhere, locally or in the cloud. It addresses the complications of large, distributed applications running across complex environments. Docker is finding favor as a solution for supporting and incorporating microservices into application architectures.

The AppDynamics Docker monitoring solution provides an “application-centric” view inside and across Docker containers, so performance of business transactions can be tagged, traced and monitored even as they transverse multiple containers, while also providing data and insights into the performance of the Docker container itself. The monitoring solution automatically generates a customizable dashboard that displays key metrics such as total containers versus running containers, CPU and memory utilization, and network activity. All of the functionality of the AppDynamics Application Intelligence Platform — including automatic dynamic baselining, alerting, analytics, and more — is available for Docker containers and applications.

“AppDynamics delivers advanced topology visualization to manage these new and increasingly complex architectures,” said Jonah Kowall, AppDynamics’ vice president of market development and insights. “It’s imperative to be able to trace interactions from the user through all the various application service calls and infrastructure, including processes that happen inside Docker containers. Our platform delivers visibility into Docker containers just as it does into any other application or infrastructure component. If you’re going to manage effectively, ‘end-to-end’ visibility really has to mean ‘end-to-end.’”

Bhaskar Sunkara, AppDynamics’ CTO and senior vice president of product management, said, “Docker and AppDynamics are a nearly perfect match for optimizing continuous application delivery. Docker containers bring new levels of flexibility, agility and portability to the integration process, and the AppDynamics platform enables teams to accelerate their troubleshooting and maintain the highest levels of application performance at every step, from daily integrations through delivery and deployment.”

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.