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Paessler Adds Support for Docker Container Monitoring

Paessler AG announced a new sensor type for its core product, PRTG Network Monitor, that enables users to monitor Docker containers, giving them key insights into performance, availability and other metrics.

PRTG Network Monitor is a comprehensive monitoring solution with more than 200 out-of-the-box sensor types, capable of monitoring everything from routers and switches to virtual machines and, now, Docker containers. Docker has become an increasingly popular technology with both DevOps teams and network administrators since its launch in 2013. Network administrators in particular have used containers to efficiently roll out and manage applications. With this progress in mind, Paessler created a dedicated Docker Container Sensor to give users the ability to monitor containers without the need to acquire a specific application performance monitoring solution. PRTG users can now monitor container status, uptime/downtime, CPU usage, memory, packets and traffic in/out, and exit code.

"IT should be able to monitor its entire infrastructure from one solution, and Docker is quickly becoming a part of that infrastructure," said Andrew Cutting, Director of Channel Sales, North America, Paessler. "It has always been our belief that IT is best served by a single pane of glass view of its infrastructure, and to make that a reality for PRTG Network Monitor, we have incorporated the Docker Container Sensor. This new sensor affirms are longstanding commitment to our customers to provide a fully-featured, flexible and vendor-neutral monitoring solution."

The Docker Container Sensor displays values for each metric within channels in PRTG Network Monitor. Users can define threshholds in each specific channel that will affect the sensor's status and trigger notifications and alerts.

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Paessler Adds Support for Docker Container Monitoring

Paessler AG announced a new sensor type for its core product, PRTG Network Monitor, that enables users to monitor Docker containers, giving them key insights into performance, availability and other metrics.

PRTG Network Monitor is a comprehensive monitoring solution with more than 200 out-of-the-box sensor types, capable of monitoring everything from routers and switches to virtual machines and, now, Docker containers. Docker has become an increasingly popular technology with both DevOps teams and network administrators since its launch in 2013. Network administrators in particular have used containers to efficiently roll out and manage applications. With this progress in mind, Paessler created a dedicated Docker Container Sensor to give users the ability to monitor containers without the need to acquire a specific application performance monitoring solution. PRTG users can now monitor container status, uptime/downtime, CPU usage, memory, packets and traffic in/out, and exit code.

"IT should be able to monitor its entire infrastructure from one solution, and Docker is quickly becoming a part of that infrastructure," said Andrew Cutting, Director of Channel Sales, North America, Paessler. "It has always been our belief that IT is best served by a single pane of glass view of its infrastructure, and to make that a reality for PRTG Network Monitor, we have incorporated the Docker Container Sensor. This new sensor affirms are longstanding commitment to our customers to provide a fully-featured, flexible and vendor-neutral monitoring solution."

The Docker Container Sensor displays values for each metric within channels in PRTG Network Monitor. Users can define threshholds in each specific channel that will affect the sensor's status and trigger notifications and alerts.

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.