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Paessler Partners with Flowmon Networks

Paessler announces an alliance with global network intelligence company Flowmon Networks to offer more extensive IT monitoring combined with in-depth analysis.

Paessler and Flowmon Networks have integrated their solutions to bring together comprehensive IT monitoring capabilities with AI-powered analysis and advanced security features. The integration ensures availability, performance, and security for IT environments.

For IT specialists using Flowmon together with PRTG, the new integration offers the following benefits:

- Extensive IT monitoring, combined with in-depth flow analysis, joined to deliver maximum transparency

- Detection of unusual behavior, insider threats and DDoS attacks, monitor firewalls, virus scanners, and backups

- Monitoring Flowmon with PRTG assures availability based on PRTG’s included failover

PRTG Network Monitor by Paessler monitors IT infrastructure, network performance and applications as well as cloud services or virtual environments. Using conventional monitoring methods and protocols, as well as a powerful API, it has become one of the most common solutions for extensive IT monitoring.

The Flowmon Networks solution monitors and analyses network and cloud traffic detecting anomalies using a spectrum of methods all deployed simultaneously. It combines machine learning and behavior analysis seeking indicators of compromise to uncover malicious behaviors or data breaches.

The integration is technically based on two PRTG custom sensors:

- An SNMP sensor that monitors Flowmon appliances

- A Python Script sensor to display monitoring values from Flowmon in PRTG

The combination of the two solutions gives the user insights into network performance issues and security threats in addition to information on the status of every device in the organization. The events appear on the dashboard and are grouped by severity to enable instant prioritization and fast response. When an unusual incident happens, PRTG alerts the user, who can then switch to Flowmon for root-cause analysis. PRTG also monitors every Flowmon appliance deployed so that the user always knows that all relevant components of Flowmon are up and working.

“Integrating Flowmon with PRTG creates a new level of insights from both solutions, bringing together a broad overview with in-depth analytics making it even easier for our customers to keep their IT up and running and secure,” explains Steven Feurer, CTO at Paessler. “We found a partner that fits so well with Paessler, from a business view as well as from a technical perspective.”

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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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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 ...

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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.

Paessler Partners with Flowmon Networks

Paessler announces an alliance with global network intelligence company Flowmon Networks to offer more extensive IT monitoring combined with in-depth analysis.

Paessler and Flowmon Networks have integrated their solutions to bring together comprehensive IT monitoring capabilities with AI-powered analysis and advanced security features. The integration ensures availability, performance, and security for IT environments.

For IT specialists using Flowmon together with PRTG, the new integration offers the following benefits:

- Extensive IT monitoring, combined with in-depth flow analysis, joined to deliver maximum transparency

- Detection of unusual behavior, insider threats and DDoS attacks, monitor firewalls, virus scanners, and backups

- Monitoring Flowmon with PRTG assures availability based on PRTG’s included failover

PRTG Network Monitor by Paessler monitors IT infrastructure, network performance and applications as well as cloud services or virtual environments. Using conventional monitoring methods and protocols, as well as a powerful API, it has become one of the most common solutions for extensive IT monitoring.

The Flowmon Networks solution monitors and analyses network and cloud traffic detecting anomalies using a spectrum of methods all deployed simultaneously. It combines machine learning and behavior analysis seeking indicators of compromise to uncover malicious behaviors or data breaches.

The integration is technically based on two PRTG custom sensors:

- An SNMP sensor that monitors Flowmon appliances

- A Python Script sensor to display monitoring values from Flowmon in PRTG

The combination of the two solutions gives the user insights into network performance issues and security threats in addition to information on the status of every device in the organization. The events appear on the dashboard and are grouped by severity to enable instant prioritization and fast response. When an unusual incident happens, PRTG alerts the user, who can then switch to Flowmon for root-cause analysis. PRTG also monitors every Flowmon appliance deployed so that the user always knows that all relevant components of Flowmon are up and working.

“Integrating Flowmon with PRTG creates a new level of insights from both solutions, bringing together a broad overview with in-depth analytics making it even easier for our customers to keep their IT up and running and secure,” explains Steven Feurer, CTO at Paessler. “We found a partner that fits so well with Paessler, from a business view as well as from a technical perspective.”

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.