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What is Network Visibility for APM and NPM?

Keith Bromley

Most everyone in IT has heard about network performance monitoring (NPM) and application performance monitoring (APM) tools. But what are the real benefits? For instance, what kind of information do I really get and is it worth the investment? Also, what about the complexity involved with these types of solutions?

The answer boils down to implementation. Essentially, did you install a visibility architecture first (so that you can optimize the flow of information to APM and NPM tools), or did you just add point solutions for APM and NPM? This answer will determine the effectiveness of your application monitoring solutions.

Any performance monitoring solution is only as good as the quality of data feeding the tools

The visibility architecture concept is extremely important because it organizes the flow of information to security and monitoring tools. Without it, you don’t know what the quality and integrity of the input data to the tools is. A visibility architecture delivers an end-to-end infrastructure which enables physical and virtual network, application, and security visibility. Specifically, network packet brokers can be included in the architecture to parse the requisite data needed and distribute that data to one or more application monitoring tools.

Once the network packet brokers are installed, it makes it much easier for the APM and NPM solutions to optimize your performance. In addition to these tools, other capabilities, like application intelligence and proactive network monitoring, can be installed as part of the visibility architecture to further increase the range of capabilities.

Here are some example use cases of what you can accomplish when a visibility architecture is combined with performance monitoring tools:

■ NPM solutions can be used to improve the quality of service (QoS) on the network and optimize the network service level agreement (SLA) performance.

■ APM solutions can be used to improve quality of experience (QoE) and optimize SLA performance for network applications, i.e. capture data that can be used by an APM tool to observe and diagnose application slowness.

■ APM tools can be used to analyze user behaviors.

■ Application intelligence can be used to identify slow or underperforming applications and network bottlenecks.

■ Proactive monitoring can be used to provide better and faster network rollouts by pre-testing the network with synthetic traffic to understand how it performs against either specific application traffic or a combination of traffic types.

■ Proactive troubleshooting can be combined with application intelligence to help you more quickly anticipate where network and application problems may be coming from.

■ It is also possible to prevent applications from overloading the network bandwidth by using application intelligence to “see” application growth on the bandwidth in real-time and prevent catastrophic events.

■ You can also conduct inline network performance monitoring to optimize A/B traffic route flows (i.e. investigate path latency and performance problems)

In the end, any performance monitoring solution is only as good as the quality of data feeding the tools. Extraneous and duplicate data will affect the ability and speed of monitoring tools to come to an accurate analysis. A few minutes of time to understand your network visibility and the types of blind you have might be well worth it.

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What is Network Visibility for APM and NPM?

Keith Bromley

Most everyone in IT has heard about network performance monitoring (NPM) and application performance monitoring (APM) tools. But what are the real benefits? For instance, what kind of information do I really get and is it worth the investment? Also, what about the complexity involved with these types of solutions?

The answer boils down to implementation. Essentially, did you install a visibility architecture first (so that you can optimize the flow of information to APM and NPM tools), or did you just add point solutions for APM and NPM? This answer will determine the effectiveness of your application monitoring solutions.

Any performance monitoring solution is only as good as the quality of data feeding the tools

The visibility architecture concept is extremely important because it organizes the flow of information to security and monitoring tools. Without it, you don’t know what the quality and integrity of the input data to the tools is. A visibility architecture delivers an end-to-end infrastructure which enables physical and virtual network, application, and security visibility. Specifically, network packet brokers can be included in the architecture to parse the requisite data needed and distribute that data to one or more application monitoring tools.

Once the network packet brokers are installed, it makes it much easier for the APM and NPM solutions to optimize your performance. In addition to these tools, other capabilities, like application intelligence and proactive network monitoring, can be installed as part of the visibility architecture to further increase the range of capabilities.

Here are some example use cases of what you can accomplish when a visibility architecture is combined with performance monitoring tools:

■ NPM solutions can be used to improve the quality of service (QoS) on the network and optimize the network service level agreement (SLA) performance.

■ APM solutions can be used to improve quality of experience (QoE) and optimize SLA performance for network applications, i.e. capture data that can be used by an APM tool to observe and diagnose application slowness.

■ APM tools can be used to analyze user behaviors.

■ Application intelligence can be used to identify slow or underperforming applications and network bottlenecks.

■ Proactive monitoring can be used to provide better and faster network rollouts by pre-testing the network with synthetic traffic to understand how it performs against either specific application traffic or a combination of traffic types.

■ Proactive troubleshooting can be combined with application intelligence to help you more quickly anticipate where network and application problems may be coming from.

■ It is also possible to prevent applications from overloading the network bandwidth by using application intelligence to “see” application growth on the bandwidth in real-time and prevent catastrophic events.

■ You can also conduct inline network performance monitoring to optimize A/B traffic route flows (i.e. investigate path latency and performance problems)

In the end, any performance monitoring solution is only as good as the quality of data feeding the tools. Extraneous and duplicate data will affect the ability and speed of monitoring tools to come to an accurate analysis. A few minutes of time to understand your network visibility and the types of blind you have might be well worth it.

Hot Topics

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.