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Why You Should Use Packet Analysis to Complement NetFlow When Monitoring Network Performance

Chris Bloom

Most organizations understand that centralized network monitoring is vital to maintaining the health of critical infrastructure and applications. And while solutions using NetFlow undoubtedly help gain perspective into capacity planning, trend analysis, and utilization, they lack the important precision of packet-based analytics tools that provide root-cause analysis for application performance, latency, TCP/IP or VoIP problems. Both monitoring technologies have their advantages and ideal use cases, so let's see how enterprises can maximize existing infrastructure and equipment investments by using packet analytics to complement NetFlow.

Evolution of Network Monitoring Technologies

First up is NetFlow. This is a well-known, well-established standard that provides conversational information about network status. Compared with even older protocols like SNMP, NetFlow offers greater precision, delivering data at intervals of around 1 second, depending on the equipment being monitored. NetFlow also has the advantage when it comes to providing a good global view of a network. This can be extremely helpful when monitoring the network's general health.

Next, let's turn to packed-based analytics. This breed of network monitoring solution delivers a much higher troubleshooting value to NetOps teams thanks to its data granularity and access to raw data. Since it is packet based, it is very precise, and interval times are often as short as a few nanoseconds. On top of that, the data is completely based on the original payload, so it isn't abbreviated or compiled.


What's the Difference Between Flow- and Packet-Based Analytics?

The key benefit of packet-based solutions is that they can provide much more information that can be used in network diagnostics. If there is a problem, the packet-based approach is completely passive, so it doesn't burden the network or interfere with existing operations or services. As you can imagine, this is very important, especially because nobody wants to exacerbate existing problems by piling on more network traffic.

NetFlow data, which typically comes from Layer 3 devices like routers and firewalls, provides good information about traffic volume between devices. But if you need to use multiple ports, NetFlow is at a disadvantage. This is where packet-based analytics come into their own. Packet analysis allows users to drill down and discover information about how the network is behaving, not just whether it's operating well. All of the packets and all of the information is there in the packets, so it's also going to be 100 percent accurate. And the final advantage is that packet-based analysis can be implemented with very little impact on the network, while supporting monitoring and troubleshooting simultaneously.

When it comes to troubleshooting, Flow-based technology is useful only up to Layer 3 (and occasionally Layer 4) so at least we can see where data traffic is being generated. When the NetOps team starts to get trouble tickets about a slow network or a CRM that's unable to save any records (for example), they need to start looking at the root cause. In this scenario, NetFlow would reveal that traffic is going between the client and the server, and that it's running on a specific port. It could also tell you what volume of traffic is produced by each of the clients. In other words, you could verify simple problems like whether the server is up and running and whether the port is operational.

The key here is that NetFlow alone isn't adequate in a modern network setting. It struggles to identify any activity associated with content delivery networks and applications that use multiple TCP or UDP ports. It also has no visibility into the payload or its contents. You may be able to see that a server has an issue, but that's far from definitive.

Take a look at the screenshot below, taken from a real use case. In this situation, a client is unable to get a response from a server, and its task is canceled. By investigating the reason for this problem, the packet-based solution quickly identifies the issue and shows the cause. In the text box at the bottom we see a message: “Your server command (process id 169) was deadlocked with another process and has been chosen as deadlock victim. Re-run your command.” This reference code tells us that the error was generated when two tasks concurrently requested access to the same resource. Armed with this information, the network team quickly determines that the problem is with the application, not the network, and provides the application team with actionable data to directly address the issue.


Packet-based analysis has been designed specifically to reveal the “how” of the network. Rather than being about just the volume of traffic, these solutions expose vital details about performance and application response. Users can compare network latency with application latency. They can see the efficiency of TCP communications on their network. They can evaluate the performance of VoIP and video over the network and determine if these real-time protocols are prioritized correctly. None of this can be achieved with NetFlow or its derivatives.

To help make my point, here are five common questions that can be solved when packet-based analysis is used in tandem with Netflow:

■ Is it the network or the application?

■ Is the issue isolated to a single user, a single server, or the network overall?

■ Are critical applications using network resources efficiently?

■ Is my network correctly configured for unified communications, and are unified communications co-existing with other network transactions?

■ Are critical functions, for example user authentication, failing due to protocol issues?

NetFlow certainly has its place in the network monitoring hierarchy, but its limitations make it less than ideal in many situations. For most network professionals, having access to packet data is a no-brainer and significantly accelerates mean-time-to-resolution (MTTR). The challenge is in learning how to balance the way we use these tools in our approach to network monitoring.

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Why You Should Use Packet Analysis to Complement NetFlow When Monitoring Network Performance

Chris Bloom

Most organizations understand that centralized network monitoring is vital to maintaining the health of critical infrastructure and applications. And while solutions using NetFlow undoubtedly help gain perspective into capacity planning, trend analysis, and utilization, they lack the important precision of packet-based analytics tools that provide root-cause analysis for application performance, latency, TCP/IP or VoIP problems. Both monitoring technologies have their advantages and ideal use cases, so let's see how enterprises can maximize existing infrastructure and equipment investments by using packet analytics to complement NetFlow.

Evolution of Network Monitoring Technologies

First up is NetFlow. This is a well-known, well-established standard that provides conversational information about network status. Compared with even older protocols like SNMP, NetFlow offers greater precision, delivering data at intervals of around 1 second, depending on the equipment being monitored. NetFlow also has the advantage when it comes to providing a good global view of a network. This can be extremely helpful when monitoring the network's general health.

Next, let's turn to packed-based analytics. This breed of network monitoring solution delivers a much higher troubleshooting value to NetOps teams thanks to its data granularity and access to raw data. Since it is packet based, it is very precise, and interval times are often as short as a few nanoseconds. On top of that, the data is completely based on the original payload, so it isn't abbreviated or compiled.


What's the Difference Between Flow- and Packet-Based Analytics?

The key benefit of packet-based solutions is that they can provide much more information that can be used in network diagnostics. If there is a problem, the packet-based approach is completely passive, so it doesn't burden the network or interfere with existing operations or services. As you can imagine, this is very important, especially because nobody wants to exacerbate existing problems by piling on more network traffic.

NetFlow data, which typically comes from Layer 3 devices like routers and firewalls, provides good information about traffic volume between devices. But if you need to use multiple ports, NetFlow is at a disadvantage. This is where packet-based analytics come into their own. Packet analysis allows users to drill down and discover information about how the network is behaving, not just whether it's operating well. All of the packets and all of the information is there in the packets, so it's also going to be 100 percent accurate. And the final advantage is that packet-based analysis can be implemented with very little impact on the network, while supporting monitoring and troubleshooting simultaneously.

When it comes to troubleshooting, Flow-based technology is useful only up to Layer 3 (and occasionally Layer 4) so at least we can see where data traffic is being generated. When the NetOps team starts to get trouble tickets about a slow network or a CRM that's unable to save any records (for example), they need to start looking at the root cause. In this scenario, NetFlow would reveal that traffic is going between the client and the server, and that it's running on a specific port. It could also tell you what volume of traffic is produced by each of the clients. In other words, you could verify simple problems like whether the server is up and running and whether the port is operational.

The key here is that NetFlow alone isn't adequate in a modern network setting. It struggles to identify any activity associated with content delivery networks and applications that use multiple TCP or UDP ports. It also has no visibility into the payload or its contents. You may be able to see that a server has an issue, but that's far from definitive.

Take a look at the screenshot below, taken from a real use case. In this situation, a client is unable to get a response from a server, and its task is canceled. By investigating the reason for this problem, the packet-based solution quickly identifies the issue and shows the cause. In the text box at the bottom we see a message: “Your server command (process id 169) was deadlocked with another process and has been chosen as deadlock victim. Re-run your command.” This reference code tells us that the error was generated when two tasks concurrently requested access to the same resource. Armed with this information, the network team quickly determines that the problem is with the application, not the network, and provides the application team with actionable data to directly address the issue.


Packet-based analysis has been designed specifically to reveal the “how” of the network. Rather than being about just the volume of traffic, these solutions expose vital details about performance and application response. Users can compare network latency with application latency. They can see the efficiency of TCP communications on their network. They can evaluate the performance of VoIP and video over the network and determine if these real-time protocols are prioritized correctly. None of this can be achieved with NetFlow or its derivatives.

To help make my point, here are five common questions that can be solved when packet-based analysis is used in tandem with Netflow:

■ Is it the network or the application?

■ Is the issue isolated to a single user, a single server, or the network overall?

■ Are critical applications using network resources efficiently?

■ Is my network correctly configured for unified communications, and are unified communications co-existing with other network transactions?

■ Are critical functions, for example user authentication, failing due to protocol issues?

NetFlow certainly has its place in the network monitoring hierarchy, but its limitations make it less than ideal in many situations. For most network professionals, having access to packet data is a no-brainer and significantly accelerates mean-time-to-resolution (MTTR). The challenge is in learning how to balance the way we use these tools in our approach to network monitoring.

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.