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20 Technologies to Support APM - Part 3

APMdigest continues the list, cataloging the many valuable tools available – beyond what is technically categorized as Application Performance Management (APM) – to support the goals of improving application performance and business service.

Start with Part 1

Start with Part 2

11. Network Performance Monitoring (NPM)

The performance and availability of the network is an essential factor in whether applications meet employee expectations. The rapid pace of innovation in mobile technology means that ensuring adequate network performance is becoming increasingly important. Therefore investing in a good network performance monitoring solution that is able to perform packet capture and analysis at a minimum in relation to the applications served is important and will enrich your APM strategy.
John Rakowski
Analyst, Infrastructure and Operations, Forrester Research

12. Application-Aware Network Performance Monitoring (AA-NPM)

The challenge is that APM has evolved into a mosaic of monitoring tools, analytic engines, and event processors that provide many solutions to different problems. When you step back and look at the big picture it all comes into focus, but when you're trying to rationalize one technology over another, things aren't so clear at close range. I have found that the simplicity and ease of use with agentless monitoring (i.e. wire data analytics) is a great place to start. You may also hear the terms Application Aware Infrastructure Performance Monitoring (AA-IPM) or Application Aware Network Performance Monitoring (AA-NPM) both of which are complimentary to APM and I believe to be an essential part of an overall APM solution.
Larry Dragich
Director of Enterprise Application Services at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

13. Deep Packet Inspection (DPI)

When it comes to APM, Deep Packet Inspection (DPI) isn't the first thing that comes to mind, but it should be, and we consider it a must-have in supporting APM. The general consensus seems to be that flow-based technologies (NetFlow, sFlow, IPFIX, etc.) provide enough visibility regarding communication, and end-point solutions provide the details from the client point of view. But network and application analysis based on DPI can provide all this and more. DPI provides definitive latency measurements, and it quickly allows analysts to isolate the problem to the network or the application. Once isolated, payload information from packets in the communication path can provide insights that no other solution can – like error messages that are being returned but not correctly processed by applications. And when combined with network forensics (storing packets for detailed, post-incident analysis), critical application transactions can be unequivocally verified from days or even weeks ago, something that is not available in any other form of APM solution.
Jay Botelho
Director of Product Management, WildPackets

14. Network Packet Recording

Something that all enterprises should seek out is accurate network packet recording. It's imperative to have a solution that can capture, index and record network traffic with continuous 100% accuracy even during unpredictable traffic spikes. Accurate network packet recording enables IT teams to troubleshoot and diagnose network and application performance issues as soon as they arise, and help security teams investigate and contain security problems and help risk and compliance teams do their jobs. Operations teams can determine whether the problems reside within the IT infrastructure or within the applications running on the network – reducing time-to-resolution (TTR) and lowering operational expenditures (OPEX). Traditional detection tools won't cut it in an era where millions of dollars in revenue can be lost with milliseconds of downtime – the key is maintaining a network infrastructure that delivers continuous historical network visibility.
Mike Heumann
Sr. Director, Marketing (Endace), Emulex

15. Network Emulation

Network Emulation is a must have. The first part of an APM cycle is to ensure that applications are designed/suitable for the deployed environment. The Network (Mobile, WAN, Internet...) is a critical but often ignored component of this. One reason is the complexity of going about verifying applications in real world networks, however Network Emulation makes this easy by providing the ability to replicate the complete network environment. By re-creating all real world network conditions (restricted bandwidth, latency, loss, QoS etc), Network Emulation gives organizations an accurate assessment of whether an application is suitable for them, long before they try to manage, with APM, the unmanageable.
Jim Swepson
Pre-sales Technologist, iTrinegy

20 Technologies to Support APM - Part 4

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

20 Technologies to Support APM - Part 3

APMdigest continues the list, cataloging the many valuable tools available – beyond what is technically categorized as Application Performance Management (APM) – to support the goals of improving application performance and business service.

Start with Part 1

Start with Part 2

11. Network Performance Monitoring (NPM)

The performance and availability of the network is an essential factor in whether applications meet employee expectations. The rapid pace of innovation in mobile technology means that ensuring adequate network performance is becoming increasingly important. Therefore investing in a good network performance monitoring solution that is able to perform packet capture and analysis at a minimum in relation to the applications served is important and will enrich your APM strategy.
John Rakowski
Analyst, Infrastructure and Operations, Forrester Research

12. Application-Aware Network Performance Monitoring (AA-NPM)

The challenge is that APM has evolved into a mosaic of monitoring tools, analytic engines, and event processors that provide many solutions to different problems. When you step back and look at the big picture it all comes into focus, but when you're trying to rationalize one technology over another, things aren't so clear at close range. I have found that the simplicity and ease of use with agentless monitoring (i.e. wire data analytics) is a great place to start. You may also hear the terms Application Aware Infrastructure Performance Monitoring (AA-IPM) or Application Aware Network Performance Monitoring (AA-NPM) both of which are complimentary to APM and I believe to be an essential part of an overall APM solution.
Larry Dragich
Director of Enterprise Application Services at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

13. Deep Packet Inspection (DPI)

When it comes to APM, Deep Packet Inspection (DPI) isn't the first thing that comes to mind, but it should be, and we consider it a must-have in supporting APM. The general consensus seems to be that flow-based technologies (NetFlow, sFlow, IPFIX, etc.) provide enough visibility regarding communication, and end-point solutions provide the details from the client point of view. But network and application analysis based on DPI can provide all this and more. DPI provides definitive latency measurements, and it quickly allows analysts to isolate the problem to the network or the application. Once isolated, payload information from packets in the communication path can provide insights that no other solution can – like error messages that are being returned but not correctly processed by applications. And when combined with network forensics (storing packets for detailed, post-incident analysis), critical application transactions can be unequivocally verified from days or even weeks ago, something that is not available in any other form of APM solution.
Jay Botelho
Director of Product Management, WildPackets

14. Network Packet Recording

Something that all enterprises should seek out is accurate network packet recording. It's imperative to have a solution that can capture, index and record network traffic with continuous 100% accuracy even during unpredictable traffic spikes. Accurate network packet recording enables IT teams to troubleshoot and diagnose network and application performance issues as soon as they arise, and help security teams investigate and contain security problems and help risk and compliance teams do their jobs. Operations teams can determine whether the problems reside within the IT infrastructure or within the applications running on the network – reducing time-to-resolution (TTR) and lowering operational expenditures (OPEX). Traditional detection tools won't cut it in an era where millions of dollars in revenue can be lost with milliseconds of downtime – the key is maintaining a network infrastructure that delivers continuous historical network visibility.
Mike Heumann
Sr. Director, Marketing (Endace), Emulex

15. Network Emulation

Network Emulation is a must have. The first part of an APM cycle is to ensure that applications are designed/suitable for the deployed environment. The Network (Mobile, WAN, Internet...) is a critical but often ignored component of this. One reason is the complexity of going about verifying applications in real world networks, however Network Emulation makes this easy by providing the ability to replicate the complete network environment. By re-creating all real world network conditions (restricted bandwidth, latency, loss, QoS etc), Network Emulation gives organizations an accurate assessment of whether an application is suitable for them, long before they try to manage, with APM, the unmanageable.
Jim Swepson
Pre-sales Technologist, iTrinegy

20 Technologies to Support APM - Part 4

Hot Topics

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...