Network and Application Performance Monitoring: Why You Need Both

When application performance suddenly drops, how do you quickly identify the source of the problem? Is it the network, application code, a database bottleneck, or even a combination of all three?

In distributed, cloud-native environments, where services communicate across multiple systems and networks, finding the answer is rarely straightforward. Every minute spent isolating the cause is another minute of subpar user experience and potentially lost revenue.

This is where Network Performance Monitoring (NPM) and Application Performance Monitoring (APM) prove useful. They help teams investigate problems from different angles, so they can see the complete picture of a situation unfolding.

This is because they monitor different layers of your infrastructure: NPM focuses on network traffic and connectivity, while APM tracks application behavior and performance.  They work best together, though, giving you insight into both the network and application layers simultaneously.

This article explains what NPM and APM are, how they work together to eliminate blind spots, and the trends making integrated monitoring easier to implement.

What is network performance monitoring?

Applications rely on networks to move data between services, databases, and users. When network performance drops, applications can become slow or unreliable. In these situations, one of the biggest challenges is determining whether the problem originates in the network or somewhere else in the application stack.

Network performance monitoring (NPM) helps teams investigate this by tracking network activity to identify issues that can make applications unreliable, such as delays between services, lost data, network congestion, or failed connections.

In modern cloud-based environments, applications are made up of multiple services communicating across internal and external networks. Each new service added introduces more interactions and dependencies, increasing the complexity of the environment and the number of possible causes that may need to be investigated. This makes it harder to isolate the primary source of failure if you don’t understand how traffic moves through the network.

To provide this understanding, network performance monitoring tools collect data about network traffic and activity. This includes information such as traffic volume, connection errors, and delays between services. This information helps teams determine whether performance issues stem from the network itself or rule out network-related problems, indicating that the cause is the applications running on top of it.

In modern cloud-native environments (like Kubernetes), NPM often uses eBPF (extended Berkeley Packet Filter) technology. eBPF allows for deep network visibility without the overhead of traditional packet sniffing, making it the gold standard for monitoring high-speed service meshes.

What is application performance monitoring?

But what if the application itself is the source of the problem? Performance issues can occur for many reasons, such as inefficient code, overloaded services, or applications running out of the resources they need to process requests. In these situations, the challenge is confirming that the problem originates within the application rather than the network, and then identifying where within the application the issue occurs.

Application performance monitoring (APM) helps teams investigate this by tracking how applications handle requests as they are processed. This includes information such as error rates, response times within the application, and the amount of CPU, memory, and other resources an application consumes over time.

Modern applications are composed of many interconnected services, databases, APIs, and third-party integrations that work together to fulfill a single user request. If any one of these components becomes slow or overloaded, it can slow down the entire request, making the application appear slow or unreliable. These interconnected layers make it difficult to identify which part of the application is responsible.

APM tools help teams investigate this by collecting data about how applications behave as requests are processed. By analysing how different parts of the application perform over time, teams can trace performance issues to the specific service or component responsible.

Why you need both NPM and APM

Neither network performance monitoring nor application performance monitoring provides a complete view on its own.

• Network monitoring can indicate whether services communicate properly with each other, but it cannot determine whether the application handles requests correctly once they arrive.
• Application monitoring, on the other hand, shows how the application responds to requests, but it cannot show whether delays or failures occurred before those requests reached the application.

The challenge is that performance problems can look the same from the outside. A slowdown or failure may be caused by an issue within the application itself or by delays in communication between services. The symptom alone doesn’t reveal the source of the problem.

Consider a hypothetical scenario where an eCommerce store's checkout suddenly becomes five seconds slower. Both NPM and APM detect the issue: response times have spiked, and users are experiencing delays. The question is: which layer is responsible?

In one case, the network is the culprit. NPM reveals problems with how data is moving between the payment service and the external payment processor – packets are being lost and having to be resent, creating delays. The application code itself is working fine.

In another case, the network is fine. NPM shows healthy performance: connections are stable, and data is moving normally between services. This allows teams to rule out network issues and focus on the application itself, where APM reveals the actual problem: one of the application's services is running out of memory and restarting repeatedly, causing intermittent failures.

(While APM uses distributed traces to follow a request through code, modern integrated tools can now "stitch" network latency data directly into those traces. This allows an engineer to see exactly which network hop caused a delay within a single request's path.)

In both situations, the symptom is the same, even though the root cause is different.

This highlights the value of using both NPM and APM together. If you use just one, you can still get to the answer, but it’s a much slower process — which isn’t ideal during an incident. For example, APM shows the application is fine, so it must be a network problem by process of elimination, but without NPM data, you're making an educated guess rather than confirming the specific issue.

With integrated monitoring, using NPM and APM together, teams gain direct visibility into both layers simultaneously. They can quickly determine whether an issue originates in the network or application layer, reducing time to resolution and preventing wasted effort investigating the wrong part of the system.

This mean time to innocence (MTTI) refers to the speed at which a team (usually the network team) can prove that the problem isn't in their layer. Integrated NPM/APM data is the primary tool for achieving a low Mean Time to Innocence, which reduces friction between network operations and development teams: instead of pointing fingers, both sides have shared visibility into the complete picture.

A clearer view of performance

Network performance monitoring and application performance monitoring address different parts of the same problem. NPM helps teams understand how systems communicate, while APM shows how applications behave as requests are processed.

Used together, they give teams a complete view of how requests move through an environment, making it easier to move quickly and with confidence from symptoms to root cause. By looking at network activity alongside application behavior, organizations can identify problems more efficiently to keep everything running smoothly, even as environments become more complex.

Trends shaping network and application performance monitoring

Several developments are making it easier for organizations to implement the integrated monitoring approach.

• AI-assisted analysis and root cause identification: AI is increasingly being used to analyze performance data across network and application layers simultaneously. Rather than investigating each layer manually, teams can use AI-assisted analysis to narrow down the likely source of an issue more quickly, reducing investigation time during incidents. According to recent industry research, over 60% of IT teams now use AI within their observability workflows, although most are still in the early stages of adoption.
• OpenTelemetry and standardized telemetry collection: OpenTelemetry (OTel) is becoming a common standard for collecting metrics, traces, and logs across different tools and platforms. By standardizing how performance data is collected, organizations can more easily combine network and application data without complex integrations, making it simpler to compare signals across layers and avoid fragmented monitoring approaches.
• Unified observability platforms: Many organizations are moving away from separate monitoring tools toward platforms that unify network, application, and infrastructure monitoring. This consolidation reduces data silos and allows teams to correlate performance data more easily, helping them move from symptoms to root cause without switching between tools.

FAQs about Network vs. Application Performance Monitoring