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Performance Testing and APM: Why You Need Both

Paola Moretto

Performance Testing (aka synthetic testing) and APM (Application Performance Management and monitoring solutions) are often regarded as two competing solutions, as if one were an alternative to the other. They are actually perfect complementary tools to achieve optimal performance for your application. Whether you develop web applications, SaaS products or mobile apps, you‘ll find both approaches to be absolutely necessary in your software operations. Here, I explain why. (I’ll use “monitoring solutions” as a synonym for APM solutions.)

Let’s talk about performance for a moment. Performance can refer to end-user performance or backend performance.

End-user performance is a measurement of the user experience as relates to speed, responsiveness and scalability. Page load time is one of the typical metrics for the user experience.

Backend performance, as the name suggests, refers to the performance of backend resources used by the application. This is something end users may not be aware of, but which is important to the way the app performs.

Let’s look at APM or monitoring solutions first. These tools are normally used to collect data from a live environment. Monitoring solutions help gather important information on the application backend metrics, server behaviors, slow components and transactions. Several application monitoring solutions track database and browser performance as well.

Monitoring is Not Enough

There are many kinds of monitoring solutions that operate at different levels of the stack, with different timing granularity, with the opportunity to define custom metrics. You have server monitoring, infrastructure monitoring, user monitoring, high-frequency metrics, and others. What is clear with monitoring solutions is that one is not enough. You probably need a portfolio of tools to have a clear understanding of what is happening with your application. Most tools have alerts systems that provide real-time information on page load and are designed to notify you in real time of events so you are immediately informed about deteriorating performance.

It doesn’t matter how comprehensive your monitoring architecture is, it doesn’t provide the full picture. First of all, live traffic is noisy. You have no control over what your users are doing and how many users you have at any given point. If you have a performance issue, that makes it really hard to troubleshoot. Was that expected behavior? Did they just hit a corner case previously unanticipated? Is a combination of the live workloads that crashed the system? So even though the technology is designed to enable real-time troubleshooting, the reality is that since it’s not a controlled environment, you might not be able to identify root cause performance issues in a timely manner.

Second and most important, the information produced by monitoring is delivered after the fact. Monitoring is like calling AAA after an accident. It’s a great service to have, but it’s much better to prevent the accident in the first place.

This explains why you need to add performance testing to the mix. While monitoring can inform you about performance after the fact, performance testing can help you prevent bad things from happening.

While monitoring is usually done on your live/production environment, performance testing usually utilizes synthetic traffic on a pre-production/staging environment. Having a pre-production environment as close as possible to your production environment will help you derive the most meaningful results.

In performance testing, users are simulated but the traffic is absolutely real. You can apply different types of loads to discover the breaking point of your application before it goes live. You can use performance testing to test with traffic that is higher than anything your actual application has seen – yet – so that you can prepare for peak of traffic.

Performance testing can also help you identify performance degradations that might have resulted from code changes, infrastructure changes or third party changes. It basically answers the question: “Can you trust this build to deliver the same user experience your users are counting on?”

In performance testing, you have total control over the amount of traffic and the workloads your users execute. That makes it a lot easier to troubleshoot.

And, if you are using Docker or containers-based architecture, you could also test performance improvements, under different configurations and platforms easily.

With performance testing, you can also measure end-to-end performance – a good indication of user experience -- which gives visibility into the entire application delivery chain, enabling greater transparency and targeted troubleshooting.

You wouldn’t build a bridge and then send hundreds of thousands of cars over it without first doing structural tests for structural problems. But you also wouldn’t open a bridge to traffic without continually monitoring how it is holding up under all the traffic. You need to do both, whether you’re talking about bridges or apps. The difference is, while a bridge is static and needs to be tested only once or at periodic intervals, software today is highly dynamic and needs to be tested on a daily basis as part of your regular flow.

Used together, performance testing and monitoring make a great team, so to speak. Use both to make sure you deploy a reliable product.

Paola Moretto is Founder and CEO of Nouvola.

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Performance Testing and APM: Why You Need Both

Paola Moretto

Performance Testing (aka synthetic testing) and APM (Application Performance Management and monitoring solutions) are often regarded as two competing solutions, as if one were an alternative to the other. They are actually perfect complementary tools to achieve optimal performance for your application. Whether you develop web applications, SaaS products or mobile apps, you‘ll find both approaches to be absolutely necessary in your software operations. Here, I explain why. (I’ll use “monitoring solutions” as a synonym for APM solutions.)

Let’s talk about performance for a moment. Performance can refer to end-user performance or backend performance.

End-user performance is a measurement of the user experience as relates to speed, responsiveness and scalability. Page load time is one of the typical metrics for the user experience.

Backend performance, as the name suggests, refers to the performance of backend resources used by the application. This is something end users may not be aware of, but which is important to the way the app performs.

Let’s look at APM or monitoring solutions first. These tools are normally used to collect data from a live environment. Monitoring solutions help gather important information on the application backend metrics, server behaviors, slow components and transactions. Several application monitoring solutions track database and browser performance as well.

Monitoring is Not Enough

There are many kinds of monitoring solutions that operate at different levels of the stack, with different timing granularity, with the opportunity to define custom metrics. You have server monitoring, infrastructure monitoring, user monitoring, high-frequency metrics, and others. What is clear with monitoring solutions is that one is not enough. You probably need a portfolio of tools to have a clear understanding of what is happening with your application. Most tools have alerts systems that provide real-time information on page load and are designed to notify you in real time of events so you are immediately informed about deteriorating performance.

It doesn’t matter how comprehensive your monitoring architecture is, it doesn’t provide the full picture. First of all, live traffic is noisy. You have no control over what your users are doing and how many users you have at any given point. If you have a performance issue, that makes it really hard to troubleshoot. Was that expected behavior? Did they just hit a corner case previously unanticipated? Is a combination of the live workloads that crashed the system? So even though the technology is designed to enable real-time troubleshooting, the reality is that since it’s not a controlled environment, you might not be able to identify root cause performance issues in a timely manner.

Second and most important, the information produced by monitoring is delivered after the fact. Monitoring is like calling AAA after an accident. It’s a great service to have, but it’s much better to prevent the accident in the first place.

This explains why you need to add performance testing to the mix. While monitoring can inform you about performance after the fact, performance testing can help you prevent bad things from happening.

While monitoring is usually done on your live/production environment, performance testing usually utilizes synthetic traffic on a pre-production/staging environment. Having a pre-production environment as close as possible to your production environment will help you derive the most meaningful results.

In performance testing, users are simulated but the traffic is absolutely real. You can apply different types of loads to discover the breaking point of your application before it goes live. You can use performance testing to test with traffic that is higher than anything your actual application has seen – yet – so that you can prepare for peak of traffic.

Performance testing can also help you identify performance degradations that might have resulted from code changes, infrastructure changes or third party changes. It basically answers the question: “Can you trust this build to deliver the same user experience your users are counting on?”

In performance testing, you have total control over the amount of traffic and the workloads your users execute. That makes it a lot easier to troubleshoot.

And, if you are using Docker or containers-based architecture, you could also test performance improvements, under different configurations and platforms easily.

With performance testing, you can also measure end-to-end performance – a good indication of user experience -- which gives visibility into the entire application delivery chain, enabling greater transparency and targeted troubleshooting.

You wouldn’t build a bridge and then send hundreds of thousands of cars over it without first doing structural tests for structural problems. But you also wouldn’t open a bridge to traffic without continually monitoring how it is holding up under all the traffic. You need to do both, whether you’re talking about bridges or apps. The difference is, while a bridge is static and needs to be tested only once or at periodic intervals, software today is highly dynamic and needs to be tested on a daily basis as part of your regular flow.

Used together, performance testing and monitoring make a great team, so to speak. Use both to make sure you deploy a reliable product.

Paola Moretto is Founder and CEO of Nouvola.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...