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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

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...