You may have already read here on the Inmetrics blog that modern applications have two fundamental capabilities: processing large volumes of data quickly and with virtually no errors, and providing seamless user experiences regardless of the device used to access them. However, there is a third, equally important characteristic: being able to deliver those two capabilities to many users simultaneously. To determine whether the system can do this, we conducted various performance tests.

These are technical evaluations that measure, under varying levels of usage, a system's efficiency—whether in terms of speed, stability, or responsiveness.

Read on to learn more about what performance tests are, how they can be categorized, and why they are so crucial—especially when dealing with programs that rely on cloud computing.

Performance Testing: What It Is, Metrics, and the Four Most Common Types

According to the International Software Testing Qualifications Board, performance tests are those we conduct to determine the efficiency of a component’s or system’s performance. At first glance, the definition may seem redundant, but one point should be emphasized: the focus of these tests is on performance efficiency, not merely on the existence of performance.

Any program, even a bad one, will “perform” in some way. The main purpose of testing is to measure how efficient the performance is in order to improve it or, when it’s already good, to ensure that it remains that way with each new version.

Another important aspect of the definition is that performance tests can be run on both individual components and the entire application; in other words, they can range from unit tests to tests of the final integration layers of complex systems.

The performance test results consist of five key metrics:

• Response time → the time a user waits for each action. This metric is typically measured in percentiles.
• Throughput → the number of transactions per second that the system processes. In e-commerce applications, for example, orders per minute can be measured. In a bank’s system, it’s transfers. In an insurance company’s system, it’s quotes.
• Error rate → the percentage of requests that fail as the load increases. Errors are inevitable, and not all of them cause the system to crash. When silent errors begin to occur, it is essential to check what is not working properly, even when the system is available.
  Concurrent users → the number of people who can be connected to the system at the same time while keeping both response time and error rate at acceptable levels.
• Resource consumption → consumption of processing power, memory, connections, queues… This metric links observed behavior to the underlying causes and infrastructure costs

By cross-referencing these metrics, we can not only verify whether the system can handle the load but also tell its full story: how it degrades, which failures occur first, and how much each one consumes from the application’s budget.

Generally speaking, we can divide performance tests into four main groups:

1. Load testing → Checks whether the system can handle the expected volume of users during both normal and peak usage periods
2. Stress testing → In this process, the load is intentionally increased beyond what is expected until the breaking point is reached. Stress testing draws on two concepts borrowed from structural design in civil engineering: the “Serviceability Limit State,” the point at which usability is compromised, and the “Ultimate Limit State,” the point at which the structure collapses. Stress testing maps these two limits in applications; knowing where they lie transforms an unknown risk into a number.
3. Spike testing → Checks whether the system can handle sudden spikes in traffic
4. Endurance testing (soak or endurance testing) → Identifies problems that would only appear after long periods of use. In endurance testing, a high load is maintained for hours on end with the intention of causing failures. These tests are typically performed before the system goes live in a production environment and simulate what would happen, for example, during the early morning hours—the worst time to discover an error.

Regardless of the type, they handle variations in system volume—whether in terms of users, data, transactions, or exposure time. What differs among them is the way this volume reaches the application.

Effective performance testing strategies combine more than one of these types, since real-world applications encounter all of these scenarios throughout their lifecycle.

Why are they essential for scaling applications?

The need to investigate how application performance evolves over time—especially in light of its growth—has become even more critical in a context where computational resources are widely available. This scenario, driven by AI and cloud computing, has placed performance testing at the center of the discussion in the field of Quality Engineering.

The availability of artificial intelligence agents capable of developing applications has greatly facilitated software development. Widespread access to cloud computing services has made it easier to launch these applications, even when they are still in the prototype phase.

However, there is a huge gap between launching an MVP and, after validating it, keeping it available and stable—while also making it possible to continue enhancing it. Although barriers to entry are easier to overcome, achieving steady growth remains a challenge.

In this scenario, performance testing becomes crucial. It is thanks to the vast number of automated tests that run almost continuously—thus defining quality as a service —that we are able to keep applications ready at all times to move up to the next level of scale.

For sustained growth, even a system’s scalability must be planned and monitored. Performance tests will show how it responds to automatic scaling mechanisms—known as “auto-scaling.”

As we mentioned at the beginning of this text, testing serves to identify the level of performance efficiency so that it can be improved or, in cases where it is already optimal, maintained. In these scenarios, performance tests also help manage cloud computing infrastructure costs. They allow you to determine the system’s actual demand for computational resources, keeping it running without wasting the budget.

Performance testing is one of the specialties of our Digital Platforms unit, as it focuses on the fundamentals that underpin growth at scale. Through our ongoing quality-as-a-service efforts, we help our clients operate applications with predictability, thereby providing the confidence needed for growth.

If the time has come to scale your company’s software and you want to carry out this process with a partner who specializes in this area, click here to get in touch! Our experts will demonstrate all of Inmetrics’ capabilities that ensure the scalability of systems in a secure and sustainable manner.

Frequently Asked Questions About Performance Testing

1. What are performance tests?
   These are tests that evaluate the efficiency of a system or component under different workloads by measuring response time, throughput, stability, and resource consumption.
2. What are the main types of performance tests?
   The four main types are load testing, stress testing, peak testing, and endurance testing. Each one answers a different question: whether the system can handle the expected volume, where it breaks down, whether it can withstand sudden spikes in traffic, and how long it can sustain a high load.
3. What is the difference between a load test and a stress test?
   A load test validates the system at the expected volume of use. A stress test intentionally pushes the load beyond that volume to determine at what point the system actually degrades or stops responding.
4. Do performance tests help reduce cloud costs?
   Yes. They reveal the system’s actual demand for computing resources at each usage level, preventing both oversizing—which wastes budget—and undersizing—which causes the application to crash.