Load Testing vs Performance Testing: Comparison & Examples

September 20, 2023

Load testing and performance testing are closely related concepts, and the terms are often used interchangeably in informal conversations. While both types of testing evaluate the performance of an application or system to identify areas for optimization, there are key differences in load testing vs performance testing.

Load testing evaluates how well an application or system performs under expected and peak workload conditions. The primary purpose of load testing is to determine the breaking point (i.e., the point at which the system no longer operates within acceptable tolerances) of a system’s weakest link. In other words, load testing answers the question: “What is a system’s maximum transaction processing capacity?”

Performance testing, on the other hand, measures the speed, responsiveness, and stability of an application or system while under a reasonable or expected workload. The primary purpose of performance testing is to measure response times and latency to ensure that a system meets performance requirements for end users or other software programs accessing it (for example, via an API). Performance testing answers the question: “How fast does the system respond to transaction requests?”

Using the definitions above as a starting point, this article will explore similarities and differences between load and performance testing. It will also provide practical recommendations on when and how best to utilize each type of test in your application.

Summary of key load testing vs performance testing concepts

Reviewing the similarities and differences in load testing vs performance testing helps build an intuition for each test's use cases. The tables below summarize the similarities and differences between these two popular types of software tests.

Load testing and performance testing similarities

Similarity Description
Purpose Assess application performance under various scenarios and workloads to identify bottlenecks, weaknesses, and areas for improvement. This helps identify defects in the system design and inform decisions on provisioning CPU, memory, network bandwidth, and disk I/O resources.
Testing environment Should be conducted in an environment that mimics production as closely as is feasible, given time and resource constraints.
Scripting Test scripts should reflect real user interactions as closely as possible while prioritizing high-risk, computationally expensive, or business-critical user behaviors.
Metrics Test data will include metrics, such as response time, throughput, resource utilization (CPU, memory, disk, etc.), error rates, and latency. Some tools also support the creation of custom metrics, such as the correctness of API responses, database size, and message queue length.

Load testing and performance testing differences

Difference Load Testing Performance Testing
Purpose Measures the system’s capacity to process concurrent users or concurrent transactions per second and aims to find the breaking point of a system’s weakest link. Tests can be run with many virtual users or scaled down to run tests faster and at a lower cost. Measures a system’s response time and latency from an end user's perspective or another software program accessing it while under a reasonable load.
Use cases Quantifies how an application behaves under high user load scenarios, such as during peak hours or special events. Can also determine whether a B2B company has sufficient computing resources to meet a client’s functional requirements or SLA. Identifies performance issues that directly impact user experience, such as server response times, database queries, and third-party API calls.
Scalability During a load test, the number of virtual users is gradually ramped up until the system reaches its maximum capacity or exhibits performance degradation. Scaling tests can be a significant pain point without proper tooling. May or may not come into play depending on the purpose and conditions of the test. In either case, performance testing still requires proper tooling to implement effectively.

Load testing vs performance testing: An in-depth look

In the following sections, we will examine the similarities and differences between load testing and performance testing in greater depth to provide a clear understanding of each test’s purpose, implementation considerations, metrics, and use cases.


Load and performance testing both aim to assess an application’s performance in predetermined load conditions. During each type of test, performance metrics such as throughput, response times, and latency are measured to identify system components that may break, perform outside of acceptable tolerances, or fail to comply with service-level agreements. Both tests can inform the planning of application infrastructure resources like CPU, memory, network bandwidth, and disk I/O resources.

Beyond these general similarities, there are significant differences between the two practices. First, while performance tests tend to keep load constant (with other test conditions altered), load tests frequently adjust load by changing the number of virtual users or ramp-up times to simulate different patterns. Some load tests (such as soak and spike tests) deliberately apply unusual workload patterns intended to cause performance degradation.

Additionally, while performance tests are primarily concerned with system response times under anticipated load, load tests typically simulate much higher load to break the system or strain various components or subsystems. In other words, performance testing places a system under reasonable load to measure the speed with which a system responds to transaction requests. By contrast, load testing puts a system under unusually high load to measure performance degradation during ramp up and peak load scenarios.

Testing environment

To accurately simulate a system’s response to real user traffic, both performance and load testing should be conducted in an environment that mimics production as closely as possible. Some organizations may test the production environment directly. Others — typically larger companies with more resources dedicated to QA — may create and test an exact, isolated replica of the production environment. For companies with fewer resources or who desire greater flexibility in their testing schedule, it is also common to test on a smaller-scale environment that is similar (but usually not identical) to production. This approach typically involves mocking some services and dependencies.

Organizations can utilize different approaches with one another to fulfill different test requirements. For example, a company may run full-scale tests on the production environment during dedicated maintenance windows while also maintaining a scaled-down testing environment for more frequent tests.

Choosing the right testing environment(s) for your organization will likely involve weighing tradeoffs between test accuracy, financial cost, and time spent configuring test environments, writing test scripts, and running tests.



Several best practices can help testers write and prioritize load and performance test scripts. Following these practices allows for more efficient and realistic testing, better ease and accuracy in interpreting test results, and the potential for saving time by utilizing similar test scripts for both performance and load tests.

In deciding which system components to test, it is essential to prioritize the most common, expensive, and business-critical user journeys. For example, an e-commerce application’s highest-priority journeys are likely related to login, search, and purchase. In addition, be sure to test high-risk user actions, such as those that rely on a component or subsystem with lesser-known or untested performance characteristics (e.g., a database system, message queue, or webhook).  Finally, writing test scripts that reflect lesser-anticipated user journeys is also advisable, such as users who drop off before completing an entire checkout or payment flow.

Selecting the right test tool can also save considerable time writing test scripts and interpreting results. We recommend choosing a tool that allows members of the development and QA teams to write tests in a language already included in your tech stack. In addition, ensure that the tool supports parameterized user inputs for fields like usernames and passwords. For a more fine-grained interpretation of test results, choose a tool with uniquely identifiable virtual users, which allows your team to precisely track the actions of each virtual user throughout the testing process.


Load and performance testing involves measuring various metrics to assess how well a system handles different user activity and stress levels. Monitoring these metrics provides insights into the system’s performance, scalability, and potential bottlenecks. Some common metrics for performance and load testing include:

  • Response time: the time (typically measured in milliseconds) it takes for an application to respond to a user request.
  • Throughput: the number of transactions or requests the application can process per unit of time.
  • Error rates: the percentage of requests resulting in errors relative to the total number of requests.
  • Resource utilization: the amount of a given resource (such as CPU, memory, or disk transfer speed) utilized during a test.
  • Latency: also known as remote response time, the time it takes for data to travel from one computing location to another (i.e., from client to server or vice versa).

Simply monitoring appropriate metrics does not in itself guarantee better application performance. Load and performance testing typically generate tremendous amounts of data, and its analysis can quickly become overwhelming without proper planning. To avoid this pitfall, ensure your organization has clearly defined performance goals and establishes baseline performance measurements. This will provide reference points to help assess the impact of code, infrastructure, or configuration changes on system performance over time.

To further simplify the analysis of test results, performance and load testing tools like Multiple allow for the creation of custom performance metrics. This will enable teams to define specific metrics–such as database size, correctness of API response, or Kafka queue length–that are most critical for their application’s success.

Sample graph of metrics generated from a load test (source)

Use cases

As mentioned previously, load testing primarily focuses on how an application handles heavy loads and high user activity, while performance testing examines the application's overall responsiveness, stability, and resource utilization. As such, each has slightly different (although sometimes related) use cases.

Performance testing can be used to establish an application’s baseline performance through metrics such as response time, throughput, and resource utilization under expected load. It is closely tied to user experience and ensures that a system responds to user actions quickly, providing an overall smooth and enjoyable experience. Performance testing can also be used for comparative analysis and regression testing to assess which version of an application best meets performance goals.

Load testing is primarily used to determine system capacity and how a system behaves during ramp up and peak usage periods. It is often performed in anticipation of seasonal sales, promotional events, or for business-to-business applications that need to support a large number of concurrent users or transactions. For example, stakeholders of a customer relationship management system need to know how many customers and data points their application can support to determine whether they can onboard a given client. In addition, load testing can be leveraged to test a system’s automated processes, such as autoscaling.


Although scalability is a concern in both performance and load testing, scaling load tests often presents more significant challenges. This is primarily because load testing aims to determine a system’s maximum transaction processing capacity. This means that load tests, by definition, must generate a high enough workload to strain a system beyond its usual processing capacity (which may have been determined through performance testing).

Many organizations conduct load testing using a custom solution that combines open-source software with in-house or cloud-based computing resources. While this approach has some benefits, scalability can present a significant challenge. Simulating a high enough load to stress a distributed, production-grade system usually involves provisioning and configuring multiple worker nodes as load generators, which takes considerable developer time. If the test plan also includes specialized load tests (such as spike or soak testing), more time is required to develop and maintain a system capable of generating the necessary load patterns.

Different types of load tests. (source)

Because of these difficulties, hosted SaaS load testing solutions have emerged to provide both the tooling and IT infrastructure needed to run and scale load tests. These tools largely abstract scalability concerns away and allow load tests to be set up and run with a lower investment of time for developers and DevOps.

Load testing and performance testing best practices

As we have seen, both load and performance testing help ensure overall software quality. However, choosing between load testing and performance testing and implementing each type of test can present challenges. To better inform your application testing decisions, here are three important considerations:

  • Do not make load and performance testing mutually exclusive. The choice between load testing and performance testing for an application is not a binary "either-or" decision. Both techniques can (and should) be utilized on different schedules and for different purposes. In general, load testing measures system capacity and emphasizes scalability concerns, while performance testing assesses how an end-user experiences an application.
  • Find the right tooling. Choosing an appropriate tool for load or performance testing eases the burden of writing and running tests. For more guidance beyond the recommendations already presented in this article, check out our Guide to the Must-Have Features for Load Testing Tools.
  • Revisit and revise your strategy. Creating a comprehensive test strategy is a complex and iterative process. Run load and performance tests regularly and keep thorough documentation of test conditions, parameters, configuration, and results to observe performance trends over time.



Load testing vs performance testing is a nuanced topic. The two types of testing are distinct but closely related practices. While each test has its individual purpose and use cases, both play an essential role in quality assurance efforts. With the information and recommendations presented in this article, organizations can begin to leverage load and performance testing in their software projects effectively.