Load Testing vs Stress Testing: Tutorial & Comparison

May 6, 2024

While load and stress testing both fall within the realm of nonfunctional testing, each has a different focus and assesses system performance under different conditions.

Load testing assesses an application’s performance under anticipated and peak workload scenarios. Its primary aim is to determine the point at which the system no longer operates within acceptable tolerances. This type of test helps answer the question: “Can the system handle the user load it is expected to encounter?”

On the other hand, stress testing intentionally pushes a system beyond its intended capacity. It simulates extreme user loads, significantly exceeding anticipated usage, to uncover breaking points and behavior under unforeseen pressure. The goal is not to replicate real-world peak scenarios but rather to identify the system’s limitations and potential weaknesses that might emerge under excessive stress. This allows developers to understand the system’s resilience and prepare for unexpected load conditions.

In the sections that follow, we provide actionable steps and best practices to navigate both load and stress testing and ensure that your system is prepared for various scenarios.

Summary of load testing vs stress testing key concepts

The table below compares load and stress testing in terms of their purpose, use cases, and tooling.

Load Testing Stress Testing
Purpose Assess system performance under expected and peak loads, with a focus on performance optimization. Identify the system’s breaking point and behavior under extreme loads, with a focus on evaluating the system’s resilience and analyzing failures.
Use cases Common use cases include measuring system performance before deployment, identifying bottlenecks impacting user experience, and validating system readiness for anticipated user growth. Common use cases include testing system resilience against unexpectedly high loads, discovering potential software faults under extreme conditions, and evaluating system recovery mechanisms after failures.
Tooling Choose a tool that supports testing different communication protocols, is compatible with popular tech stacks, provides a hosted infrastructure, and allows tests to be written as code (as opposed to a custom XML or config file). Stress testing tools benefit from many of the same features as load testing tools. In particular, hosted tools allow for easy scalability during stress tests.


Load testing: Testing system limits

Load testing enables developers to identify potential bottlenecks and performance limitations that could hinder the user experience within expected operational boundaries. The goal is to ensure that the system can handle the anticipated volume and variety of user activity it is designed for, consistently meeting performance expectations and service level agreements (SLAs). Another critical purpose of load testing is to establish the application’s point of overload, which is the maximum number of concurrent users it can manage without experiencing performance degradation.

Stress testing: Testing system resilience

Stress testing exposes the system to extreme loads to identify weaknesses and potential failure points that might not be apparent under normal usage conditions. This allows developers to proactively address these issues and enhance system resilience.

Another purpose of stress testing is to provide insights into the system’s ability to recover gracefully from failures. By observing how the system behaves after experiencing crashes or heightened load (“stress”), developers can evaluate and improve recovery mechanisms to ensure a swift return to normal operation.


Load testing best practices

Here are a few best practices to keep in mind when performing load tests.

Perform baseline tests and monitor performance regression

Load testing should not be treated as a one-time activity. Establish baseline performance metrics for your system under load. Then, as you iterate on your application’s codebase and infrastructure, run your load tests regularly to monitor for performance regressions. Early identification of performance regressions allows you to address issues early in the development lifecycle.

Isolate microservice performance

Modern applications often rely on interactions among numerous microservices. To effectively pinpoint performance bottlenecks, make sure to test individual microservices in isolation. This is often done using service virtualization. For more information on microservices performance testing, check out our free guide.

Consider data-driven testing approaches

Traditional load testing often relies on static test cases with predefined data values. While this approach can be effective for basic scenarios, using static data for all tests does not accurately reflect real-world scenarios. Consider data-driven testing approaches by adding parameterized inputs in test scripts and injecting test cases with realistic data sets. This increases efficiency in the testing process by allowing developers to write fewer and more modular test scripts while still testing a variety of user workflows and data manipulation scenarios.

Stress testing best practices

Before beginning to conduct stress tests, it is important to establish system performance baselines through load or performance testing. Doing so provides benchmark metrics that can be used as points of comparison as stress tests begin pushing the system beyond its normal capacity. The following are a few other best practices to keep in mind when performing stress tests.

Define “breaking-point” criteria with SLAs in mind

Do not simply push the system until it breaks. Clearly define what constitutes the system’s breaking point—the point at which the system no longer operates within acceptable tolerances—based on your organization’s business goals and SLAs. This could be exceeding a certain threshold in terms of the system’s error rates or response times, or it could mean complete system unavailability for a certain duration. Thoroughly understanding business requirements and SLAs allows you to craft stress testing scenarios strategically to identify potential violations under extreme load conditions.

Test security implications under stress

Do not forget about security under pressure. Stress testing can inadvertently expose vulnerabilities that might not be apparent under normal loads. Integrate security testing tools like vulnerability scanners or penetration testing frameworks into your stress testing process. Run security scans concurrently with your stress tests to identify potential security weaknesses that might be exploited during high-traffic scenarios.

Measure recovery time objectives (RTOs) and recovery point objectives (RPOs)

Measuring the disaster recovery (DR) capabilities of a system or application is vital. Use stress testing to measure your system’s ability to recover from failures within your defined RTOs and RPOs. This helps you assess the effectiveness of your DR plan and identify areas for improvement in data backup, failover procedures, and automated recovery mechanisms.

Common use cases for load testing

Load testing should not be a single, resource-intensive exercise. An efficient approach incorporates different levels of testing throughout the development lifecycle:

  • Small-scale tests: Frequent, small-scale tests with a limited number of simulated users can be conducted during development phases. These tests help identify and address performance issues as features are built and integrated, preventing problems from snowballing into major roadblocks later on. Small-scale tests are generally conducted earlier in the lifecycle and more frequently than full-scale tests.
  • Full-scale tests: Large load tests with a realistic number of virtual users are costly to perform. As such, many organizations may choose to conduct them only for critical milestones, such as in anticipation of major releases or events that are expected to attract a significant user base. For example, a video streaming platform may conduct full-scale load tests in anticipation of a major live event broadcast. These more comprehensive and realistic load tests provide valuable insights into the system’s capacity to handle peak loads and ensure it can perform optimally under anticipated real-world conditions.

Load testing can also be carried out at different granularity levels:

  • Individual components: When performance issues arise in a complex system, pinpointing the root cause can be challenging. Granular load testing streamlines troubleshooting by enabling developers to isolate the exact component causing performance degradation, which facilitates quicker resolution times and minimizes disruptions to the overall development process. Isolating components during load testing can also allow developers to assess different components’ scalability characteristics. This is particularly crucial for microservices architectures where individual services may have varying resource requirements.
  • Entire system: End-to-end load testing goes beyond individual components and exposes potential bottlenecks arising from inter-component interactions under load. This can reveal issues like communication delays, data synchronization problems, or cascading failures that might not be evident through isolated component testing.

Common use cases for stress testing

Stress testing goes beyond just identifying bottlenecks: It is also used to assess the overall resilience of a system under extreme pressure. Here are a couple of examples of common stress-testing scenarios:

  • Simulating a system outage that could occur during peak usage hours: This could involve simulating a complete system failure or partial outages affecting specific functions. The test can assess failover mechanisms, data integrity, and recovery time objectives to ensure that system functionality and data are protected.
  • Simulating a cybersecurity attack such as a distributed denial-of-service (DDoS) attack: This kind of attack aims to overwhelm the system with traffic and prevent legitimate users from accessing the platform. Simulating a DDoS attack helps assess the system’s security measures, intrusion detection systems, and ability to mitigate such attacks while maintaining functionality for authorized users.

Stress testing can also be utilized to evaluate an application’s scalability, particularly when deployed on cloud resources with autoscaling capabilities. Ramp-up and ramp-down testing are two common ways to test the system’s ability to handle increasing and decreasing workloads:

  • Ramp-up testing: This simulates a gradual increase in user load, mimicking a real-world scenario where user activity builds over time. This helps identify potential bottlenecks and resource limitations at different load levels, allowing for adjustments to cloud resource configurations (like adding virtual machines) to ensure efficient system scaling to meet growing demand.
  • Ramp-down testing: This simulates a sudden or gradual decrease in user load, which can occur during off-peak hours or after resolving an unexpected surge. This helps assess the system’s ability to gracefully scale down resources to optimize costs and prevent resource waste.

By testing both ramp-up and ramp-down scenarios, developers can determine the optimal cloud resource configuration to balance performance, cost efficiency, and resource utilization.


Developers often use the same tools for both load and stress testing. However, when using the same tool for both, the flexibility and scalability of the tool itself play a crucial role. Here are a few considerations:

  • Configuration: As discussed previously, load tests typically involve a smaller number of virtual users (VUs) with a gradual ramp-up pattern, while stress tests employ a much higher number of VUs with a steeper ramp-up to simulate extreme load scenarios. Tools like Multiple allow for easy configuration of these parameters (number of VUs, ramp-up duration, test duration, etc.), which makes it suitable for both load and stress testing with minimal effort.
  • Scalability: The testing infrastructure also needs to be scaled accordingly. Load tests might require a moderate increase in resources, while stress tests might necessitate significant scaling to accommodate the high volume of VUs. Cloud-based solutions can be advantageous in this regard, offering on-demand scalability to handle the varying demands of load and stress testing.

When selecting a load or stress testing tool, we recommend considering these other key features to ensure comprehensive testing and efficient execution:

  • Support for various communication protocols: A tool that supports a wide range of communication protocols (such as HTTP, RPC, AMQP, MQTT, etc.) allows you to test your system across different layers. This holistic approach helps pinpoint bottlenecks and performance limitations not just at the application level but also within network communications, database interactions, message brokers, and other underlying components.
  • Integration and compatibility: Look for a tool that integrates seamlessly with your existing technology stack. This streamlines the testing process and reduces setup time. Additionally, consider your team’s skillset and choose a tool that is intuitive and user-friendly for them to learn quickly and operate efficiently.
  • Hosted infrastructure: Cloud-based load and stress testing solutions offer significant advantages. They provide on-demand infrastructure, eliminating the need for you to manage on-premises hardware and software resources. This translates to cost savings and flexibility: It eliminates the need for in-house developers to provision and maintain the testing infrastructure, and it allows you to run tests at any scale without worrying about the capabilities of the underlying infrastructure. This is particularly useful when simulating extreme user loads during stress testing.
  • Scripting language: Avoiding proprietary languages specific to the testing tool eliminates an additional learning curve for your developers and testers. They can leverage their existing skills and knowledge of the organization’s tech stack to start using the tool effectively much sooner. This fosters faster adoption and a smoother testing process.

As discussed above, many testing tools can be adapted for both load and stress testing by adjusting the script parameters and user load settings. The key lies in understanding the nuances of each type of testing and configuring the tool appropriately.

The test script below simulates POST and GET requests to a chat API. It is written for the Multiple platform and uses Faker to generate fake test data:

// faker for generating synthetic data
import { faker } from '@faker-js/faker';

class ChatTestSpec {
  npmDeps = {
    '@faker-js/faker': '7.6.0',

  defaultRunOptions = {
    numVUs: 50,
    testDuration: 300000, // ms
    rampUpDuration: 30000, // ms
    minVULoopDuration: 1000, // ms

  async vuLoop(ctx) {
    // Send a POST request to the chat endpoint with a random message
    await ctx.axios.post('chat', {
      // Generate synthetic data with faker
      message: faker.lorem.paragraph(),

    // Send a GET request to the chat endpoint
    await ctx.axios.get('chat');

The defaultRunOptions property defines the default configuration for running the test. These options include:

  • numVUs: The number of concurrent virtual users, set to 50 in this case.
  • testDuration: The total duration of the test (in milliseconds), set to 300 seconds (5 minutes).
  • rampUpDuration: The time it takes for the number of virtual users to reach its peak, here set to 30 seconds. This creates a gradual increase in load.
  • minVULoopDuration: The minimum amount of time (in milliseconds) between the beginning of each vuLoop, set to 1 second. This introduces a slight delay between user actions to mimic the wait time of real users.

You can use the same script to run a stress test. The only adjustment required is to alter the defaultRunOptions property. You can do this by taking at least one of these steps:

  • Increase the number of virtual users (numVUs): For a stress test, you would want to push the system beyond its normal capacity. The exact value depends on the system’s capabilities, but it could be several times higher than the typical user load.
  • Adjust the ramp-up duration (rampUpDuration): Load tests typically use a gradual ramp-up to mimic a natural rise in user activity. A stress test may use a similar ramp-up pattern with a higher number of VUs or may seek to simulate a sudden surge of activity. Adjusting this parameter accordingly allows for both scenarios.
  • Adjust the minimum virtual user loop duration (minVULoopDuration): This property controls the minimum delay between actions for each virtual user. In a stress test, you might consider reducing this delay slightly to increase the overall load on the system. The exact value will depend on several factors related to the system and the test goals.

Below is a sample defaultRunOptions object adjusted for a stress test. Note that this configuration is provided as an example; the exact values will vary depending on your use case.

defaultRunOptions = {
  numVUs: 1000,  // Significantly higher number of virtual users
  testDuration: 300000, // Maintain the same test duration
  rampUpDuration: 1000,    // Fast ramp-up
  minVULoopDuration: 500, // Slightly reduced delay between actions


Selecting the ideal testing approach is not a binary decision between load and stress testing. Instead, understanding the goals of your project and the specific scenarios you aim to test is critical. Load and stress testing are complementary–not mutually exclusive–strategies: Both types of testing offer valuable insights, and their combined application can provide a comprehensive picture of your system’s performance under various conditions. Here is how to leverage them effectively.

Start with load testing

Begin by simulating expected user activity patterns and peak loads to identify performance limitations within operational boundaries. This helps ensure that the system can handle the typical usage it is designed for.

Utilize stress tests strategically

Stress testing is generally more costly and time consuming, so it is typically not performed as regularly. However, once you have a baseline understanding of performance within expected bounds, it is beneficial to introduce stress tests to push the system beyond its intended capacity. This helps uncover potential weaknesses and vulnerabilities that might not surface under regular use but could be critical during unexpected surges or disruptions.

Conduct regular testing throughout the development lifecycle

Do not limit stress and load testing to pre-deployment or other late-stage development phases. Integrate these practices throughout the development lifecycle, including early development stages and regular code deployment cycles. This proactive (shift-left) approach allows you to identify and address performance issues early on. For example, if load testing is conducted weekly and a given test shows significant performance degradation, the underlying issue becomes significantly easier to pinpoint than in a scenario where the last load test was run three months prior.

The concept of shifting testing left in the development lifecycle (source)

The benefits of early load and stress testing go beyond simply preventing issues in production (although this is a significant benefit in and of itself). Testing early also saves valuable time and resources by catching performance problems sooner in the development process, preventing these issues from becoming more problematic in later stages.



Load testing and stress testing are essential for ensuring a system’s performance and resilience. The primary focus areas of load and stress tests differ, but it is important to note that they are complementary testing strategies.

Load and stress testing help development teams gain a holistic understanding of the system’s capabilities and limitations when used strategically and in combination. They help identify areas for performance optimization, pinpoint weaknesses that could lead to outages, and ultimately build applications that are reliable, resilient, and scalable in the face of real-world pressures.