Cloud Load Testing: Tutorial & Best Practices

February 20, 2024

Cloud load testing is a dynamic approach to load testing that leverages cloud computing resources to simulate real-world user traffic and stress conditions on applications. Unlike traditional on-premises load testing, which involves setting up and maintaining dedicated infrastructure, cloud load testing makes use of the scalability and flexibility of cloud resources to conduct load tests with more flexibility and at lower cost. This testing methodology helps identify performance bottlenecks, scalability issues, and response time constraints. It allows developers and QA teams to optimize the application’s architecture, configuration, and codebase as the application evolves.

To facilitate robust and scalable load testing, cloud-based load testing services have emerged to provide a range of functionalities, from load generation to test case and data management. Some cloud testing services also offer collaborative features, such as the ability to share the test data, scripts, and results across different teams within an organization with role-based access control (RBAC). This further enhances the ease and organization of testing by offering the ability to manage infrastructure, test cases, test data, and results all in one place.

This article will explore cloud load testing best practices and provide practical recommendations for how to choose appropriate tooling and conduct load tests in cloud environments.

Summary of key cloud load testing best practices

The table below outlines five essential best practices for conducting cloud load testing. A more detailed discussion of each practice is presented in the sections that follow.

Concept Description
Manage test data effectively Use anonymized or synthetic data to maintain privacy and security.
Choose appropriate tooling Consider several factors, including the tool’s ease of adoption, compatibility with existing application infrastructure, and scalability.
Employ automation Integrate load testing into CI pipelines to address performance considerations early in the development lifecycle.
Test across protocols Conduct tests across each protocol your application uses. This ensures comprehensive and realistic testing of the entire application.
Distribute testing across multiple regions Geo-distributed load testing assesses the performance of geo-targeted features and system components like load balancers, CDNs, and distributed microservices.

An in-depth look at cloud load testing best practices

The best practices below form a strong framework for effective cloud load testing. They enable developers to identify and address potential performance issues before deployment and help ensure a resilient and efficient application. The following sections delve into the importance of each practice and provide practical tips for how to implement cloud load testing in software projects.

Manage test data effectively

The significance of data management is frequently overlooked when conducting tests. Consequently, organizations might resort to ad hoc approaches to generating test data or use poorly managed production data, which introduces the potential for inaccurate test outcomes and susceptibility to data breaches.

There are multiple challenges to consider when dealing with test data, including the following:

  • Storage: Store test data centrally for ease of access. Developers should run test suites with the same data sets to ensure consistent and reliable test results. 
  • Updates: Ensure that test data reflects any new features or data models as the application evolves.

Security: Maintaining data security typically involves the strategic use of anonymized and synthetic data in load testing (data masking) as well as other techniques like tokenization and encryption. These practices safeguard user data while maintaining a representative data set for accurate load testing.

As an example of data security, consider a financial services application handling sensitive transaction data. Data masking can be applied to conceal specific data elements, such as financial data or personally identifiable information, while still allowing the testing team to employ realistic test scenarios. Alternatively, libraries like Falso can be used to generate fake data programmatically within test scripts, thus eliminating privacy concerns. 

The goal of implementing data security practices is to facilitate realistic and accurate testing while protecting sensitive information, maintaining industry data privacy standards, and adhering to data compliance regulations like the European Union’s GDPR. The following code shows an example of how fake data can be used in a load test script run on the Multiple platform:

const { randAddress, randFullName, randEmail } = require('@ngneat/falso');

class MyTestSpec {
  npmDeps = {
    '@ngneat/falso': '6.4.0',

  async vuInit(ctx) {
    // Set the base url of Multiple's built-in axios instance
    // The built-in axios instance automatically captures metrics
    ctx.axios.defaults.baseURL = process.env.API_BASE_URL;

  async vuLoop(ctx) {
    // Generate fake data using Falso
    const userData = {
      name: randFullName(),
      email: randEmail(),
      address: randAddress(),
      // Add other necessary fields for account creation

    // Send a POST request to the accounts endpoint
    await'accounts', userData);

As you can see, the test script above makes use of the Falso and built-in Axios NPM libraries to make a POST request with a fake name, email, and address. The test script simulates traffic on the API under test without exposing sensitive data and measures response times for successful requests and errors.

Choose appropriate tooling

When evaluating load testing tools, it is essential to consider the nonfunctional testing requirements for your application. Load testing tools can be broken down into three broad categories: open-source tools, third-party vendors, and cloud-based SaaS solutions. Each has advantages and considerations that will be discussed below.

Open-source tools

Open-source tools are freely available and—in some cases—have wide community support. This fosters a collaborative community around the tool and provides transparency into the tool’s inner workings. 

However, open-source tools have their limitations, such as steep learning curves and less intuitive user interfaces. They may require developers to learn a proprietary scripting language and configure computing resources to run and scale load tests. It is also important to evaluate open-source tools’ support for different communication protocols, such as gRPC, message queue protocols, AMQP, or GraphQL. This consideration will be discussed in greater detail in a later section.

User interface of Apache JMeter (Source)

Third-party test vendors

Many vendors offer end-to-end load testing services to help companies avoid the need for in-house developers to learn and maintain load testing tools and infrastructure. These vendors take over the load testing process entirely, handling everything from writing test scripts, conducting the tests, and generating result reports to providing actionable recommendations based on test results. 

The primary considerations when choosing this approach are the cost of the service and whether the vendor and the vendor’s testing timelines align with the client company’s testing goals. This solution may not be feasible for client companies hoping to implement the “test early, test often” philosophy or to integrate load testing into CI/CD pipelines.

Cloud-based tools

More recently and to address the limitations of other load testing solutions, cloud-based SaaS load testing tools have emerged. A primary benefit of these tools is their inherent scalability: They leverage cloud infrastructure to distribute and manage the testing load, which effectively alleviates scalability concerns. Tools like Multiple additionally allow developers to write tests in a common scripting language and offer intuitive, user-friendly interfaces ease the tool’s adoption and allow both technical and non-technical team members to configure and run load tests.

The user interface for creating a load test script in JavaScript using Multiple (Source)

Many cloud SaaS solutions also provide dedicated support, ensuring timely assistance and issue resolution. In addition, some include advanced features such as comprehensive reporting, the ability to share test scripts and data with role-based access control (RBAC), and seamless integration with continuous integration or continuous deployment (CI/CD) pipelines.

Choosing an appropriate load testing tool depends on several factors, such as an organization’s size, the nature of the application under test, and the available QA resources. For more information on choosing effective tooling, check out our Guide to the Must-Have Features for Load Testing Tools.


Employ automation

In the rapid release cycles of agile software development, implementing automation is crucial for maintaining an efficient testing approach. Automating load tests brings various benefits, particularly when it is implemented early in the development process and performed frequently. These benefits include:

  • Early detection of performance issues
  • Better understanding of the performance impact of code changes
  • The ability to evaluate different options for system configuration and architecture
  • Enabling informed decisions based on historical test data

In an ideal scenario, automated load testing can be incorporated into CI processes to run early and often. However, it is important to note that running load tests with great frequency–such as with every pull request–is not realistic for many organizations due to time and resource constraints. A common solution for such organizations is to adopt a tiered approach, where small-scale load tests are run more frequently to provide regular feedback while full-scale tests are reserved for major updates or anticipated high-traffic periods. This strategy allows organizations to balance frequent testing with time and resource efficiency.

Test across protocols

For applications that rely on multiple communication protocols, testing across different protocols is imperative to ensure a thorough evaluation of the entire system. Some common protocols in modern distributed systems include the following:

  • Hypertext Transfer Protocol / HTTP Secure (HTTP/HTTPS)
  • File Transfer Protocol (FTP)
  • Simple Mail Transfer Protocol (SMTP)
  • Transmission Control Protocol (TCP)
  • Message Queuing Telemetry Transport (MQTT)
  • Advanced Message Queuing Protocol (AMQP)
  • WebSocket

There are numerous approaches to load testing different protocols, depending on the nature of the system, the protocols under test, and the load testing tool itself. While some load testing tools may require installing and configuring third-party plugins to test different protocols, tools like Multiple allow developers to utilize third-party packages (such as NPM) to write test scripts similar to frontend or backend code.

Distribute testing across multiple regions

Simulating load across different geographic regions helps evaluate the location-specific characteristics of global or distributed systems, such as network conditions, CDNs, and geo-targeted features. If your application is deployed across many regions or serves a geographically dispersed user base, geo-distributed testing may be necessary to ensure consistent performance for all users.

Example architecture of a web app that operates in multiple geographic regions.

This approach is not necessary for every application. However, as many modern distributed systems have grown to include more microservices, edge computing, serverless architectures, and backend-as-a-service platforms, the need for geo-distributed testing has never been greater. The table below provides a list of system components and architecture styles that may benefit from (or require) geo-distributed testing:

Component/architecture Approach
Content delivery network (CDN) Test CDN edge servers to evaluate response times, content delivery speeds, and the impact of different caching strategies.
Load balancer Ensure that load balancers distribute global traffic efficiently to minimize response times. Consider testing both static and dynamic load-balancing algorithms to find an effective approach for your application.
Internet of Things (IoT) systems Simulate the behavior of users interacting with IoT devices, which are often dispersed across a variety of geographic locations and may utilize lightweight communication protocols like MQTT or AMQP.
Distributed microservices Microservices often have services deployed across different regions. Test the performance of each individual microservice as well as the communication and interaction between microservices.

Using distributed testing, developers can simulate user interactions from multiple locations, ensuring that the application’s performance is optimal for users worldwide. When issues are identified, engineering teams can respond accordingly by scaling resources in the affected region, optimizing server configurations, or implementing different load-balancing strategies.



As engineering teams navigate the intricacies of load testing in cloud environments, the best practices above serve as invaluable tools. While there is no one-size-fits-all approach to load testing, we hope that the strategies above will help your team integrate testing more effectively into its development process. By doing so, your team will gain valuable insights into application performance, allowing you to make informed decisions on how to optimize code, fine-tune infrastructure, and ultimately ensure a consistent user experience.