A Guide to the Must-Have Features for Load Testing Tools

July 15, 2023
10 min

Load testing has historically been an essential part of software development because it allows companies to better understand how a system behaves when accessed by many concurrent users or under other real-world load conditions. This helps companies meet application performance goals while avoiding costly downtime and system failures that can erode consumer trust and undermine user satisfaction.

Before the widespread adoption of agile software development in the early 2000s, trained specialists often performed load testing using complex tools. This was done either in-house using dedicated teams and load generators or by a third party that provided the testing infrastructure, wrote and performed tests, and reported the results. Regardless of where testing was performed, some of the most significant challenges with these approaches to load testing were keeping costs low and managing development bottlenecks while testing was scheduled and completed. 

While open source is not new, in recent years, open-source and hosted commercial load testing tools have begun to be integrated into development workflows in ways that align better with agile methodology and modern DevOps practices as part of the “shift-left” trend. 

Open-source solutions include tools like JMeter, Locust, Predator, Vegeta, and, most recently, Postman. In general, open-source solutions have a lower upfront cost but are typically less flexible and provide a less intuitive way to define tests and view relevant metrics. In addition, many organizations with on-prem hosting choose to load test using open-source solutions behind a firewall for security reasons. 

That said, as more and more companies move computing resources into the cloud, the case for hosted load testing solutions has never been stronger. Because hosted load testing solutions combine more intuitive tooling with the IT infrastructure needed to run and scale load tests, hosted tools have the potential to allow companies to load test without in-house computing resources and with a much lower investment of time for developers and DevOps.

When selecting a load testing tool, organizations should consider several important factors, including the tool’s ease of adoption, compatibility with existing application infrastructure, and scalability. This article will address these and other considerations by presenting nine load testing tool features that will benefit a variety of software projects. 

Summary of must-have features for load testing tools

Desirable feature Description
Simulation of real users A simulation should realistically represent virtual user journeys via user workflows, API calls, and generating representative data.
Service hosting Hosting lets you avoid the effort and expense required to create a load-generation platform.
Indefinite scaling on demand The scale of the load test should be a parameter and not require more preparation.
Support for popular programming languages and common syntaxes Learning a new scripting language takes time and slows down the project.
Ability to run tests without requiring programming skills after setup Once set up by developers, anyone should be able to run tests and adjust test parameters.
Support for API load testing The tool should include features that help load test REST APIs, a common abstraction behind user interfaces in modern applications.
Ability to test any middleware technology The tool must be able to load test technologies like Kafka, Redis, and MySQL to test key bottlenecks.
Support for various protocols for testing modern applications based on microservices A good tool should be able to test microservices in isolation and provide support for testing the system as a whole.
Inclusion of key metrics in the results report The transaction rate, error rate, and latency should be measured and reported for the full scope of the test. Ideally, the tool should also be able to accommodate additional metrics, which could possibly be business-specific.

Explanations of desired features

The following sections will expand on the features summarized above and provide practical tips for how your organization can implement load testing effectively.

Simulation of real users

The primary goal of load testing is to assess the performance of a system under real-world conditions. One challenge in doing this effectively is that the behavior of real users is variable and unpredictable, which makes it difficult to automate. Because of this, most load testing tools generate load with virtual users (VUs), which run scripts written to simulate the expected client behavior of a tested system. A sample VU script simulating a user login process is included below. (Note that this example is JavaScript, but many different languages can be used.)

const apiClient = axios.create({ baseURL: 'https://www.mybaseurl.com' })

const { data: { token } } = await apiClient.post('/auth/signup', {
  email: 'user@test.com',
  password: 'testPassword#123'

if (token) {
  // user is now logged in
  apiClient.defaults.headers.common['Authorization'] = `Bearer ${token}`

The primary benefit of employing VUs running test scripts is that they more accurately represent user journeys than (for example) simply configuring a large number of HTTP requests to hit a given API endpoint. However, many load testing tools still have limitations regarding the supported behavior of scripts run by VUs. To ensure that your load testing tool provides the functionality required to meet your testing goals, consider the following: 

  • VUs should be unique and identifiable: Simulating activity across many distinct users will likely test the impact on a system differently and in a more realistic way than automating one user to perform the same task many times. For example, a popular e-commerce site is likely to experience many concurrent users browsing different products, checking out at different times, and performing operations related to authentication and account management.
  • VU journeys should not end after logging in: To most accurately represent user journeys, VUs should be able to continue to make API calls after being authenticated.
  • VU tests should support control flow logic: Rather than repeatedly reproducing the same user flow, VUs should take different actions based on varied API responses and application states.
  • VUs should maintain open connections to system services: Maintaining multiple consistent, simultaneous client connections to system services such as databases and WebSockets more accurately represents real-world application behavior.

Note that the list above is not exhaustive and does not apply to every use case. Your team should consider the criteria above as a starting point to determine which supported VU features are needed to test your application.

Service hosting

Using a hosted service for load testing offers several benefits over relying on in-house, SaaS, or open-source solutions. Like other hosted services, hosted load testing removes the need for on-premises or cloud computing resources that can only be used intermittently and the requirement to have team members with special skills to configure and maintain those resources. 

Hosted load testing offers extreme scalability without requiring the purchasing and management of compute resources to adjust the simulated load. 

Finally, hosted load testing has the advantage of more easily simulating web traffic from multiple machines and locations, which more realistically reflects traffic for applications with users from different geographic regions. This makes confirming the performance goals of local servers, CDNs, and other location-specific factors more manageable.

It is possible to leverage some of these benefits with a custom cloud-based load testing solution using open-source tools. However, this approach is much more complex and involves spending considerable developer time to write tests and set up, configure, and tear down cloud infrastructure. In addition, hosted load testing solutions like Multiple facilitate greater collaboration as well as allowing teams to easily manage test scripts and results.

Indefinite scaling on demand

As described above, scaling load tests to simulate a higher level of traffic entails varying levels of complexity and effort, depending on the types of tooling and computing resources utilized. Scaling an in-house load testing solution often involves provisioning more computing resources and can incur higher hardware, software licensing, cloud resource, configuration, and maintenance costs. It can also create network bandwidth issues where the network infrastructure becomes saturated, leading to network bottlenecks that can affect the accuracy of test results.

We recommend choosing a load testing tool that handles the technical and logistical challenges of scalability for you to allow your team to test with any number of virtual users without the overhead of other scaling methods.


Support for popular programming languages and common syntaxes

Another common challenge in load testing is that many tools require learning a proprietary or uncommon scripting language (such as Groovy for JMeter) or other specialized training. Resources with these skills are often difficult to find, development time is increased, and testing efforts are delegated to a smaller number of team members, which creates bottlenecks and is incompatible with the decentralized principles of agile software development. Frequently, the end result is that companies reduce the amount and quality of load testing they perform, which causes software quality to suffer.

Choosing a load testing tool that does not require team members to learn a new scripting language removes a barrier to adoption and benefits the company’s overall testing culture and efficiency. We recommend selecting a tool that supports a familiar scripting language frequently used in your application’s tech stack. This lets developers avoid switching languages and enables them to more easily find in-house or online support when difficulties arise.

Ability to run tests without requiring programming skills after setup

Once the tests have been written and the initial testing parameters configured, tests will need to be run regularly, and parameters will need to be adjusted to simulate different ramp-up patterns and levels of traffic. To do this, many tools require developers to execute complex command line scripts, configure IT infrastructure to handle scaling, or write additional code to adjust test conditions. 

Choosing a tool that minimizes the technical knowledge required to accomplish these tasks allows team members with little or no technical knowledge to contribute to load testing efforts. This frees up developer time and gives organizations more freedom in the scope and scheduling of their load testing.

Support for API load testing

Although most load testing tools can be maneuvered to perform API load testing, there are several features that will allow your team to test APIs more easily. 

For example, the tool should allow you to configure different types of HTTP requests and to customize headers, parameters, payloads, and authentication methods for each request. In addition, it is usually desirable for the tool to have built-in support for monitoring response time and latency, as these metrics are crucial to identifying bottlenecks in a system. 

Ability to test any middleware technology

In addition to APIs, modern applications rely heavily on middleware technologies such as message-oriented middleware, caching services, databases, and web servers. Load testing these services in the context of a distributed system is essential to identifying bottlenecks in each separate component and the system as a whole. Furthermore, teams that shift load testing efforts left require the ability to load test individual components early as they are instantiated and integrated into the system.

Load testing a given middleware technology presents different challenges depending on its implementation and role within the system. It is, therefore, essential to evaluate testing goals carefully and choose a load testing tool that supports the features required to meet those goals. Some features to consider are:

  • Scripting: Does the tool allow testers to create scripts that simulate realistic user scenarios, such as sending and receiving messages, connecting to databases, and executing transactions?
  • Message payloads: Can the tool handle the complex message payloads that are typical in middleware technologies?
  • Scalability: Does the tool easily scale up to simulate large numbers of users or transactions to test the scalability of middleware technologies?
  • Authentication: Does the tool support security features, such as OAuth and JWT, to test the security of middleware technologies?

Support for various protocols for testing modern applications based on microservices

Load testing applications built with microservices present unique challenges because of the distributed nature of microservices, reliance on interservice communication using different protocols, and dependencies on other services. While load testing each microservice in isolation may be relatively straightforward and could provide some insight into a system’s performance, gaining a more complete picture is often more complex and involves testing individual services in addition to verifying interactions between different microservices.

There are several protocols commonly used to facilitate communication between microservices, such as HTTP, RPC, WebSockets, and message queue protocols. Choosing a load testing tool with explicit support for a variety of protocols and communication methods eases the technical burden of testing existing components and provides greater flexibility for testing any additional protocols down the line.


Inclusion of key metrics in the results report

A load testing tool that provides comprehensible reports on key metrics offers valuable insights into a system’s performance, stability, and scalability. This is crucial in identifying bottlenecks and determining whether an application meets performance requirements. 

While the appropriate metrics to track will vary from application to application, some metrics to consider include the following:

  • Transaction rate: The number of transactions processed per unit of time. This helps assess the system’s ability to handle a specific transaction load.
  • Error rate: The percentage of failed requests or transactions during the test. A high error rate may suggest performance issues or inadequate system resources.
  • Network latency: The time taken for data to travel from the user to the system and back. High network latency indicates slow responsiveness and can lead to a negative user experience.

For these and other metrics, it is important to establish performance goals and benchmarks using a lower level of load before ramping up load testing efforts. This ensures that foundational data and system performance measurements are established. Metrics from future load tests can then be compared against them and the results interpreted. Hosted load testing solutions like Multiple also allow for the creation of custom metrics, which can be useful to measure the user experience, correctness of results, database performance, CPU usage, and other relevant results. This allows your team to track mission-critical metrics while filtering out less relevant data points that may cause confusion in interpreting test results.


Load testing plays a vital role in the software development process and helps ensure the consistent performance of any application that experiences high traffic volumes. Although load testing can be challenging to implement, modern load testing tools have been developed to alleviate many of the technical and financial burdens of earlier load testing methods. We hope the features and considerations described above help you choose a tool suitable for your application and allow your team to leverage the many benefits of load testing in both current and future software projects.