Gatling Enterprise Cloud

My main use case for using Gatling Enterprise  is for doing load testing, performance testing, or API level testing for a lot of the API endpoints in my back-end services here at Intuit. We use Gatling Enterprise  for creating endpoints and then calling them and adding load, making sure our system scales properly.

We use Gatling Enterprise by defining the APIs inside Gatling. We create simulations and scenarios which are directly read by Gatling. In those scenarios, we define in the code what APIs we need to call. We then add load via the Gatling Enterprise user interface over a given amount of time by adding a number of users or requests for that particular scenario over a given time period. We then see how well our system is performing in that case.

The interesting thing about Gatling Enterprise is the fact that we can define scenarios wherein a particular scenario can have multiple API calls as a downstream happening in one scenario. This helps us load test a particular scenario or use case overall in depth. That really makes our system scalable.

The ability to add scenarios, the ability to configure concurrent users per second, and the ability to define how long a particular test should last are valuable features. After that, I can draw conclusive reports and graphs around how well the particular test took, how many API calls were successful versus how many errored out, how many were failing due to authentication errors, and what sort of granular errors I got. That really helps us understand the scale at which our system is performing.

I rely on the fact that we are able to add a particular number of users per second as simulations in the code or in Gatling Enterprise. The reason it is very important for my team is because it mimics the way actual users use our product in their day-to-day lives where there are periods wherein our system and product receive way more traffic, and that traffic results in way more downstream traffic, which Gatling Enterprise perfectly helps me capture.

It has led us to have really robust scalability testing, performance testing, and load testing, thereby making sure of our system integrity during crunch time or during times of peak traffic. It has helped build confidence in our systems.

We saw fewer outages and fewer periods of slowness in the product. Our number of customer complaints or customer escalations reduced by almost 20 to 30 percent over the period of a year when we initially started using Gatling Enterprise. As more and more teams are adopting this, the number is growing. It has led to real positive impact here.

The UI can be made a little better to understand. The overall user interface can be made so that non-technical people should be able to easily integrate with and understand what is happening in Gatling Enterprise. Additionally, there could be a really good AI integration in Gatling Enterprise, especially in the front end or UI part of it, wherein you should be able to describe a scenario in plain English, and the code should be able to automatically be generated.

I have used Gatling Enterprise for the last around five years, ever since I have been working with this company.

I have not experienced any stability issues with Gatling Enterprise.

Gatling Enterprise is pretty decent in terms of scalability. It scales well to all products and all services, and it is pretty uniformly implementable.

I have not needed to contact customer service.

It is definitely time and money saved in terms of user escalations and user actions occurring, but I am not able to speak on the exact details about it.

Before you start using Gatling Enterprise, please make sure to read all the documentation and then get up to speed with the UI because that is the biggest learning curve, but overall, it becomes smooth after that.

Gatling Enterprise is pretty decent. I have never needed to use customer support, but whenever needed, I find a good community online.

I gave this review a rating of 7 out of 10.

Public Cloud

Amazon Web Services  (AWS )

What do you like best about the product?

What I like most about Gatling is the flexibility and freedom it gives you when designing performance tests. It makes it easy to create reusable scenarios, build complex test chains, and structure tests in a maintainable way.

The ability to use a real programming language such as Kotlin is a major advantage, as it allows for advanced logic and customization that would be difficult to achieve with more restrictive tools. This gives me full control over how I design, organize, and execute my tests.

I also appreciate how clear and intuitive the reporting is. The reports are easy to understand and communicate to both technical and non-technical stakeholders. Overall, the coding-first approach and the level of control it provides are what make Gatling stand out for me.

What do you dislike about the product?

One area where Gatling could improve is troubleshooting and error analysis. The reports do not always provide enough detail about failed requests, which can make investigations more difficult. For example, when a request returns an HTTP 500 error, it would be helpful to see the underlying error message or stack trace directly in the report instead of having to search through application logs. Having more detailed failure information available out of the box would make debugging and root-cause analysis much faster and more efficient.

What problems is the product solving and how is that benefiting you?

The main problems Gatling solves for me are performance regression control and load testing under realistic conditions. It helps detect whether a new update has negatively impacted API performance, such as increased response times or reduced throughput, which is crucial for maintaining stability in production.

Another key benefit is the ability to simulate different load scenarios on various machine specifications. This makes it possible to evaluate how systems behave under stress and identify potential bottlenecks or scaling issues before they affect real users. Overall, Gatling provides valuable insight into performance trends and system reliability over time.

What do you like best about the product?

I like the flexibility I get in implementing data generators (feeders). This allows for representative and sufficiently random data that ultimately gives meaningful performance test results.
I also like the simulation run management and history offered by the enterprise product which lets non-engineers to access and use the results directly.
The technical documentation is an ever-evolving artifact but in its current state it is useful and well structured and helps newcomers create effective simulations, not just "hello world" versions.

What do you dislike about the product?

Some of our integrations (e.g. with Github workflows) are brittle, although I should add that they are improving both the integrations themselves and the relevant documentation.

What problems is the product solving and how is that benefiting you?

Infrastructure management (minimum viable configuration)
Infrastructure budget projections
Identification of throughput bottlenecks
SLO monitoring
Performance regression

What do you like best about the product?

The integration with other tools, along with the out-of-the-box reports, is what I like most.

What do you dislike about the product?

When I first started using Gatling, it was only compatible with Scala, but that’s no longer the case. What I dislike most is that it isn’t fully open source.

What problems is the product solving and how is that benefiting you?

As part of our broader software performance metrics work, Gatling helped me fine-tune request rates and limits across different microservices. That, in turn, made it easier to reduce our overall cloud costs.