How AidenAI delivers high-performance apps at scale with Gatling

AidenAI had to meet client SLAs—at speed, and at scale.

• Projects demanded NFR validation (users, response time, throughput)
• Every client app was different: new clouds, regions, and release schedules
• They needed a tool that could scale and support distributed teams
• The team needed fast, collaborative, region-specific load testing

Each AidenAI client comes with its own rules: new tech stacks, global users, strict response time targets. Load testing isn’t optional—it’s required before anything hits production.

The challenge? Doing this efficiently for dozens of apps at once. 

The QA team needed to simulate global traffic, validate performance under pressure, and reuse tests across projects—while meeting strict non-functional requirements tied to client SLAs. Legacy tools couldn’t keep up. They lacked distributed architecture, made large-scale simulation difficult, and weren’t built for collaborative teams working across regions.

• 20–30% faster response times across client applications
• Simulated load from geo-restricted regions using dedicated IPs
• Enabled team collaboration with centralized access to simulations
• Plans to migrate scripting from Scala to JavaScript for broader adoption
• Agile, on-demand testing aligned with each client’s deployment timeline

Gatling became the performance team’s go-to tool from day one. Here's how they use it:

• Built custom user journey tests in Scala (JavaScript migration underway)
• Simulated user loads with fully managed Gatling managed locations
• Shared dashboards and reports across engineers with admin-controlled access
• Iterated quickly to validate SLAs for each client before release

In one case, Gatling helped AidenAI run tests for a client in Singapore by provisioning a region-specific load generator with a dedicated IP, allowing them to bypass strict firewall rules in the client’s lower environment.

• From JMeter to Gatling Enterprise: Transitioned from JMeter-based tools to Gatling Enterprise, which provided superior scalability and seamlessly integrated with the team’s API automation workflows built on Scala.
  
  
• Enhanced collaboration & monitoring features: Overcame limitations in Gatling Open-Source by adopting Enterprise’s advanced features, including real-time monitoring and enhanced orchestration, streamlining operations by eliminating manual load generator setups and boosting efficiency.

• Transition from Scala to JavaScript for easier scripting and faster onboarding
• Integrate Gatling into CI/CD workflows (Jenkins in the pipeline)
• Scale usage across more teams and client projects