Gatling
Continuous performance intelligence, powered by AI
Founded
2011
Starting Price
Free
Category
CI/CD & DevOps
Last updated May 25, 2026
About Gatling
Gatling is an open-source load testing and performance engineering platform built for modern development teams. It enables engineers to simulate millions of virtual users, detect bottlenecks, and automate performance testing across CI/CD pipelines using code-based, no-code, or AI-assisted test creation.
Pros & Cons
Pros
- Powerful open-source core with massive community adoption (30M+ downloads)
- Code-as-tests approach gives version control, peer review, and CI native workflows
- Scales to millions of virtual users with distributed load generators
- Strong CI/CD automation via CLI, APIs, and SLO gating
- Beautiful HTML reports and rich enterprise analytics dashboards
Key Features
Code-Based & No-Code Test Creation
Write load tests in Java, Scala, or Kotlin, use a no-code recorder, or import Postman collections.
AI-Powered Assistance
Automated script conversion from JMeter and LoadRunner plus intelligent analysis of test results.
Massive Scale Load Generation
Generate millions of virtual users from globally distributed or private load generators.
Insights & Analytics Dashboards
Customizable dashboards with trend tracking and run-to-run performance comparison.
CI/CD Pipeline Integration
Trigger and automate performance tests via CLI and APIs inside any continuous delivery pipeline.
Service Level Objectives (SLOs)
Define performance SLOs and track compliance automatically on every test run.
Team Collaboration
Shared reporting, version control, and role-based seats for coordinated performance engineering.
Pricing
Community
Free
- Open-source load testing engine
- Local execution only
- Java, Scala, and Kotlin DSLs
- HTML reports
- Community support
Best For
Pre-launch load testing for web applications
Simulate realistic traffic spikes against APIs and web apps to validate capacity before launches or marketing events.
Continuous performance testing in CI/CD
Run automated performance regressions on every pull request and gate releases with Service Level Objectives.
Migration from legacy load testing tools
Convert existing JMeter or LoadRunner scripts with AI assistance to modernize the performance testing stack.
Distributed testing at global scale
Generate millions of virtual users from multiple geographic regions to validate global infrastructure.
