Gatling
High-performance load testing framework written in Scala with Java/Kotlin/Scala DSL for test scenarios. Gatling generates detailed HTML reports with response time distributions, throughput, and error rates. Uses an actor-based async engine for minimal resource overhead at high concurrency. Industry standard for agent API performance validation with CI/CD integration.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
TLS for HTTPS targets supported. Credentials in simulation scripts must use environment variables, not hardcode. JVM dependencies need security patching.
⚡ Reliability
Best When
You need detailed performance reports with concurrent user simulations and CI/CD integration for agent API load testing.
Avoid When
You want simpler scripting with k6's JavaScript DSL, or you need pure Python load tests with Locust.
Use Cases
- • Load test agent API endpoints under sustained high concurrency to validate performance SLAs
- • Simulate realistic agent workload patterns (ramp-up, steady state, spike) against backend services
- • Integrate agent API performance tests into CI/CD pipelines with threshold-based pass/fail criteria
- • Test agent WebSocket endpoints under concurrent load with Gatling's WebSocket protocol support
- • Generate detailed performance reports for agent service capacity planning and bottleneck analysis
Not For
- • Simple API testing without load — use Postman, Bruno, or pytest for functional testing
- • Teams without JVM experience — Gatling requires Java/Scala knowledge for scenario DSL
- • Real browser user simulation — use Playwright or Selenium for browser-level load testing
Interface
Authentication
Gatling Enterprise (commercial) has auth. OSS Gatling is local tool. Target API auth implemented in simulation scripts.
Pricing
Core Gatling is Apache 2.0 licensed. Gatling Enterprise adds distributed execution, cloud runners, and team features.
Agent Metadata
Known Gotchas
- ⚠ Gatling requires JVM (Java 11+) — adds JVM to CI/CD environments even for non-Java agent stacks
- ⚠ Simulation scripts are compiled Scala/Java — errors are JVM compilation errors; unfamiliar to JavaScript/Python agent developers
- ⚠ Virtual user model is different from thread-per-user — Gatling uses async actors; very high VU counts (10K+) are achievable but require understanding the execution model
- ⚠ Feeder data (CSV, JSON) for realistic test data is read once and shared — large datasets must be preloaded; real-time data generation requires custom feeders
- ⚠ Check (assertion) failures don't stop the simulation by default — all virtual users complete even when checks fail
- ⚠ HTML reports are generated after simulation completes — live monitoring requires Gatling Enterprise or Graphite/InfluxDB integration
Alternatives
Full Evaluation Report
Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for Gatling.
Scores are editorial opinions as of 2026-03-06.