Load test your LLM APIs before your costs spiral
Test non-deterministic outputs, validate token efficiency, and catch infrastructure bottlenecks
that only show up under real AI workloads.
Why LLM APIs break traditional load testing
Large language models introduce performance challenges
that standard load testing completely misses. With more users
and challenges each day, outages can kill traction.
Non-deterministic chaos
Same prompt, different outputs every time. Your response times vary wildly based on what the model decides to generate, making traditional benchmarks useless.
Token costs add up fast
Every character costs money. Without proper load testing, you might discover your prompt design burns through $10K/month instead of $1K when you hit scale.
Infrastructure strain
LLMs crush your servers in ways normal APIs don't. CPU throttling, memory leaks, and TLS handshake timeouts spike when handling thousands of AI requests.
Context window overflows
Long conversations hit token limits unexpectedly. RAG applications with heavy context can randomly fail when users ask one too many questions.
FEATURE TOOLKIT
Load testing designed
for AI workloads
Gatling understands prompt variability, token economics, and the infrastructure patterns that make LLM APIs different from everything else you've tested.
Realistic user journeys
Script complex API workflows that mirror real user behavior. Test authentication flows, data processing pipelines, and multi-service transactions.
Scale to millions of requests
Push your API infrastructure to its limits. Find out exactly how many concurrent requests you can handle before things start breaking.
End-to-end visibility
Monitor request latency, error rates, and resource utilization across your entire microservices stack with distributed tracing integration.
Catch regressions automatically
Set SLA thresholds for response times and error rates. Stop deployments automatically if API performance degrades.
Correlate test data with observability tools
Track response times, error rates, and system behavior during tests.Send results to Datadog or Dynatrace to view performance in context.
Multi-protocol support for distributed systems
Go beyond REST: test WebSocket, gRPC, JMS, MQTT, and more in the same scenario.Ideal for service meshes, event-driven systems, and polyglot APIs.
USE CASES
Every AI application has
its performance breaking point
Gatling understands prompt variability, token economics,
and the infrastructure patterns that make LLM APIs different from everything else you've tested.
Simulate different prompt lengths, complexity levels, and creativity settings. See how temperature and top-p parameters affect your response times under load.
Surface hidden tail latency that averages miss. See exactly when your slowest users start having bad experiences with your AI features.
Test if your infrastructure scales appropriately with AI compute demands. Prevent overprovisioning that wastes money or underprovisioning that kills performance.
Track token usage and calculate real costs during load tests. Compare prompt strategies and find expensive patterns before they hit your bill.
Test realistic chat flows with context that builds over time. Validate how your system handles long conversations and session management.
Real business results,
powered by Gatling
PLATFORM
Our platform in action
From test design to insight sharing, Gatling gives you full control of your load testing strategy
Ready to load test
your LLM APIs
before costs get
out of hand?
Validate performance, optimize token usage,
and catch bottlenecks before your users
and budget feel the pain
Need technical references and tutorials?
Need the community edition for local tests?
