AI Load Testing

Test your AI products
the way they actually work

Traditional load testing tools measure HTTP latency. TokenBench measures what matters for LLM-powered products — token throughput, context utilization, and concurrent agent response times.

847ms
avg token latency
12.4K
tokens/min throughput
98.2%
context utilization

HTTP benchmarks don't capture AI performance

When your product runs on GPT-4, Claude, or any LLM, the bottleneck isn't your API endpoint — it's token generation speed, context window management, and rate limit handling. Existing tools weren't built for this. TokenBench was.

Token Latency
Track time-to-first-token and per-token generation speed under load
Throughput
Measure tokens per minute across concurrent AI agent sessions
Context Utilization
See how efficiently your prompts use available context windows
Rate Limit Handling
Simulate the failure modes your users will actually hit

Built for AI teams

Concurrent Agent Simulation
Run hundreds of AI agents in parallel, each with independent conversation context and model routing
LLM-Native Metrics
Time-to-first-token, tokens-per-second, completion latency — metrics that actually reflect user experience
Realistic Workload Profiles
Replay production traffic patterns with varied prompt lengths, context sizes, and reasoning requirements
Failure Mode Detection
Surface rate limit errors, context overflows, and model degradation before users encounter them

Know how your AI product performs before your users do.