LLM Load Testing Can Cost Thousands Due to Unpredictable Token Billing

Engineering teams scaling AI applications face steep, unexpected costs when load testing LLM integrations, because every API request is billed by token count rather than flat compute time. Unlike traditional load testing tools such as k6 or Locust, which are nearly free to run, a single failed LLM load test can run up bills of thousands of dollars before revealing a configuration flaw. Token usage is also highly unpredictable: a prompt returning 200 tokens in development may generate 800 tokens under different production conditions, making cost estimates unreliable at scale. Tools like Anthropic's web search feature compound the problem further, with one team finding their real per-lead cost was nearly double their estimate due to tens of thousands of injected tokens per call. Common workarounds include custom mocking layers and rate-limit simulation, but none fully replicate real model latency or true production cost profiles.