In the context of AI-powered tools, the consumption of tokens (the units by which large language models process and generate text) as a measure of how much compute a given interaction or workflow requires. Token usage has a direct cost implication: the more complex the prompt, the longer the context, and the more requests made, the higher the token consumption and associated cost.
For teams integrating AI into testing workflows at scale, token usage becomes a practical constraint that affects how often the AI can be called, how much context can be included, and ultimately the economics of using AI tooling long-term. Rate limits on tokens per minute can also affect throughput when many tests are run in parallel.
For teams integrating AI into testing workflows at scale, token usage becomes a practical constraint that affects how often the AI can be called, how much context can be included, and ultimately the economics of using AI tooling long-term. Rate limits on tokens per minute can also affect throughput when many tests are run in parallel.
