Why your AI bill is bigger than it should be
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A $287 debugging session led engineer Tejas Chopra to build Headroom, an open-source context optimization layer for LLMs that saved users an estimated $700,000 and 200 billion tokens in five months. The tool compresses inputs before they reach LLM providers using multiple strategies: JSON stripping, statistical similarity compression, AST-based code compression, and a custom-trained model for unstructured text. Each compressed payload is cached locally (Redis or SQLite) with a configurable TTL, and a breadcrumb tool call lets the model retrieve the full original data if needed. Headroom also mines historical agent sessions to detect repeated failures and writes corrections back into config files. The broader argument is that 'token hygiene' — treating token budgets like compute credits and measuring what a task actually needs rather than what it consumes — is the next engineering discipline, analogous to minifying JavaScript or normalizing databases.
