Summary
A company running AI coding agents for trading infrastructure is spending over $100K weekly on LLM tokens, primarily due to context window bloat from long tool traces and repeated system prompts. Despite efforts like prompt caching, using smaller models for routine tasks, and aggressive conversation truncation, they are still seeking more effective cost-cutting strategies. This scenario underscores the significant operational cost challenges associated with deploying large language models at scale.
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