A significant cost issue has emerged for teams using LLM tracing, primarily due to the large storage requirements of prompts and responses. Storing full LLM trace payloads without a retention policy can drastically increase AWS S3 bills. The article proposes three solutions: sampling successful traces while retaining all errors, implementing tiered storage with lifecycle policies for older data, and optimizing the data stored by focusing on critical information. AI
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IMPACT Optimizing LLM tracing storage can significantly reduce operational costs for AI development teams.
RANK_REASON The article discusses a technical issue and provides solutions for optimizing LLM tracing infrastructure, which falls under tooling.
