LLM moderation cost estimation for user images and text

This article discusses a method for estimating LLM token costs for content moderation, particularly for user-submitted images and text. The author advocates for a preflight check at the application layer to predict costs before making a moderation call, emphasizing that token cost is just one factor in a broader evaluation of recall, review rates, and parse failures. The approach involves normalizing content, selecting a model, enforcing limits, and validating responses at the boundary to prevent data loss and ensure accurate metrics. For images, it's recommended to benchmark them separately from text, as their cost and failure modes differ significantly. AI

IMPACT Provides practical guidance for developers on managing LLM operational costs and improving moderation workflows.

RANK_REASON The article describes a practical workflow and tooling for developers using LLMs for moderation, focusing on cost estimation and validation.

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