Token count is a primary driver of Large Language Model (LLM) API costs, as providers charge based on the input and output tokens processed. To manage these expenses in applications like fintech candidate scoring, developers should count tokens before sending requests, retrieve only essential evidence relevant to the scoring rubric, and bound the model's output. This approach ensures an auditable path for cost management and quality control, where the system records token usage, model choice, and latency for each inference. AI
IMPACT Provides practical strategies for developers to manage and reduce operational costs when integrating LLMs into applications.
RANK_REASON Article discusses practical implementation details for managing LLM API costs, not a new model release or significant industry event.
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