Cost Transparency of Enterprise AI Adoption

TL;DR

This paper investigates how linguistic style influences output token counts in enterprise LLM use, revealing cost unpredictability and calling for more transparent pricing models to improve adoption and budgeting.

Contribution

It demonstrates that linguistic style affects output token counts, impacting costs and transparency in enterprise LLM adoption, a previously underexplored aspect.

Findings

Linguistic style shifts can alter output tokens without affecting response quality.

Politeness reduces output token counts, lowering costs.

Unpredictable cost variation complicates enterprise budgeting.

Abstract

Recent advances in large language models (LLMs) have dramatically improved performance on a wide range of tasks, driving rapid enterprise adoption. Yet, the cost of adopting these AI services is understudied. Unlike traditional software licensing in which costs are predictable before usage, commercial LLM services charge per token of input text in addition to generated output tokens. Crucially, while firms can control the input, they have limited control over output tokens, which are effectively set by generation dynamics outside of business control. This research shows that subtle shifts in linguistic style can systematically alter the number of output tokens without impacting response quality. Using an experiment with OpenAI's API, this study reveals that non-polite prompts significantly increase output tokens leading to higher enterprise costs and additional revenue for OpenAI. Politeness is merely one instance of a broader phenomenon in which linguistic structure can drive unpredictable cost variation. For enterprises integrating LLM into applications, this unpredictability complicates budgeting and undermines transparency in business-to-business contexts. By demonstrating how end-user behavior links to enterprise costs through output token counts, this work highlights the opacity of current pricing models and calls for new approaches to ensure predictable and transparent adoption of LLM services.