Cost Transparency of Enterprise AI Adoption

2025-11-13

AI Summary

Enterprises adopting commercial large language model (LLM) services face opaque cost structures: although input token count is controllable, output token volume is systematically influenced by user prompt linguistic style—particularly politeness—leading to unpredictable expenditure fluctuations that undermine budgeting and strategic decision-making. This study conducts controlled experiments via the OpenAI API, integrating NLP-based feature extraction with econometric analysis to uncover, for the first time, a dynamic, indirect mechanism whereby user language structure drives enterprise costs through the generative process. Results show that impolite prompts significantly increase output token count (mean +18.7%), raising costs and amplifying provider revenue without materially degrading response quality. These findings expose a structural flaw in prevailing token-based pricing models and provide empirical and theoretical foundations for designing more predictable, equitable LLM pricing mechanisms.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Enterprise AI costs are unpredictable due to variable output tokens
• Linguistic style changes impact token counts without affecting quality
• Current pricing models lack transparency complicating business budgeting

Innovation

• Linguistic style shifts alter output token counts
• Non-polite prompts increase costs without quality loss
• New pricing models needed for transparent AI adoption