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From Tokens to Credits to Outcomes: Pricing AI Across Models, Applications, and Cloud Platforms

Pratik Gupta · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30701 · cited by 0 works (OpenAlex) · open-access PDF

Korean

2023 년과 2026 년 사이에 샘플 메커니즘을 제외한 AI는 소프트웨어의 가격 방식을 변경했습니다. 전통적인 SaaS는 1 개의 더 많은 사용자를 제공하기 때문에 거의 아무것도 비용을 지불하지 않습니다. 모델 추론에는 제로 마진 비용이 있으며 사용은 고객 간의 규모의 명령에 따라 달라지며 사전 지불 된 크레딧, 측정 된 사용, 하이브리드 좌석 플러스 사용 모델 및 결과 요금이 추가되며, 드문 경우에는 좌석 기반 가격을 대체합니다. 이러한 가격 모델은 체계적으로 특징화되지 않았습니다. 이 논문은 공공 가격 예산의 재현 가능한 연구를 제시합니다 - 가격 페이지 및 문서 - 세 개의 코호트를 제외합니다 : 포브스 AI 50 (2026)는 AI

Machine translation (Meta M2M100); the English original follows.

Abstract

Between 2023 and 2026, AI changed how software is priced. Traditional SaaS charged per seat because serving one more user cost almost nothing. Model inference has non-zero marginal cost, and usage varies by orders of magnitude across customers, producing prepaid credits, metered usage, hybrid seat-plus-usage models, and outcome pricing that supplement, and in rare cases replace, seat-based pricing. These pricing models have not been systematically characterized. This paper presents a reproducible study of public pricing artifacts - pricing pages and documentation - across three cohorts: the full Forbes AI 50 (2026) as the AI-native core sample, cloud platforms selling AI services included by a documented rule, and a frozen purposive cohort of 9 distinctive designs. It contributes (1) a dataset coding each offering's pricing primitive and credit mechanics with dated primary sources, prepared for public release with the paper, (2) a taxonomy of the primitives in use and their distribution across cohorts, and (3) empirically grounded requirements for automated, configurable billing platforms, including ledger state for credits, attribution pipelines for outcome pricing, and configuration support for high pricing-change velocity. Subscription with usage quota is the dominant observable model among AI-native companies (55% of observable AI-native rows; 45-61% under sensitivity analysis of hybrid boundary cases). 23 of the 52 AI-native rows publish no pricing. Hybrid products are better understood as compositions of packaging anchor, allowance mechanism, and billable unit than as mutually exclusive categories. All artifacts are public; my employer is excluded from the sample.

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Topics: Cloud computing, Computer science, Documentation, Software as a service, Outsourcing, Dynamic pricing

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