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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

German

Zwischen 2023 und 2026 wird der Mechanismus von der Robo-Kategorie ausgenommen, die KI verändert, wie die Software berechnet wird. Traditionelle SaaS wird pro Sitz berechnet, weil ein weiterer Benutzer fast nichts kostet. Modell-Inferenz hat nicht-null-Margin-Kosten, und die Verwendung variiert durch Anordnungen der Größe über Kunden, produziert Prepaid-Credits, gemessene Verwendung, hybride Sitz-plus-Nutzungsmodelle und Ergebnispreise, die Ergänzung, und in seltenen Fällen ersetzen, Sitz-basierte Preisgestaltung. Diese Preisgestaltung Modelle wurden nicht systematisch gekennzeichnet. Dieses Papier präsentiert eine reproduzierbare Studie von öffentlichen Preisgegenständen - Preisgegenstände und Dokumentation - über drei Koh

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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