Ai pricing models evolve from seats to credits to outcomes
From Tokens to Credits to Outcomes: Pricing AI Across Models, Applications, and Cloud Platforms
Software Engineering
Summary
Software used to be priced mostly by number of users, but with AI, each use costs money and varies a lot. The authors studied how AI companies and cloud platforms price their services using data collected from public sources. They created a detailed list of pricing types like subscriptions with usage quotas, prepaid credits, and outcome-based fees. Their work helps understand the new pricing patterns and what billing systems need to handle these. This study offers a public dataset and a classification of pricing methods used in AI today.
What this means in practice
- •For cloud service providers: Design billing systems that support high-frequency pricing changes and complex credit and usage accounting based on this taxonomy.
- •For software product managers: Use the dataset and taxonomy to create flexible AI service pricing strategies that combine seats, usage, and outcomes effectively.
Authors
Pratik Gupta
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.