OpenAI and Anthropic Drive AI Customers to Pay-per-Use Model
OpenAI and other AI companies like Anthropic are guiding customers to adopt a pay-per-use model to cope with the rapidly rising costs of training and inference.
This shift aims to better align actual consumption with expenditure, replacing fixed subscription or prepaid models, and helping companies cover the surge in computing costs.
AI infrastructure costs have skyrocketed, and sellers (model providers) are passing on the pressure through pricing adjustments, while enterprise customers are under pressure to shift towards more flexible solutions, directing funds towards more efficient or self-built infrastructure, accelerating the restructuring of industry pricing mechanisms.
Source: Public Information
ABAB AI Insight
OpenAI has been continuously optimizing its pricing since the commercialization of the GPT series, with Anthropic and others following suit with similar strategies. This push for pay-per-use reflects the GPU shortage and rising energy costs, marking a shift for model providers from scale expansion to sustainable profitability.
In terms of capital pathways, AI companies are locking in high-frequency user revenue through dynamic pricing, with resources continuing to be invested in computing power expansion and model optimization. The motivation is to maintain gross margins while lowering the entry barrier for customers, thus expanding market coverage.
Similar to the evolution of cloud services from reserved instances to on-demand instances, the current AI consumption model is transitioning from fixed subscriptions to flexible payments, enhancing providers' adaptability to demand fluctuations.
Essentially, this is a concentration of capital, driven by cost pressures that force pricing innovation. Pricing power is shifting towards platforms that master efficient inference and scale effects, promoting a structural migration of AI adoption from experimentation to production-level applications.
ABAB News · Cognitive Law
As costs rise, pricing follows; the more you use, the more you pay, marking AI's entry into a true commercial era. Fixed subscriptions carry risks, while pay-per-use shows efficiency; matching is key. As computing power becomes more expensive, pricing becomes more precise, accelerating stratification in a winner-takes-all scenario.