AI Credit-Based Pricing Shift

Summary

This article analyzes the rapid adoption of credit-based pricing models for AI products and services, led by major vendors like OpenAI, Salesforce, Microsoft, and others.
It details why traditional pricing models are proving insufficient for the unique costs and user behavior patterns associated with AI, and highlights the benefits, trade-offs, and best practices for implementing credit-based pricing.
The article also addresses common challenges with credit models, including unpredictability, user confusion, and management friction, and suggests practical steps to mitigate these issues.

Major software vendors—including OpenAI, Salesforce, Microsoft, Adobe, Apollo, Asana, Atlassian, HubSpot, Google, and others—are moving to credit-based pricing for their AI offerings.
Credits (also called tokens, AI units, or flex credits) are becoming the standard, with companies moving away from seat-based or all-you-can-eat subscription models due to unpredictable and high AI infrastructure costs.
The increasing complexity and intensity of AI usage (with a small percentage of heavy users driving most costs) is driving the need for usage-based pricing models to maintain sustainable margins.
Large vendors are easing customer adoption by educating the market and demonstrating that credit models are viable, despite initial buyer resistance due to concerns over predictability and management.

Credit models can be defined in various ways:
- Cost-Based Credits: Pass-through of underlying AI or data provider costs (e.g., Cursor, Clay).
- Output-Based Credits: Tied to successful completion of business tasks or delivered outcomes (e.g., Salesforce's Agentforce Flex, HubSpot's Breeze).
Output-based credits are preferred for better customer clarity and perceived value, though they can be harder to implement.
The value of a credit can vary widely across providers, making market comparison challenging for customers.

Monetize on Multiple Axes: Combine credit usage with other pricing mechanisms (e.g., subscriptions, feature tiers) to maintain margins and differentiation.
Include Baseline Credits: All plans should feature a base credit allowance, allowing users to form habits and reducing early churn due to overages.
Tie Credits to Outputs: Wherever possible, link credit usage to successful or recurring outputs rather than raw consumption or access.
Favor Annual over Monthly Limits: Annual credit pools and credit rollovers offer greater flexibility, foster user retention, and reduce churn risk.
Provide Transparent Usage Tracking: Enable admins to easily track, manage, and predict credit usage, tying it to business outcomes for increased customer satisfaction and willingness to spend.

Credit-based pricing is not a new concept and is commonly seen in other industries (gaming, fitness, telecom).
Potential friction includes the challenge of managing credits across multiple vendors, uncertainty about per-task pricing, and risks of unused or overdrawn credits.
Some startups are exploring alternative approaches, such as separating app pricing from raw AI infrastructure ("bring your own key" models).
The future of AI pricing is expected to further shift towards outcome-based models, with credits serving as an interim solution.

Credit-based pricing is validated as a mainstream approach for AI products — Led by major vendors, credit models help manage unpredictable costs, align charges with usage, and create opportunities for more value-based pricing, despite buyer education and management challenges.

Will vendors provide sufficient clarity and predictability regarding the exact credit cost for specific tasks?
How will credit usage be managed on a per-user vs. company-wide basis, especially with varying user proficiency and needs?
Are there emerging best practices for reducing user friction and confusion as the industry standardizes around credit-based models?