To ease customer anxiety about runaway costs from its new AI agents, Notion is implementing usage-based pricing but delaying actual billing for several months. This grace period allows users to see their metered usage, understand the value, and adjust, mitigating the fear of unpredictable bills before they have to pay.

Confusing credit-based AI pricing models will likely be replaced by a straightforward value proposition: selling AI agents at a fixed price equivalent to the cost of one human worker who can perform the work of ten. This simplifies budgeting and clearly communicates ROI to CFOs.

Usage-based pricing for AI faces strong customer resistance. Unlike cloud storage where usage is directly controlled, AI credit consumption can be driven by new vendor-pushed features. This lack of control and predictability leads to bill shock, making customers prefer the stability of per-seat models.

Switching a usage-based AI product to an unlimited SaaS model eliminates budget as a barrier, driving deep adoption. The new bottleneck becomes the client's time to process the AI's output, creating an opportunity to build features that automate this "last mile" of work.

Beyond upfront pricing, sophisticated enterprise customers now demand cost certainty for consumption-based AI. They require vendors to provide transparent cost structures and protections for when usage inevitably scales, asking, 'What does the world look like when the flywheel actually spins?'

AI SaaS companies have variable, usage-based costs, but customers demand predictable flat fees for procurement. Product Fruits found charging per usage failed. The solution is to accept the uncertainty, create flat-fee plans, and absorb the risk of variable backend costs to close deals.

Enterprise buyers are hesitant to adopt new AI tools due to unclear, consumption-based pricing from vendors like ServiceNow. Lacking transparency on how 'meters' work or what future usage will cost, customers fear 'locked-in cost increases' and a new form of vendor lock-in, which is slowing down sales cycles.

To combat the unpredictable costs of token-based AI usage, Pega is adopting a value-based pricing model. Instead of charging per token, they charge based on work completed (e.g., per loan funded or service request processed), aligning costs directly with business outcomes and enabling forecasting.

Customers are intimidated by token-based pricing. Offering a flat-fee "unlimited agents and usage" package removes this friction. In reality, clients rarely need more than a few well-configured agents, making the model profitable and simple to sell by focusing on value instead of usage.