AI Pricing Tool

Related questions

How should you price an AI product or feature?

The core tension is that AI costs are usually usage-based (compute, tokens, inference) while buyers often prefer predictable seat-based pricing. The model has to cover variable cost without punishing adoption — which is why usage-based, seat-based, and hybrid models each fit different products. Start from your cost structure and your buyer's willingness to pay, not a competitor's price.

Usage-based vs seat-based pricing for AI — which is better?

Seat-based is predictable and easy to buy but can decouple price from the cost you incur — a power user and a light user pay the same while consuming very differently. Usage-based aligns price with value and cost but creates bill anxiety and harder forecasting. Many AI products land on a hybrid — a platform or seat fee plus usage — to get the best of both.

What is hybrid pricing?

Hybrid pricing combines a fixed component (a platform fee, seats, or a base tier) with a variable, usage-based component (per request, per token, per outcome). It gives you predictable baseline revenue and covers the variable cost of heavy users — a common shape for AI-native products where consumption varies widely.

How do you protect margin when your AI costs are usage-based?

Tie at least part of the price to the cost driver — usage tiers, included allowances with overages, or outcome-based pricing — rather than a flat fee a few heavy users can make unprofitable. Model the distribution of usage across your base before you set the number; the average hides the users who'll define your costs.