AI credits are a prepaid unit of value a customer buys and spends on a product's AI features. Each action consumes credits, which abstract the underlying token or compute cost into one simple unit.
AI credits are how many AI products let customers pay for usage without exposing raw token or compute costs. The customer buys a balance of credits and spends them as they use AI features, an image generation costs credits, a long document costs more. Credits turn variable, opaque backend costs into one unit a customer can understand and budget.
How AI credits works
The product defines a credit and how much each action costs in credits, then sells credits in packs or includes them in a plan. As the customer uses AI features, the system deducts credits from their balance. When credits run low, the customer buys more or their plan refreshes.
Behind the scenes, the product maps real costs (model tokens, GPU time, third-party API calls) to a credit price, marking up to preserve margin. The credit is an abstraction layer: it shields the customer from token math while letting the product meter and monetize consumption. This is distinct from charging customers raw AI token pricing, where the unit is the provider’s token.
AI credits examples
An image tool gives 100 credits a month; a standard generation costs 1 credit, an upscale costs 4. A writing app sells credit packs spent per document, with longer outputs costing more. A video product charges credits per second of generated footage. A coding assistant includes monthly credits, then sells top-ups.
In each case the customer sees “credits,” not tokens or GPU-seconds, even though credits map to those costs underneath.
AI credits vs Raw token pricing
| AI credits | Raw token pricing | |
|---|---|---|
| Unit shown to customer | Product-defined credit | Provider tokens |
| Cost transparency | Abstracted, simpler | Exposed, technical |
| Margin control | Built into credit price | Pass-through or markup |
| Best for | Consumer and prosumer AI | Developer/API products |
Benefits & when to use it
AI credits make AI pricing legible and budgetable for non-technical users, who rarely want to reason in tokens. They give the product full control over margin (the credit-to-cost ratio is the product’s to set) and let one credit balance span multiple AI features with different underlying costs. For consumer and prosumer AI products, credits are usually the right abstraction.
The trade-off is transparency: customers cannot directly see the token cost, so the credit-to-value mapping must feel fair. Developer and API products often prefer raw token pricing for exactly that transparency. Many products offer credits to end users while paying providers in tokens.
FAQ
What are AI credits?
A prepaid unit of value a customer buys and spends on a product's AI features. Each action consumes credits, which abstract the underlying token or compute cost into one simple unit the customer can budget around.
How do AI credits work?
The product prices each AI action in credits and sells or includes a credit balance. Using a feature deducts credits; running low prompts a top-up or plan refresh. Underneath, credits map to real costs (tokens, compute) with a margin built into the credit price.
What is the difference between AI credits and tokens?
Tokens are the provider's raw unit of model usage. AI credits are a product-defined unit shown to the customer that abstracts tokens (and other costs) into one simple balance. Credits hide the token math and let the product control margin.
How Credyt handles AI credits
Credyt is built to run AI credits. Each customer's wallet holds credits as a native asset, and Credyt authorizes and debits credits per action in real time, with stacked grants for included, promotional, and purchased credits. Because Credyt also ingests the underlying vendor cost, the product can see real margin behind every credit spent and set the credit-to-cost ratio with confidence. See Credyt for AI products. Explore Credyt →