TL;DR: Credit burndown pricing lets customers prepay for a pool of credits that burn as they use your product. It combines multiple usage types into one metric (e.g. model calls, file processing, exports) so buyers can track a single balance and you can iterate on pricing cleanly.
Credit burndown is an usage based pricing model. Customers maintain a prepaid credit balance that decreases with each billable action (e.g. model calls, file processing, exports). When the balance gets low, they buy a bundle of additional credits or move to a plan with more credits. Bundles are also a natural place to add volume discounts or promotional prices.
For buyers, this means one balance funds everything they do. Model runs, file processing, call transcription, enrichment, exports, and reports all share a common pool of usage. An AI enrichment might cost 1 credit, a call transcription 5 credits, and a long video transcode 10 credits.
Over the last couple of years it’s grown in popularity, especially with AI and other compute heavy products, because it pulls many different usage types into one balance. This gives buyers budget control and product teams more levers for pricing iteration.
Key properties:
Prepaid balance: credits are purchased before use
Usage based: product actions map to credit costs
One meter across features: a single balance covers many usage types
Pricing iteration: you can easily change how many credits an action costs
Realtime enforcement: enforcement happen during the request, not at the end of the month
Credit burndown shows up across many products. In each, the pattern is consistent: reduce diverse actions to a single pricing metric and draw from one balance.
In an AI platform, a week might include chat completions, embedding lookups, and a few file processing jobs. You might price a small model call at ~1 credit, a mid tier at 5 credits, and a premium model at 10 credits. All of it draws down the same pool, so finance tracks a single balance while engineering can switch models or tune prompts without renegotiating price metrics.
For email verification, marketers upload a list and burn credits as they validate emails. One credit could cover 100 checks, while a pre-send spam test on the campaign may cost 3 credits. Regardless of provider changes or retry rates, the buyer watches one balance.
For document generation, each render burns a baseline credit, with larger or more complex documents consuming a little more. Teams quickly learn “a standard invoice costs ~1 credit; the 80‑page board packet costs a few.”
For video processing, you might debit 10 credits per minute of HD output. A six minute clip is 60 credits, while captions or 4K video increase the per minute rate. Creators can estimate costs up front and avoid surprise invoices.
For data enrichment, operations teams pull company profiles at 2 credits each and people records at 1. Even as sources vary, reporting stays simple because everything maps to the same meter.
Credits make it easy to start small with trials and modest credit bundles, then grow into larger, committed packs as usage scales. Users see one balance the whole way, keeping onboarding simple and spend predictable, even when AI workloads spike.
Your team benefits from clear usage limits, so you can adjust burn rates and credit prices without breaking trust. That combination helps startups find their price and gives established products a clean bridge from PLG to enterprise.
If you're interested in learning more, checkout our rundown of the key benefits and drawbacks of credit burndown pricing.
