TL;DR: Credit burndown pricing lets customers prepay for a pool of credits that burn as they use your product. It turns complex pricing mechanics into a clear per-action credit price and can simplify multiple features into a single usage number users can track.
Credit burndown is a usage-based pricing model that prices the outcome the user wants, not the technical inputs. Users see a single credit cost per action aligned to outcomes they care about, while technical details like API calls, compute time, or AI models remain internal. 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:
Hides implementation details: users stay focused on the product features
Usage based: product actions map to credit costs
One meter across features: a single balance covers many usage types
Prepaid balance: credits are purchased before use
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. Each example shows how this simplifies the buyer’s decision while giving your team room to experiment without changing what you sell.
In an AI product, users might summarize a document, draft a blog post, or synthesize a multi-file report; these might be priced at 1, 5, and 10 credits respectively. Users see clear per-action costs aligned to the goals they want to accomplish, while you manage the models and prompts under the hood.
For an email verification product, marketers might upload email lists to validate emails and prepare campaigns. One credit might cover 100 email validations, 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 a video podcast editing product, adding captions might cost 5 credits per minute, whereas automatically improving audio could cost 15 credits per minute. Creators can estimate costs up front and avoid surprise invoices.
For a data enrichment product, users might spend 1 credits to pull an updated company profile, and 5 credits to find phone numbers associated with an email address. Users stay focused on the information they are looking up, without needing to think about details like which dataset the is being used.
Credits reduce complexity for users and increase flexibility for your team. They collapse technical mechanics into a simple, value-facing price per action and unify many features under one prepaid balance. Users make clearer decisions and keep spend predictable.
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 customers keep asking “what will this cost me?” or your team avoids pricing changes because your current model is too brittle, consider credit burndown pricing.
If you're interested in learning more, checkout our rundown of the key benefits and drawbacks of credit burndown pricing.
