Not sure what credit burndown is? Checkout our Credit Burndown Introduction first.
While credit burndown is a usage-based model, it differs in a few key ways from another common usage based model, pay-as-you-go (PAYG). PAYG bills after the period on raw units like tokens, API calls, minutes, or GB (think AWS). It’s transparent when one metric dominates, but invoices can swing with spikes and meters tend to multiply as you add features.
Below, we outline some of the key differences and tradeoffs between credit burndown and PAYG.
How it feels to users
PAYG: Simplest when a single usage-based feature defines value (e.g., events ingested or minutes recorded). Cost is transparent, but without adequate cost controls usage spikes can lead to surprise bills and frustration. As you add more billable actions, meters proliferate and the model gets harder to explain.
Credits: Best when you have multiple billable actions or model tiers; users watch one balance across features. Because it’s prepaid, teams can cap spend upfront and start with a small bundle, then grow into larger usage, leveraging bundles for discounts.
How it feels to your team
PAYG: Quicker to implement when only a single feature requires tracking and aggregating for billing. Expanding to multiple meters across features can fragment packaging, and changing units or rates for existing plans risks confusion and migrations.
Credits: One meter for multiple actions. You can rebalance burn per action as costs or behavior change without reworking every plan. Bundles make volume discounts and promotions straightforward. Requires real-time balance checks, entitlement enforcement, idempotent eventing, and a credit ledger for Revenue Recongition accounting.
The table below provides a side by side comparison of the key attributes of credit burndown and PAYG.
Pay-As-You-Go | Credit Burndown | |
|---|---|---|
Time of billing | Billed at end of period | Billed at the start of the period; extra usage pre-purchased |
Pricing complexity | A single metric clearly captures value | Multiple billable actions can be reduced into one workflow |
Pricing clarity | Users are likely to understand the underlying implementation and cost | The underlying service cost is unclear, uncertain, or likely to change |
Pricing agility | Changing units/rates risks confusion and migrations | Adjust burn per action without plan churn |
Growth path | Commit minimums or volume discounts per unit/meter | Scale to committed credit packs without changing the model |
Product Expansion | Each new feature adds additional meters and complexity | New features draw from existing balance |
Budget Control | Variable: depends on alerts/quotas; invoices can swing with spikes | Strong: cap spend up front; low-balance alerts and caps |
Customer Support Impact | Large, surprise bills can cause customer frustration; accounting for usage is clear | Prepaid usage allows for better cost control; understanding usage can be difficult with shared balance |
Implementation | Each features requires a meter; enforcement differs per feature | Credit ledger in addition to feature meters; enforcement at the credit balance |
For AI and quickly growing products, credits are a common choice to simplify pricing. But for both the user and builder perspectives, credits change what’s simple and what’s hard. Below is a list of key considerations for using credit burndown pricing.
Benefits
One meter, many actions: a single balance covers multiple features, simplifying packaging and reporting.
Prepaid budget control: users cap spend up front; low-balance alerts and caps limit surprises.
Pricing agility: tune pricing by adjusting cost to purchase credits or the credit cost of each action.
PLG → enterprise path: start with trials or small bundles, graduate to large, committed packs without changing the model.
Backend flexibility: switch models, optimize prompts/caching, or change vendors without exposing raw units to users.
Clear receipts: full credit ledger provides receipts to build trust and reduce billing disputes.
Drawbacks
Real-time systems required: low latency balance checks, entitlement enforcement, idempotent eventing, and a reliable ledger.
UX education: users must learn "what costs what"; poor mappings create confusion.
Finance policy work: expirations, rollovers, refunds, and revenue recognition (deferred revenue).
Support risk if opaque: opaque action costs erode customer trust and can drive support tickets.
Not ideal for a single metric: when one raw input cleanly captures value, PAYG is often simpler.
If you answer yes to at least 3 of these questions, you should strongly consider using credit burndown pricing:
Do you have 3+ distinct billable actions (e.g., model runs, file ops, exports)?
Do you rely on underlying services that also have variable or shifting prices (e.g. many AI products)?
Do users mix actions in a single workflow?
Is providing customer upfront cost control an important consideration?
Are you concerned customers might try to avoid paying (large) bills?
Do you plan to iterate costs often (monthly/quarterly)?
Unlike the traditional usage-based model of pay-as-you-go pricing, credit burndown differs by combining all metered features into a single, prepaid balance. Choose credit burndown when value spans several actions. One balance keeps spend predictable, enables bundles and volume discounts, and lets you adjust burn rules without plan churn.
