Your pricing model didn’t exist two years ago. Your billing stack is older than that.
Token metering, hybrid seat-plus-usage, credits, commitments, weekly pricing experiments — AI-native monetization moves faster than any stack built for per-seat SaaS. Genesis executes it against one live commercial state.
You’re inventing your pricing model while running it.
Nobody in AI knows their final pricing model — you’re discovering it in production. Seats today, usage tomorrow, credits and commitments for enterprise, a new experiment every month. Every iteration on a conventional stack means engineering work, migration risk, and an ops team reconciling three generations of pricing logic by hand — while inference costs move underneath you and margin depends on the meter being right.
The companies that win this market will be the ones whose pricing can move as fast as their product. Most stacks guarantee it can’t.
What Genesis executes
How should an AI company bill for usage?
Whatever the unit — tokens, requests, compute-time, credits — the architecture question is the same: metering, entitlement, and invoicing must resolve from one commercial state, or revenue leaks at every system boundary. The unit is a pricing decision; the single state is the requirement.
Can we run seat-based and usage-based pricing together?
Yes — hybrid seat-plus-usage and platform-fee-plus-credits are composed pricing logic on one engine, including commitments with drawdown against them.
How fast can we change AI product pricing?
On Genesis, a pricing change is described in plain language, human-approved, and applied to live rules — days, not sprints. That capability is live today.
Ship the pricing experiment this week. Bill it correctly on the first.
The model you price is the revenue you execute.
Price your first use case