AI Monetisation
When the marginal cost isn't zero.
This is where the generic pricing pages stop being useful. The core AI pricing problem is not that customers won't pay — it's that your top 10% of users can consume 60% of the inference cost, model prices reprice quarterly, and outcomes are probabilistic enough that guarantees are dangerous.
We work through the credit-vs-outcome-vs-consumption choice for your specific feature — with the unit-economics model, not a general framework — and design guardrails so gross margin doesn't quietly collapse two quarters after launch.
The specifically-AI questions
- Which model tier belongs to which price tier — and how you handle a Sonnet-to-Opus routing decision commercially
- Credits vs. included quota vs. true consumption billing — with the buyer psychology and dispute exposure of each
- Outcome pricing (paid per resolved ticket, per lead qualified) — when it works and when it's a margin bomb
- Guardrails: throttles, per-account caps, and margin-alert thresholds so a single customer can't reprice the plan
- How to reprice when your provider reprices — grandfathering, notice, and contractual position
Related
This engagement usually runs alongside Value Metrics (to pick the billable unit) and Pricing Architecture (if the underlying model needs to shift from seat to hybrid). It is complementary, not a substitute.
Next step
Before committing capital, changing pricing, or restructuring product teams — let's talk.
We will be direct about whether an engagement is the right answer, and what scope would make it useful.