Lesson 32 of 1550
Pricing an AI Feature: Per-Seat vs. Per-Use vs. Credits
Choose a pricing model that survives when your COGS is a variable OpenAI or Anthropic bill.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1Pick a model that matches usage shape
- 2pricing strategy
- 3COGS
- 4usage-based billing
Concept cluster
Terms to connect while reading
AI features cost real money per request. If you price like classic SaaS ($20/seat/month unlimited) and one power user runs a 10M-token job, you just lost money on them.
Section 1
Pick a model that matches usage shape
- Per-seat: great when usage is predictable and bounded (daily summaries)
- Per-use / metered: best when usage is spiky or power-law (video generation)
- Credits: hybrid — users feel in control, you protect margin with expiry
- Freemium with hard rate limits: use only if free users drive real virality
- Always model gross margin at 60%+ AFTER inference cost, not before
Watch your 90th-percentile user. If their inference cost exceeds your plan price, either raise the ceiling, switch to credits, or cap the feature.
Code example
# Simple margin math
Plan price: $29/user/month
Avg inference cost: $4/user/month
P90 inference cost: $22/user/month <-- danger zone
Stripe + infra: $3/user/month
Gross margin (avg): ($29 - $4 - $3) / $29 = 76% OK
Gross margin (P90): ($29 - $22 - $3) / $29 = 14% NOT OK
Fix: add a 500-credit cap, overage at $0.05/credit.Key terms in this lesson
You're pricing right when 80%+ of customers feel the plan is fair, your P90 user still pays more than they cost, and you don't panic when Anthropic has a latency spike.
End-of-lesson quiz
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