Monetization โ turning a product into revenue. Without it, a startup won’t survive.
Monetization Models
API Pricing โ pay per request
api_pricing = {
"text_generation": "$0.02 / 1K tokens",
"image_generation": "$0.015 / image",
"data_analysis": "$0.001 / request",
}
Examples: OpenAI, Anthropic, Stability AI.
Subscription โ fixed monthly fee
tiers = {
"free": {"price": 0, "requests": 100},
"basic": {"price": 10, "requests": 1_000},
"pro": {"price": 50, "requests": 10_000},
"enterprise": {"price": 500, "requests": 100_000},
}
Freemium โ free + paid features
freemium = {
"free": {"features": ["Basic AI", "100 requests/month"], "price": 0},
"premium": {"features": ["Advanced AI", "Unlimited", "API"], "price": 29},
}
Key Metrics
MRR (Monthly Recurring Revenue):
users = {"free": 1000, "basic": 150, "pro": 30, "enterprise": 5}
prices = {"free": 0, "basic": 10, "pro": 50, "enterprise": 500}
mrr = sum(users[t] * prices[t] for t in users) # $5,500
arr = mrr * 12 # $66,000
ARPU (Average Revenue Per User):
arpu = mrr / total_users # $5,500 / 1185 โ $4.6 / user
Conversion Rate (free โ paid):
conversion = paid_users / total_users # 185 / 1185 โ 15.6%
LTV / CAC:
ltv = arpu * avg_lifetime_months # $4.6 * 24 = $110
cac = marketing_spend / new_customers # $1000 / 50 = $20
# LTV/CAC > 3 is a healthy ratio
Choosing a Strategy
| Scenario | Model |
|---|---|
| B2B, enterprise clients | Enterprise subscription |
| Developers / API | Pay-per-use |
| Mass consumer | Freemium โ paid |
| High inference cost | Usage-based + minimum |
Common Mistakes
- No free tier โ high entry barrier, users never try the product
- Vague limits โ “basic features” means nothing; use concrete numbers
- Ignoring CAC โ if acquiring a user costs more than their LTV, the model is unsustainable
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