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From pm-copilot
Use this skill when the user asks specifically about "how to monetize AI features", "should AI be a separate tier", "pricing for AI capabilities", "how to charge for AI", "AI add-on vs. bundle", "AI feature pricing strategy", or is adding AI capabilities to an existing product and wants to decide how to monetize them. This is a specialized version of pricing-review focused on AI feature economics.
npx claudepluginhub productfculty-aipm/pm-copilot-by-product-facultyHow this skill is triggered — by the user, by Claude, or both
Slash command
/pm-copilot:ai-feature-monetizationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are helping the user decide how to monetize AI features specifically — a distinct challenge from general pricing because AI features have meaningful variable costs (API inference) and often deliver value in a way that's hard to measure per-use.
Use this skill when the user asks to "review our pricing", "help me think through pricing", "should we change our price", "pricing strategy", "how should we price this", "pricing tiers", "is our pricing right", "freemium vs. paid", or wants to evaluate or design their product's pricing model.
Calculates AI feature costs, challenges necessity, models economics at scale, and provides verdicts with optimizations using ai-cost-analyzer agent.
Analyzes and designs pricing strategies including models, competitor pricing, willingness-to-pay estimation, and price elasticity. Useful for setting prices, evaluating models, price adjustments, or freemium vs paid comparisons.
Share bugs, ideas, or general feedback.
You are helping the user decide how to monetize AI features specifically — a distinct challenge from general pricing because AI features have meaningful variable costs (API inference) and often deliver value in a way that's hard to measure per-use.
Framework: Palle Broe (How should you monetize your AI features, Lenny's Newsletter 2024 — analysis of 44 tech incumbents), Lenny Rachitsky (AI monetization patterns).
Key data from Palle Broe: Of 44 leading tech incumbents:
Read memory/user-profile.md for product stage, business model, and existing pricing. Read context/company/competitors.md for competitive pricing context.
Model 1 — Bundle (59% of incumbents): AI features are included in existing plans. No separate pricing.
When to use:
Risk: If AI inference costs are high and usage is uncapped, you lose money on high-usage customers. Mitigation: Soft limits or fair use policies on AI feature usage.
Model 2 — Add-on (23% of incumbents): AI features are in a separate tier or add-on price point.
When to use:
Risk: Adds friction to adoption; users have to actively decide to pay for AI. Mitigation: Free trial of AI features; show clear ROI before paywall.
Model 3 — Standalone (18% of incumbents): AI is a separate product with its own pricing.
When to use:
Risk: Splits focus; dilutes brand; harder to sell. When it works: When the AI product has clear standalone value (like PM Copilot — it's a plugin that adds genuine PM capability, not just an AI wrapper on an existing feature).
For AI-powered features, calculate the cost structure:
Cost per session / per use:
At scale:
Threshold analysis:
Use this decision tree:
Is the AI feature a product improvement (enhances existing use case) or new value (entirely new use case)?
Is the marginal cost per user per month < 10% of their subscription revenue?
Is there a clear segment willing to pay more specifically for AI?
Is the AI product compelling enough to stand alone without the core product?
Produce: