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Building & Product

How should I price AI features: per seat, per use, or per outcome?

AI broke flat per-seat pricing because every request has a real compute cost; most AI-native companies now run hybrid pricing: a base subscription plus usage credits, with outcome-based pricing (like Intercom's $0.99 per resolved ticket) emerging where results are measurable. Anchor your price to the value of the work replaced, not to your token costs, and protect margins with usage caps or credits. Expect to revise pricing often; the leading AI companies changed theirs multiple times a year.

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17 resources.

More in AI in Your Product

Should my startup add AI features, and which ones actually matter to users? 20 → How do founders ship their first AI feature (APIs, wrappers, RAG in plain terms)? 19 → How much does it cost to run AI features, and how do I keep costs sane? 19 → Which AI model should I build on (GPT, Claude, Gemini, open-source), and how do I avoid locking myself in? 17 → How do I test whether my AI feature actually works before launch (evals, in plain terms)? 18 → How do I stop my AI feature from making things up in front of customers? 17 → Prompt engineering, RAG, or fine-tuning: which one does my product actually need? 18 → How do I design AI features people trust (chat vs buttons, streaming, showing the work)? 18 → What is my moat if I'm building on someone else's model? 19 → Should I put an AI agent in my product, or is a simple workflow enough? 18 → Is it safe to send customer data to AI APIs, and what about privacy laws like India's DPDP Act? 18 → My AI feature is live. How do I monitor it and make it better with real usage? 18 →

Also in Starting Up

The same ground, over in Build the product, our Starting Up track.

What actually counts as an MVP, and what's the smallest one I can get away with? 20 → Which no-code tool should I use to build my first product? 20 →
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