AI startups don't all make money the same way. Some charge per API call, others sell seats to enterprises, others take a cut of transactions their AI enables. Understanding the core business models helps you evaluate which AI companies are building durable businesses versus burning cash on expensive compute.

The AI boom has produced several distinct business models, each with different economics, defensibility, and growth dynamics. API-as-a-service companies like OpenAI and Anthropic sell raw model access priced per token — high volume, thin margins after compute costs, scales with usage. SaaS AI products like Jasper, Copy.ai, and Notion AI charge monthly seats for AI-enhanced workflows — predictable revenue, but high churn if the underlying model capability commoditizes. Vertical AI companies like Harvey (legal) and Abridge (medical) focus on a specific industry with deep workflow integration — higher switching costs and enterprise contract values. Agent-as-a-service startups charge for outcomes rather than usage — a resolved support ticket, a qualified sales lead, a scheduled meeting. AI infrastructure companies like Pinecone, LangChain, and Modal sell picks and shovels to other AI builders. Each model has different unit economics: token-based pricing has razor-thin margins while outcome-based pricing captures more value but requires higher reliability. The big question for every AI startup: is your moat the model, the data, the workflow integration, or the distribution? If it's just the model, you're likely competing with a foundation model provider who does it cheaper next quarter.

What Are the Business Models of AI Startups?