Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Let's make this real with one company. Harvey is one of the best-funded AI companies built for a single industry. It started in 2022, with early backing from OpenAI, and it builds AI agents for lawyers. These agents read contracts, run due diligence, search regulations, and draft documents. By March 2026, Harvey had raised more than $1.2 billion and was valued at around $11 billion.
Its customers included more than fifty of the largest law firms in the United States, plus legal teams at big corporations. The platform reportedly charges about $1,000 to $1,200 per lawyer each month, with a twenty-seat minimum. That means a single firm signs up for at least $240,000 to $288,000 a year before running even one contract.
Harvey is useful here not because it is unusual, but because it stands in for a whole category. Any company that sells AI agents to a large group of professionals (in law, finance, medicine, or consulting) runs on the same math. The professionals are the scale. The agents doing their routine work are the engine. And the model is the cost of every turn of that engine.
There is one more reason Harvey is a good example. The company has said openly that it uses more than one model provider. In mid-2025, it added Anthropic and Google models next to its original OpenAI setup. That tells you something. Even at the leading edge of legal AI, picking a model is not a one-time choice. It is an ongoing decision that someone has to own. And owning it means working across both engineering and finance, which is exactly what this chapter is about.
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