Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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This brings us back to where we started. The difference between Opus and Maverick on this one workflow is about $1.5 million a year. And a company like Harvey does not run just one workflow. It runs many: contract review, research, drafting, and compliance. Depending on how model choices are made, the total model bill could land anywhere from $500,000 to $15 million a year. That is not a small technical detail. It is a real conversation about margins, pricing, and how the product is built.
On one side is an engineer who picked a model for good engineering reasons. It was fast, accurate, and easy to build with. In doing so, they also set the cost structure for the whole product. On the other side is a finance leader building cost forecasts on top of that choice, often without having seen it. Both are doing their jobs well. But each one's work depends on understanding the other.
This is the new overlap, and it is here to stay. With agentic AI, the line between an engineering decision and a financial one has mostly disappeared. The most useful approach is to treat model choice as a shared, ongoing decision. Put engineering and finance in the same room, look at the cost of the work together, and revisit the choice as the market changes. Reviewed regularly, model selection becomes a normal part of running the product rather than a surprise later on.
There is one more point worth raising, gently, because it does not have a clean answer. Some of the cheapest and most capable open models, including Qwen and DeepSeek, come from labs in China. Their cost advantage looks real and likely to last, helped by lower labor costs, different ways of buying compute, and strong government support. For a company serving customers around the world, this raises questions that a simple cost comparison cannot answer.
They involve compliance, where data is allowed to flow, and what customers are comfortable with. The cost math might point one way. A cautious enterprise customer might point another. These are not purely engineering questions, and they are not purely finance questions. They are exactly the kind of decision that lives in the overlap this chapter keeps coming back to.
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