Routing Economy in B2B SaaS

The important shift is that AI models are becoming components, not products. In practice, that means SaaS companies will route each task to the cheapest model that is good enough, just like software chooses different databases, clouds, or chips for different jobs. Intercom already described this as a live product decision, using stronger models for harder reasoning and cheaper, faster models for routine support flows, which pushes power toward the application layer that controls workflow, data, and routing logic.

• This matters because the end user often will not touch a model vendor directly. Brex framed the likely interface as companies like Intercom, Zapier, and Brex embedding models inside their own products, so the customer experiences better support, automation, or expense review rather than consciously choosing a foundation model.
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• A multi model stack is also economically rational. OpenAI lists large price gaps across its model lineup, with GPT-4.1 priced above GPT-4.1 mini and nano, reinforcing the idea that product teams will reserve expensive models for high judgment tasks and use lower cost models for classification, extraction, and fast response paths.
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• That setup changes where defensibility lives. If raw model quality keeps converging across OpenAI, Anthropic, Mistral, and open models, the durable edge moves to the company that owns the customer workflow, the proprietary data, the evaluation layer, and the willingness to charge on outcomes like a resolved support ticket instead of a seat.
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The next phase of AI software is a routing economy. Winning products will quietly mix premium and commodity models behind the scenes, then package that complexity as a reliable workflow with clear ROI. That favors SaaS companies that own frequent user decisions and can turn model competition into lower costs, faster features, and more outcome based pricing over time.