As token budgets take on a larger part of operating expenses over time, model routing is the inevitable conclusion. This is also one of the biggest areas of differentiation for the applied AI layer over time. By understanding the different work patterns in your domain, and having strong evals for that domain, you’ll be able to cost/performance optimize effectively. We’re still likely at the point where most use-cases will need frontier performance for the foreseeable future; but soon you will be able to peel off individual use-cases and send them to lower cost models once the quality is sufficient for the task. Enterprises individually trying to figure this out themselves at scale will likely not be possible, so the products that can intelligently route these workflows to the right tier of model will be in a strong position to aggregate more demand.
随着 token 预算在运营支出中长期占据越来越大的比例，model routing（模型路由）将成为不可避免的结论。这也将是 applied AI layer（应用型 AI 层）长期最重要的差异化领域之一。通过理解你所在领域中的不同工作模式，并为该领域建立强有力的 evals（评估），你就能够有效地在成本与性能之间做优化。我们目前很可能仍处在这样一个阶段：在可预见的未来，大多数 use-case（用例）仍然需要 frontier performance（前沿性能）；但很快，一旦质量足以胜任任务，你就可以把单独的 use-case 剥离出来，发送给成本更低的模型。各家 enterprise（企业）如果想各自独立地、大规模地把这件事摸索清楚，可能并不现实，因此，能够智能地将这些工作流路由到正确模型层级的产品，将处于能够聚合更多需求的有利位置。