Sending every request to your most expensive model is the default — and it’s a margin problem waiting to happen. Routing, caching, and instrumentation fix it without users noticing.
The quiet killer of AI products isn’t accuracy — it’s unit economics. A feature that delights in beta can become unsustainable at scale when every request hits a premium model. The good news: most of that cost is avoidable without anyone noticing a drop in quality.
Not every step deserves your best model
Inside a typical agent or pipeline, the steps are wildly uneven in difficulty. Classifying intent, extracting a field, or formatting output is routine. Synthesizing a final answer or making a judgment call is not. Routing sends routine steps to cheaper, faster models and reserves premium models for the high-impact ones.
Done well, the user sees the same quality. Your bill doesn’t.
The levers that actually move cost
- Model routing — match model capability to the difficulty of each step.
- Caching — stop paying twice for the same work; identical or near-identical requests should be cheap.
- Batching and scheduling — group work and run non-urgent tasks when it’s efficient to.
- Prompt and context discipline — tokens you don’t send are tokens you don’t pay for.
You can’t optimize what you don’t measure
The prerequisite for all of this is instrumentation. You need cost and latency per feature, per tenant, and per workload — otherwise “make it cheaper” is guesswork, and you risk degrading quality to save pennies in the wrong place.
We instrument unit economics from the start, so cost is a dial you can turn deliberately, with evaluation guarding quality on the other side. That combination — routing plus measurement plus evals — is how AI stays both good and affordable as it scales.
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