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Follow these five sequential steps to architect a production-ready system that cuts costs without compromising performance or quality.
Your AI feature shipped. Your cloud bill didn’t get the memo. #
Your AI feature works. Users are happy. Last month your cloud cost dashboard crossed $40,000, almost entirely from one line item: LLM API spend.
This is the conversation happening in engineering standups across the industry right now. The teams having it share one trait: they picked a frontier model during prototyping, it shipped, and nobody went back to ask whether every request actually needed that model.
They usually didn’t. They still don’t. And the price gap for getting this wrong has grown considerably.
LLM API pricing in mid-2026 spans from $0.10 per million input tokens for budget models to $30 per million for frontier reasoning models — a 100× gap on output tokens. Most production workloads contain a mix of tasks that simply do not require top-tier inference for every call.
The fix is not about switching providers wholesale. It is about building a routing layer that stops paying frontier prices for work a cheaper model can handle. That layer is an LLM gateway. How you configure…