How to Cut LLM API Costs in SaaS Using Prompt Routing and Batch Processing

A software engineer outlines a cost-reduction strategy for SaaS platforms that rely on large language model APIs, focusing on three core techniques: prompt routing, fallbacks, and batch processing. The approach involves classifying incoming requests by type and routing simpler, testable prompts to a smaller, cheaper model first, only escalating to a larger model when a local quality check fails. Engineers are advised to define clear output contracts and acceptance rules for each request class before choosing a routing strategy, rather than relying solely on model confidence scores. The article also presents a build-versus-buy comparison covering managed single-model setups, managed small-to-large routing, and self-hosted inference, helping teams choose based on traffic volume and operational capacity. The author cautions that adding a router introduces a second production system with its own policy, telemetry, and rollback requirements, and recommends collecting measurements before committing to a multi-model architecture.