How Java Developers Can Cut LLM Costs Using Prompt Caching and Model Routing

A technical guide published on DEV Community outlines practical strategies for reducing the cost of running large language model applications in Java. The post explains how Anthropic prices input and output tokens separately, with output consistently more expensive due to autoregressive generation, making verbose prompts and large system prefixes a significant cost driver. Prompt caching allows developers to mark stable request prefixes so repeated calls read from cache at roughly one-tenth the base input price, rather than reprocessing identical content each time. The guide also covers model routing, where a cheaper model handles straightforward requests and only escalates complex cases to a more powerful, costlier one. Throughout, the author emphasizes measuring actual usage before applying any optimization, noting that each technique carries its own overhead and can backfire if applied to the wrong workload.