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

A technical guide published on DEV Community outlines four practical strategies for controlling costs when building large language model applications in Python. LLM providers like Anthropic charge separately for input and output tokens, with output costing significantly more due to its sequential generation process. A key insight is that long, repeated system prompts — not user queries — typically drive the bulk of API spending, making stable-prefix caching the highest-leverage cost-reduction tool. The guide explains how Anthropic's prompt caching works via exact byte-matching of prefixes, with cache reads costing roughly one-tenth of standard input pricing. Additional levers covered include the Batches API for non-urgent tasks and model routing, where a cheaper model handles simple requests and escalates only complex ones to more expensive models.