Three Python Techniques to Cut Claude API Costs Without Switching Models

Developers using Anthropic's Claude API can significantly reduce LLM spending through three key strategies: prompt caching, the Message Batches API, and model routing. Prompt caching allows repeated prefixes like system instructions or large documents to be reused at roughly one-tenth the normal input token price, though a minimum token threshold must be met for caching to activate. The Batches API offers a 50% discount on standard token pricing for asynchronous, non-latency-sensitive workloads such as nightly summarization or bulk classification, with results available for up to 29 days. Both discounts can be combined, meaning a large batch of calls sharing a cached system prompt benefits from both savings simultaneously. Model routing — directing simpler tasks to less expensive models before they reach a call — represents a third lever that can be applied before a request is even sent.