The Problem If you're calling the Claude API with a large system prompt, every request reprocesses the same tokens from scratch. Production AI systems — agents, RAG pipelines, customer-facing assistants — routinely carry 10K–30K token system prompts (tool definitions, reference docs, few-shot examples). At $3/MTok across hundreds of thousands of daily requests, redundant prefix processing can easily run $500–$3,000+/day. That's pure waste for context the model has already seen. Anthropic's prompt caching solves this. You mark a stable prefix as cacheable, pay a small one-time write surcharge (1.25×), and every subsequent request reads that prefix at 10% of the standard price . I ran a controlled experiment to measure the real-world savings. Here are the numbers.…