Stop Burning Cash on Long-Context RAG: Ephemeral Prompt Caching with Spring AI and JTokkit

Stop Burning Cash on Long-Context RAG: Ephemeral Prompt Caching with Spring AI and JTokkit

If your enterprise RAG pipeline is processing megabytes of legal documents or codebase context, you are likely burning thousands of dollars daily on redundant input tokens. Ephemeral prompt caching can slash these LLM costs by up to 90%, but only if you align your token boundaries perfectly inside your Java backend.

Why Most Developers Get This Wrong

Blindly trusting Spring AI's defaults: Relying on default ChatClient configurations without verifying token boundaries, causing cache misses on every slight prompt variation.

Ignoring the 1024-token floor: Underestimating the strict minimum boundary requirements of providers like Anthropic or OpenAI, leading to zero cache hits for smaller context chunks.