Tired of watching your AI API costs skyrocket? Prompt caching can save you up to 90% on your Claude bill by caching repeated content like system prompts and tool definitions. Let me show you exactly how to implement it in Spring AI.

In this tutorial, we'll explore what prompt caching is and why it matters for your AI applications. You'll learn how the context window works, what can be cached (system messages, tools), and build a complete Spring AI application that leverages Anthropic's prompt caching feature. By the end, you'll have a working implementation that dramatically reduces your API costs.

- Understand how the context window works and what content can be cached
- Learn which Anthropic Claude models support prompt caching and the potential savings
- Set up AnthropicChatOptions with caching strategies in Spring AI
- Implement system prompt caching using the SYSTEM_ONLY strategy
- Monitor cache creation and cache read tokens to verify caching is working

If you found this tutorial helpful and want to save money on your AI applications, give this video a thumbs up, subscribe to the channel, and start implementing prompt caching in your projects today!

0:00 - Intro - Why Prompt Caching Matters
0:30 - Understanding the Context Window
1:45 - What Can Be Cached
2:30 - API Pricing and Savings
3:15 - Spring AI Blog Post Overview
4:00 - Creating the Spring AI Project
4:45 - Setting Up the System Prompt
6:00 - Building the Chat Controller
7:30 - Configuring Anthropic Cache Options
9:00 - Creating the User Prompt
10:30 - Testing and Verifying Cache Hits
12:00 - Wrap Up and Key Takeaways

#SpringAI #PromptCaching #Anthropic #Claude #Java #SpringBoot #AIApplications #CostSavings

GitHub Repository: https://github.com/danvega/promptcache
Spring AI Anthropic Prompt Caching Blog Post: https://spring.io/blog/2025/10/27/spring-ai-anthropic-prompt-caching-blog
Spring Initializr: https://start.spring.io

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Dan Vega

Prompt caching is a technique to reduce LLM API costs by caching static parts of prompts like system messages and tool definitions, so they aren't re-processed on every request. A Spring AI application is built using the Anthropic Claude integration, demonstrating how to configure AnthropicChatOptions with a caching strategy (system-only) to cache a long system prompt. On the first request, cache creation tokens are logged; on subsequent requests, cache read tokens replace the full input cost, yielding up to 90% savings on cached tokens with models like Claude Sonnet 4.5.

Spring AI Prompt Caching: Stop Wasting Money on Repeated Tokens