Caching Strategies to Slash Your LLM Bill | Prompt & Semantic Caching Explained with Demo

Description

Stop overpaying for your LLM API calls! 💸 If you are building AI applications, you’ve likely noticed that costs scale quickly. Every token—from your system prompt to chat history—adds up. In this video, I break down how to use Prompt Caching and Semantic Caching to slash your LLM bills by 30% to 70% using Azure OpenAI, Microsoft Foundry, and Redis. We explore the technical "rules" of caching, why exact text matching isn't always enough, and how to implement a meaning-based cache layer to handle similar user intent. 🔍 What You’ll Learn: Prompt Caching: How Azure OpenAI reuses processed prefixes to save costs on large system prompts. Semantic Caching: Using embeddings and vector similarity to avoid calling the LLM for repeated user questions. The Redis Layer: Why Redis is the ultimate "Swiss Army Knife" for AI (handling Caching, RAG, Session Memory, and Rate Limiting). Architecture Comparison: When to choose Redis vs. Azure API Management vs. Cosmos DB. Github - https://github.com/Shailender-Youtube/prompt-and-semantic-caching-azure 0:00 - Introduction 1:05 - Prompt Caching 8:05 - Semantic Caching 13.06 - Redis additional features 17:50 - Summary & Best Practices