The introduction of massive context windows (up to 2 million tokens in Gemini 1.5 Pro) has revolutionized AI architecture. However, it introduced a new bottleneck: the "Token Tax." If you are building a RAG (Retrieval-Augmented Generation) system, a legal document analyzer, or a codebase assistant, you likely face a recurring inefficiency. You send the same massive preamble—hundreds of pages of documentation or thousands of lines of code—with every single user query. This redundancy bloats your cloud bill and degrades Time to First Token (TTFT). For Senior DevOps engineers and AI Architects, the solution lies in Context Caching . The Root Cause: Why Stateless Inference is Expensive To understand why caching is necessary, we must look at how Transformer-based LLMs process input. The Gemini API, like most LLM interfaces, is stateless by default. When you send a request containing a 50,000-token system instruction and a 50-token user query, the model does not "remember...
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