The LLM Redundancy Tax: How Prompt Caching Cuts API Costs by 90% скачать в хорошем качестве

The LLM Redundancy Tax: How Prompt Caching Cuts API Costs by 90%

Are you paying the "Redundancy Tax" on your AI applications? In traditional stateless LLM interactions, up to 95% of your compute spend is wasted re-processing the same static system prompts, documents, and instructions for every single user request. In this video, we break down the economics and architecture of Prompt Caching (specifically Anthropic's implementation), a strategy that converts "ephemeral" token processing into a "semi-persistent" asset. We explain how this shifts unit economics from linear growth to sub-linear growth, allowing you to decouple the cost of context storage from the price of reasoning. Key Topics Covered: • What is the LLM Redundancy Tax? Why stateless systems force you to pay full price ($3.00/MTok) for data the model has already seen. • The "Token Arbitrage" Opportunity: How caching creates a 90% discount ($0.30/MTok) and reduces latency by 85%. • The "System 1 vs. System 2" Architecture: How to split your AI into cached context (fast/cheap) and dynamic reasoning (slow/expensive) to subsidize deeper intelligence. • Real-World Case Study: How a YouTube Analytics bot dropped its daily cost from $24.40 to $2.69 (an 89% reduction) just by caching its metadata, • The "Use It or Lose It" Rule: Understanding Time-To-Live (TTL) and why you need a "burst" of at least 3 requests every 5 minutes to break even,.• The "Exact Match" Trap: Why a single trailing space or unsorted JSON key can cost you money.