LLM API costs add up fast. A single Claude or GPT-4 call can cost cents, and at scale those cents become thousands of dollars. Token efficiency isn't about being cheap — it's about being smart. The right prompt structure, caching strategy, and model selection can cut your API bill by 50-80% while maintaining the same output quality. This matters whether you're building an AI startup burning through credits or a developer trying to stay under the free tier. We cover practical techniques: prompt compression, response caching, model routing (use cheap models for easy tasks), and context window management.
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