Prompt Compression and Cache Tuning: Cut Your LLM API Costs by 60%
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A practical guide to reducing LLM API costs by up to 63% using four techniques: prompt compression, semantic caching, chain-of-thought pruning, and output length constraints. Covers token economics across OpenAI, Anthropic, and Google Gemini, including pricing ratios and provider-native caching discounts. Includes working Python and Node.js code examples for each technique, a cost comparison table across five models, and a recommended order of implementation based on effort-to-impact ratio. Key insight: output tokens cost 2x–5x more than input tokens, making output constraints the highest-leverage optimization. Semantic caching using embedding similarity can eliminate API calls entirely for repeated query patterns.
