A tutorial on reducing LLM API costs explains that cost optimization is a token economics problem, as every API call to providers like OpenAI, Anthropic, or Google Gemini bills by the token. It covers four techniques—prompt compression, semantic caching, chain-of-thought pruning, and output length constraints—that when combined can reduce LLM API costs by up to 63%. The techniques include compressing system prompts by eliminating hedge language and using tools like LLMLingua, implementing semantic caching using embedding similarity to skip redundant calls, pruning chain-of-thought reasoning in production by instructing the model to return only the final answer, and constraining output length with max_completion_tokens or max_tokens. The tutorial also recommends instrumenting token logging on every API call to establish a cost baseline before optimizing, and validating output quality against an evaluation set after each optimization. It notes that output tokens cost more than input tokens, by a factor of 2x to 5x depending on the model.