LLM cost optimization is fundamentally a token economics problem, as every API call to OpenAI, Anthropic, or Google Gemini bills by the token. Most production systems send far more tokens than necessary due to verbose system prompts, repeated context, unconstrained output lengths, and enabled chain-of-thought reasoning, causing bills to run two to three times higher than needed. The tutorial 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%, though the exact figure depends on use case, model selection, and traffic patterns. Recommended steps include instrumenting token logging on every API call to establish a cost baseline, compressing system prompts by eliminating hedge language and using tools like LLMLingua, constraining output length with max_completion_tokens or max_tokens, enforcing structured JSON schemas, pruning chain-of-thought reasoning in production by instructing the model to return only the final answer, implementing semantic caching using embedding similarity to skip redundant API calls, and leveraging provider-native prompt caching from OpenAI, Anthropic, or Google for automatic input token discounts. Each section includes working code examples in Python and Node.js targeting the OpenAI and Anthropic APIs directly, with measured token counts showing before and after. The audience is developers already calling LLM APIs in production or at scale.