This story was originally published on HackerNoon at: https://hackernoon.com/llm-cost-optimization-your-bill-is-an-architecture-problem-not-a-prompt-problem. Cut LLM inference costs with architecture-first techniques: model routing, context optimization, prompt caching, semantic caching, batching, and observability. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llm, #system-design, #ai-architecture, #generative-ai, #semantic-caching, #ai-unit-economics, #prompt-optimization, #ai-cost-monitoring, and more. This story was written by: @mdwasi. Learn more about this writer by checking @mdwasi's about page, and for more stories, please visit hackernoon.com. Most LLM cost problems cannot be solved by trimming a few words from a prompt. The bigger savings come from architecture: measure every call, route simpler tasks to smaller models, control context growth, design for prompt caching, use semantic caching carefully, batch asynchronous workloads, limit unnecessary output, and put budgets around retries and agents. Most importantly, optimize cost per useful outcome, not simply token spend.
