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Cost Engineering and Token Budgeting

LLM cost optimization is the discipline of systematically reducing per-request and per-feature API spend without sacrificing model quality or user experience. As large language model APIs cost between $0.50 and $15 per million input tokens (depending on model tier and provider as of 2026), understanding token economics is mission-critical for any production AI system handling scale. Companies deploying Claude, GPT-4, or Mistral at volume often find that unoptimized prompt engineering and naive request routing waste 30–60% of their budget on redundant processing, retry loops, and oversized models. This series teaches you to audit token spend, route requests intelligently by task difficulty, compress prompts without losing accuracy, enforce hard budgets, batch process asynchronously, and build real-time cost dashboards that surface spend anomalies before they explode your quarterly bill.

By the end of these ten articles, you will have built a complete cost-aware LLM architecture: from low-level token counting scripts through per-feature financial models, integrated budget enforcement, and observability tools that let you answer "how much did feature X cost to run last month?" in seconds. You'll understand how to route a customer-support query to a small 70B model instead of a premium 405B flagship, when to compress context windows with sophisticated retrieval strategies, how batch APIs cut costs in half for non-real-time workloads, and how to design prompts that respect both accuracy and spend constraints. Whether you're operating a chatbot serving thousands of users daily or a data-labeling system processing millions of examples, the patterns here apply across domains and providers.

Articles in this series​