Accuracy Is Expensive: How to Evaluate ‘Quality per $’ for Agents and RAG

Cost/Latency-Aware Evaluation: Quality per Dollar, Token Efficiency, and Time-to-Answer Accuracy Is Expensive: How to Evaluate ‘Quality per $’ for Agents and Retrieval-Augmented Generation (RAG). Building a prototype AI agent or RAG system that works flawlessly on your laptop is relatively easy today. Getting that same system into a high-traffic production environment is where the real engineering begins. Suddenly, you realize that state-of-the-art accuracy has a literal, heavily compounding price tag. Developers naturally obsess over leaderboard metrics and benchmark scores. Yet, in real-world deployments, token costs and system latency are often ignored until the first massive API bill arrives or users churn due to slow responses. In this article, you will learn how to shift your engineering mindset from purely qualitative evaluation to cost- and latency-aware metrics. We will explore how to measure "quality per dollar," optimize token efficiency, and build evaluation pipelines that