AI is becoming easier to build. Understanding its economics is becoming harder. Large Language Models have dramatically lowered the barrier to adding sophisticated AI capabilities to software. What once required specialized models, infrastructure, and machine-learning expertise can increasingly be accessed through APIs, open models, and AI platforms. But making AI easy to add does not make it easy to design economically.
An AI feature may appear inexpensive during early experimentation. As usage grows, however, applications can consume more tokens, process larger contexts, make multiple model and tool calls, retrieve more knowledge, and require greater infrastructure capacity. Model selection, inference configuration, architecture, and workload placement can all change the economics of the application.
AI Application Design and Economics course provides a framework for understanding these trade-offs. In this course, you will explore how AI applications are designed across the model, inference, and application layers, and how decisions at each layer affect cost, quality, latency, resource consumption, and scalability.
