In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how to choose between them.
Topics we will cover include:
What prompt caching is, how it works, and when it reduces costs and latency most effectively.
What fine-tuning is, why parameter-efficient methods like LoRA keep compute costs manageable, and when it is the right tool for the job.
A practical decision framework for applying prompt caching, fine-tuning, or a hybrid of both to your agentic architecture.
