The Unseen Optimization Reshaping LLM Economics: How Prompt Caching Cuts Inference Costs

As LLM applications multiply in production environments, the inefficiency of reprocessing identical token sequences across requests creates massive cumulative costs in compute resources and latency. Prompt caching solves this by preserving the transformer's internal representations (K/V tensors) rather than recalculating them, enabling substantial improvements in both speed and cost-per-inference. For teams deploying AI at scale, understanding this technique is essential to building economically viable systems.