Prompt Caching
Storing and reusing processed portions of AI prompts to reduce latency and costs.
Definition
Prompt caching is an optimization technique where the processed representations of frequently used prompt components are stored and reused across API calls. Instead of reprocessing the same system prompt or context for every request, cached versions are retrieved instantly.
This technique is particularly valuable for applications with long system prompts or shared context, reducing both response latency and API costs. Major AI providers now offer built-in prompt caching for their APIs.
Why It Matters
Prompt caching can dramatically reduce AI costs for applications with consistent system prompts or shared context. For high-volume applications, savings of 50-90% on cacheable tokens are common.
Understanding prompt caching helps you architect AI applications efficiently and optimize for both speed and cost from the start.
Examples in Practice
A customer service AI caches its 3,000-token knowledge base prompt, reducing per-conversation costs by 75%.
A coding assistant caches repository context, enabling instant responses when developers ask follow-up questions.
An enterprise chatbot caches company policies and guidelines, slashing response times and monthly API spend.