Prompt caching: 10x cheaper LLM tokens, but how?

Questions & Answers

What is prompt caching in the context of LLMs?

Prompt caching involves storing the results of previous LLM input token processing to avoid re-calculating them for repeated prompts. This can lead to substantial cost savings and latency reductions for prefill tokens.

Who can benefit most from implementing LLM prompt caching?

Developers and engineers building applications that interact with large language models can benefit significantly, especially those aiming to reduce API costs and improve response times for repetitive or common prompt components.

How does LLM prompt caching differ from general application caching?

Unlike general application caching that stores computed data, LLM prompt caching specifically optimizes the processing of input tokens by an LLM, often at the API provider level, reducing costs and latency associated with recurrent prompt segments.

When should one consider implementing prompt caching for LLM interactions?

Prompt caching is most effective when your LLM applications frequently send prompts with identical or highly similar prefixes, system instructions, or recurring context, as these repeating segments are ideal candidates for caching.

What is a key technical benefit of LLM prompt caching?

A key technical benefit is that cached input tokens can be up to 10 times cheaper than regular input tokens for APIs like OpenAI and Anthropic. It can also reduce time-to-first-token latency by up to 85% for long prompts by leveraging the LLM's internal KV cache.