However, just like dynamic routing, semantic caching demands a rigorous architectural calculus. You are trading generation costs for embeddings and lookup costs. To check the cache, you still have to tokenize the prompt, call a cheap model (like text-embedding-3-small), and execute a vector search.
You also introduce the very real danger of semantic flattening. Tuning the similarity threshold is a delicate art. Set it too low, and your application starts serving general, recycled answers to nuanced user questions. For open-ended, creative, generative AI applications, semantic caching is practically useless. But for retrieval-augmented generation (RAG) implementations, customer support bots, and internal knowledge bases where users ask the same 20 questions a thousand different ways, it is the most effective cost-reduction lever you can pull.
Prompt caching
Whereas semantic caching stores the response to a given intent, prompt caching stores the data needed to contextualize the question. When the user enters a prompt, it is sent to the generative AI endpoint. But instead of your application having to repeatedly gather and send the massive contextual info needed to frame the prompt—from a RAG pipeline, a database, or other source—that information is already pre-loaded in the context cache. The model simply applies the new question to the cached data, slashing both your latency and your input costs.
