Semantic caching can cut LLM API costs by matching query meaning, not exact strings

Developers calling large language model APIs for every user request often pay repeatedly for semantically identical queries phrased differently, since standard string-matching caches treat them as unrelated. A semantic cache addresses this by converting queries into vector embeddings and comparing their meaning using cosine similarity, so paraphrased questions can return cached responses. A working prototype can be built in roughly 40 lines of Python using libraries like SentenceTransformers and a simple in-memory store, without requiring dedicated vector databases. However, a key failure mode exists: embedding models can cluster semantically opposite queries — such as 'cancel subscription' and 'reactivate subscription' — due to shared vocabulary, potentially serving wrong cached answers. Developers are advised to add rejection rules or guardrails before deploying such a cache to production traffic.