Traditional exact‑match caching fails for large language model (LLM) queries because even minor rephrasings generate new hashes, forcing costly API calls. For AI‑driven customer support handling thousands of daily queries, this hidden tax can quickly inflate expenses. Bifrost, an open‑source LLM gateway, introduces semantic caching by converting prompts into embedding vectors with a lightweight model such as OpenAI's text‑embedding‑3‑small.

The system searches a vector store for high‑similarity entries, returning cached responses when similarity exceeds a configurable threshold (typically 0.8‑0.95). This dual‑layer architecture—combining exact hash lookups with semantic similarity search—delivers hit rates of 40‑70% on FAQ‑type questions, slashing LLM usage by 40‑85% and reducing latency from seconds to milliseconds. Production data shows a documentation chatbot dropping monthly costs from $500 to $302 with a 40% reduction, while response times improve from 2,000 ms to 50 ms.

The approach scales to millions of queries, delivering tens of thousands of dollars in annual savings and higher throughput without additional infrastructure. Bifrost abstracts vector database management, allowing developers to point existing LLM clients at its endpoint and realize immediate financial and performance benefits.