Semantic Vector Cache & Embedding Solver

Underwrite in-memory Redis semantic vector caching storing pre-computed query embeddings, eliminating duplicate OpenAI text-embedding-3 calls and slashing RAG latency.

Monthly Queries & Cache Hit Rate

Monthly RAG Search Queries (Millions - e.g. 45M)

Semantic Similarity Cache Hit Rate (% - e.g. 48%)

Latency Reduction & Cloud Spend Slashed

Semantic Cache Hit Response Latency

1.2 ms (Sub-2ms In-Memory SIMD)

Monthly LLM API Token Spend Slashed

+$2,808 / month Saved

Annual Total Compute Spend Slashed

+$33,696 / yr Direct LLM Savings