100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models

ArXi:2603.15970v1 Announce Type: cross Several data warehouse and database providers have recently This paper provides an extensive evaluation of a recent AI query approximation approach that enables low cost analytics and database applications to benefit from AI queries. The approach delivers >100x cost and latency reduction for the semantic filter (AI. IF) operator and also important gains for semantic ranking (AI. RANK). The cost and performance gains come from utilizing cheap and accurate proxy models over embedding vectors.