AlloyDB ScaNN Scales Vector Search to 10 Billion Vectors with Four-Level Tree Structure

The growing demand for enterprise agentic AI applications is pushing vector databases to handle billions of vectors. The previous AlloyDB ScaNN implementation was limited to a two- to three-level tree structure, causing computational load and memory shortage bottlenecks when scaling to 10 billion vectors.

To address this, AlloyDB introduced a four-level tree architecture. This structure significantly reduces computational intensity through hierarchical partitioning and handles large-scale workloads with efficient memory usage.

To balance accuracy and build efficiency, the system integrates key features such as Top-K branch, SOAR, centroid adjustment, and balanced tree shapes. This enables AlloyDB to deliver efficient vector search at the 10 billion vector scale, meeting modern benchmark requirements.