From 10 Million to 10 Billion: Vector Search Built to Grow

LanceDB Enterprise presents a scalable vector-search platform designed to grow from 10 million to billions of embeddings while allowing users to adjust search precision, throughput, storage, and indexing strategies by workload. In benchmarks on 10 million 768-dimensional image embeddings, five-bit RaBitQ achieved 96.2% recall at 1,614 queries per second, outperforming tested product-quantization configurations in recall while maintaining comparable or better throughput through selective candidate pruning. The platform distributes initial index construction and uses different incremental-maintenance approaches for small and large data additions, avoiding full index rebuilds as tables expand. In a test dataset created by repeating 10 million vectors 1,000 times, its distributed architecture handled 10 billion entries with 18.05 ms median latency and up to 1,066 queries per second under higher concurrency. Query-time controls can switch between faster one-bit scoring and more precise multi-bit scoring without rebuilding indexes, while tiered use of memory, NVMe storage, and object storage is intended to balance cost and responsiveness for online search and offline retrieval workloads.

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