11x Faster Vector Search at 91% Lower Cost
Enterprise AI architectures are increasingly constrained by the retrieval layer. Most vector databases were designed when vector search was a niche capability, not a foundational component of production AI systems. In this white paper, we explore how VAST approaches vector retrieval differently. Rather than treating vectors as a separate system, VAST embeds vector storage and search directly into the VAST DataBase and VAST AI OS. The paper details the architecture behind VAST's 11x performance advantage over leading vector database alternatives, including how VAST scales retrieval, indexing, and governance on a single platform while reducing infrastructure costs by 91%.
Key Takeaways
Why traditional vector database architectures struggle with memory constraints, sharding overhead, and continuous data growth
How VAST's DASE architecture eliminates partition ownership and shard coordination bottlenecks
How a proprietary five-level hierarchical clustering index enables trillion-scale vector retrieval with bounded memory requirements
How VAST supports continuous ingest, clustering, and retrieval without index rebuilds, rebalancing events, or maintenance windows
Why storing vectors alongside structured and unstructured data simplifies governance, access control, and auditability
The benchmark results behind VAST's 11x higher query throughput and 91% lower cost per 1,000 queries compared to leading alternatives
How unified vector retrieval, analytics, and AI workloads can operate on a single governed platform
Download the white paper to explore the architecture, benchmarks, and design principles behind hyperscale vector retrieval on VAST AI OS.