Beyond Vector Databases: AI-Scale Vector Search in the VAST DataBase

Vector databases are often deployed as standalone systems, introducing additional infrastructure, data duplication, and governance overhead. As vector workloads scale to billions and trillions of embeddings, these architectures struggle with performance, cost, and complexity.

In this webinar, we explored the VAST vector architecture, including its hierarchical indexing approach, native in the VAST DataBase. We reviewed benchmark results and show how VAST enables high-performance vector search at massive scale.

Session Takeaways:

• Why standalone vector databases introduce complexity and cost

• How VAST implements hierarchical vector indexing

• VAST Benchmark performance at large scale

• How VAST unifies vector search with analytics and storage

We demonstrated how VAST consolidates vector search, analytics, and data storage into a single system, eliminating the need for separate vector databases and enabling unified AI pipelines.

Looking for more? This session is part of From Data to AI: VAST Analytics Explained, a free 8-part technical series on building modern analytics and AI systems on VAST AI OS. Watch the on-demand series sessions here.