Product Update: Hyperscale Vectors, Native Analytics, and Pipeline Compute in One Platform

Enterprise AI architectures are hitting a familiar ceiling. Vector search runs in separate memory-resident systems. Analytical workloads require moving data to external engines. Pipeline compute sprawls across independently managed Kubernetes clusters. Each layer solves a problem, but together they introduce operational overhead and make real-time AI at scale difficult to sustain.

VAST AI OS 5.5 advances the platform across four dimensions. In this session, we walk through what’s shipping and why it matters. We’ll explore the new Hyperscale Vector Index, built on a proprietary hierarchical clustering architecture embedded directly in VAST DataBase, delivering 10x faster retrieval at 1 billion vectors while keeping search memory-bounded. We’ll show how the VAST Native Query Engine expands analytical execution with native SQL aggregations running alongside vector search. We’ll also cover VAST Native Kubernetes, which brings managed container orchestration inside the cluster for DataEngine pipelines.

Key Takeaways:

• How the VAST Hyperscale Vector Index delivers 10x faster retrieval at scale without memory-resident full-index overhead

• Why combining vector similarity and SQL in a single execution path expands what’s practical for real-world AI workloads

• How VAST Native Kubernetes removes the need for separate infrastructure to run DataEngine pipeline workloads

• What a unified platform for vectors, analytics, and pipeline execution looks like across ingest, enrichment, and retrieval

This release reflects a broader shift: vector retrieval, analytical execution, and pipeline compute can operate within a single system, directly on governed data.