AI-Ready Vector Store & Retrieval

Trillion-Vector Scale 

The World’s Most Scalable Vector Store

VAST DataBase scales beyond the limits of traditional vector databases. Built on the DASE architecture, every compute node directly accesses all data—no sharding, no replication, no hotspots. This design enables linear scaling to trillions of vectors with consistent, low-latency performance, delivering real-time similarity search and inference at true enterprise scale.

Linear Scaling Without Sharding or Partitioning

Traditional vector databases rely on memory-bound indexes and sharding to scale, fragmenting data and introducing coordination overhead. The VAST DataBase, built on VAST’s Disaggregated, Shared-Everything (DASE) architecture, eliminates these limits. Every CNode has direct access to all data, so adding nodes instantly expands compute power without partitions or sharding, delivering linear, low-latency performance even at trillion-vector scale.

Hierarchical Vector Indexing for Real-Time  Performance at Scale

Traditional vector databases rely on memory-resident graph indexes and sharding, leading to coordination overhead, disk-bound traversal, and rising costs at scale. The VAST DataBase uses a hierarchical clustering index embedded directly in the database, organizing vectors into multi-level centroid groups. Search progressively narrows to relevant clusters, avoiding global fan-out and full index scans—delivering consistent, high-recall retrieval as datasets scale from billions to trillions.

Integrated Vector Store

Native Vector Integration

VAST treats vectors as a first-class data type that coexists with structured and unstructured data on a single platform. Vector embeddings are stored directly in VAST DataBase tables, unified with the data in the VAST DataStore.

Integrated Vector Store, Simplified Architecture

Legacy systems bolt on vector extensions or depend on separate databases, multiplying complexity and governance risk.

VAST DataBase eliminates that sprawl with a natively integrated vector store. Vectors coexist with your data and metadata in one place, removing the need for external systems and enabling real-time, context-aware AI.

Real-Time Vector Retrieval

Disaggregated Shared Everything Architecture

VAST's DASE architecture provides every compute node direct, parallel NVMe access to all-flash storage. This eliminates bottlenecks from local data caches or partition coordination, as every compute node can see and address all data in the system.

Co-Located Data for Low-Latency Retrieval

VAST DataBase organizes data into 32KB columnar chunks and keeps vectors, metadata, and raw content co-located on the same platform. This design minimizes data hops and eliminates secondary fetches, enabling sub-second retrieval performance across massive datasets. Every VAST CNode can access every flash drive through the DASE architecture, delivering consistent, predictable speed at scale—without sharding or external indexing layers.

Hybrid Queries

Vector-SQL Query Support with VAST Native Query Engine

VAST treats vectors as a first-class data type, stored natively alongside structured data and metadata. VAST’s Native Query Engine executes unified vector, SQL, and hybrid queries directly within the VAST DataBase—no orchestration layers, external indexes, or system handoffs required. This allows analysts and AI workloads to query, correlate, and interpret all data types together, in real time.

Memory-Free Design

Hyperscale Vector Store

Traditional vector databases depend on data in-memory to hold indexes, forcing costly scale-ups and limiting dataset size. VAST eliminates those constraints by storing all data and vectors on a single flash-native layer, using Storage Class Memory for low-latency writes and QLC flash for capacity. This architecture removes memory bottlenecks and reduces cost, enabling massive vector search without the fixed limits of in-memory systems.

Unified AI Governance

Governance Across Entire AI Pipeline

The VAST AI OS extends enterprise governance across every stage of the data lifecycle — from raw object data to derived vectors and query results. Row- and column-level permissions are enforced natively, and access controls can inherit entitlements from raw objects in the VAST DataStore. Because all data, vectors, and metadata reside on a single platform, every query and embedding automatically respects the same identity-aware policies. This unified model safeguards IP, supports secure RAG, and ensures compliance without duplicating or re-applying permissions across tools.

Complete Audit Trail and Compliance

Every data interaction within the VAST AI Operating System is automatically logged, creating a comprehensive audit trail of queries, searches, and serverless function executions. Through the VAST VMS, all pipelines are fully traceable and observable in real time — from data ingestion to inference. This unified visibility ensures compliance, simplifies debugging, and provides a verifiable record of every AI and analytics operation across the system.