Vector search is the engine that powers everything from intelligent chatbots to real-time recommendation engines, acting as the memory for today’s most advanced AI systems. But as these applications move from proof-of-concept to production, and scale to support enterprise demands, the underlying infrastructure is starting to show its cracks.
Traditional vector stores rely on fragmented architectures. They are often deployed as separate databases, introducing additional silos, extra pipelines, and inconsistent governance policies.
With the latest general availability release from VAST Data, vector store and retrieval is no longer just a separate tool; it's a native capability of the VAST DataBase. This means vectors and traditional structured data can easily coexist in tables that are queried through the same unified engine. It’s a fundamental shift that simplifies your data stack, accelerates development, and puts a solid foundation under your AI pipelines.
The VAST Vector Database: Unified and Ready for Enterprise AI
Building an enterprise-grade AI system requires an array of complex capabilities above and beyond vector search. Models need context from many sources, and that context is rarely just a vector. It's the associated text file, the raw video frame, or the structured metadata in a table. The conventional approach is to scatter this data across a handful of specialty systems, creating complex pipelines that are slow, fragile, and prone to consistency and governance issues.
The VAST DataBase with native vector store solves this by treating vector embeddings as a first-class data type. Vectors are stored natively in the VAST DataBase, alongside structured data, with unstructured raw data and chunks accessible in the same platform on the VAST DataStore that holds all your data. This co-location is the key to simplifying your AI infrastructure.
This native integration unlocks powerful capabilities:
Hybrid Queries: You can issue a single query that combines vector similarity with traditional SQL filters. For example, an application can find the nearest neighbors for an image embedding while simultaneously filtering the results to only include products from a specific brand or with a price below a certain threshold.
Real-Time Ingestion: Data is made searchable the moment it is created. VAST enables real-time ingest, and with the integrated VAST Event Broker, makes it easy to build scalable AI pipelines to vectorize, chunk, and store in real-time. This ensures your AI agents and applications always have access to the freshest information.
Flexible Distance Metrics: The system supports multiple distance functions, including cosine similarity, Euclidean distance, and inner product, allowing you to choose the right metric for your specific use case.
By co-locating all of this data, a single query path can retrieve the nearest vectors and instantly resolve them to their full-context source content. There's no need for a complex, multi-step retrieval process or a separate orchestration layer.
InsightEngine: The Unified Framework for AI Pipelines and RAG
VAST Vector Search works with the VAST InsightEngine, our unified framework that makes it simple to build real-time AI ingest and RAG pipelines at scale. InsightEngine is built for the demands of real-time AI, providing transactional integrity and cross-modal governance across all your data types. It is the component of the VAST DataEngine that enables pipeline automation and serves as a single interface for all your data.
With InsightEngine, applications gain:
Unified access: vectors, files, and tables managed as first-class data types
Real-time consistency: newly ingested data is searchable immediately, across all modalities
Pipeline automation: triggers and functions orchestrate ingestion, enrichment, and retrieval at scale
InsightEngine provides a robust foundation that powers the entire AI lifecycle, from data ingestion and preparation to real-time inference and model training.
Enterprise-Scale Vector Search: Trillions of Vectors, Without the Trade-offs
One of the biggest hurdles for enterprise AI is scaling vector search to truly massive datasets. As your data grows from millions to billions or even trillions of vectors, legacy architectures buckle under the pressure. Specialized vector databases often rely on fixed memory or sharding strategies that introduce complexity and bottlenecks.
VAST Vector store and search is engineered to handle this scale with consistent, low-latency performance. We achieve this through a fundamentally different architecture designed for extreme scale.
The DASE Advantage
At the heart of Vector Search is the DASE architecture, which scales databases without the complexity of sharding or data movement. DASE decouples compute from storage, yet every stateless compute node maintains direct, low-latency access to the entire dataset over NVMe-over-Fabrics. This means throughput scales linearly as nodes are added, performance remains consistent from millions to trillions of vectors, and one unified architecture can handle vector, structured, and unstructured data with ease.
A Foundation Designed for AI of Today (and Tomorrow)
By embedding vector search directly into the VAST DataBase, the system provides a unified, real-time foundation for retrieval, reasoning, and context. Vectors, structured records, and unstructured content are all accessed through a single query path, ensuring consistency and eliminating the complexity of managing separate vector stores. Built on the DASE architecture, this approach scales linearly to enterprise workloads while maintaining low latency and strong governance controls. The result is a platform designed not just for today’s AI pipelines, but as a durable foundation for the next generation of enterprise-scale AI applications.
Ready to see what unified vector search can do for your data? Explore how VAST transforms retrieval and reasoning at scale: contact us or request a demo today.
