Vector search is no longer just a lookup tool; it’s becoming the foundation for real-time memory, context retrieval, and reasoning in AI agents. But today’s vector infrastructure wasn’t built for this new era.
Whether you’re retrofitting a legacy database with vector extensions or deploying a standalone engine, the outcome is the same: another silo to manage, another fragile pipeline to maintain, and another gap in your governance model. Vector search becomes a bolt-on and not a foundation.
VAST removes these barriers by embedding vector capabilities directly into the VAST DataBase, a core component of the same unified, all-flash platform that powers your structured, unstructured, and streaming workloads.
Today, we’re introducing VAST Vector Search, the first major capability of our unified VAST native query engine. This engine powers real-time retrieval, transactional integrity, and cross-modal governance in one platform without creating new silos.
And this is just the beginning.
Future capabilities will expand beyond vector search, enabling new forms of hybrid reasoning, structured querying, and intelligent data pipelines - all from the same unified engine.
Let’s take a closer look at how this unified approach changes what’s possible with AI data.
Vectors, Files, and Tables—All in One Place
The VAST DataBase treats vectors as a first-class data type - coexisting with structured data in the same tables, and natively integrated with unstructured data in the VAST DataStore - all queried through the same engine and governed by unified policies.
Vector embeddings are stored directly inside the VAST DataBase, alongside traditional metadata and full unstructured content to enable hybrid queries across modalities, without orchestration layers or external indexes.
You can issue hybrid queries like:
Return the nearest neighbors for this embedding, where the title starts with ‘A’ and the author is ‘Colleen.’
This native integration enables agentic systems to retrieve memory, reason over metadata, and act - all without ETL pipelines, external indexes, or orchestration layers.
And vector search is just one capability of the VAST query engine that’s designed to handle vector, SQL, and hybrid queries through a single interface.
Real-Time Ingestion, Indexing, and Search
Before agents can retrieve or reason over data, they need it to be ingested, indexed, and made searchable the moment it’s created. That’s why real-time AI starts with real-time data preparation.
Incoming data is persisted to all-flash storage over NVMe and indexed in real time—building zone maps, vector indexes, and secondary indexes as part of the ingestion path With data organized into small, columnar chunks (32KB), every VAST compute node (CNode) can perform fast, locality-aware retrieval across trillions of records without relying on GPU memory or RAM-bound indexes.
The system uses sorted projections, precomputed materializations, and CPU fallback paths to maintain sub-second performance—even at trillion-vector scale. And because all indexes live with the data, every compute node can access them directly, enabling real-time search across all modalities - text, images, audio, and more - without system sprawl or delay.
How VAST Vector Search Works
Once data is ingested and indexed, agents need to retrieve the most relevant context—not just fast, but precisely matched to a task or prompt.
All vectors are stored natively in the VAST DataBase, alongside structured metadata and source content, enabling real-time search and full-context retrieval in a single query path. At query time, VAST compares the input vector to all stored vectors in parallel. This process uses compact, columnar data chunks to prune irrelevant blocks early and accelerate retrieval.
Multiple distance metrics are supported, including cosine similarity, Euclidean distance, and inner product. Users can configure query effort to trade off between speed and precision. By default, the system returns the top 1,000 closest matches, resolving each to a full table row that includes associated metadata and original content without calling external systems or indexes.
Thanks to VAST’s Disaggregated Shared Everything (DASE) architecture, every query node can directly access all vector and metadata indexes across flash storage via NVMe, eliminating the need for sharding, caching, or preloading into RAM or GPU.
Powered by VAST’s DASE Architecture
At the core of VAST Vector Search is our DASE architecture, a design that decouples compute from storage while allowing every compute node to access the entire global dataset directly over NVMe.
Unlike traditional vector databases that rely on sharding, GPU-bound indexes, or manual rebalancing, DASE supports linear scalability simply by adding compute—without data partitioning, hotspots, or coordination overhead.
This architecture unlocks:
Trillion-vector scale with consistent, low-latency performance
Real-time hybrid search across structured and vector data
Multimodal pipelines powered by unified access to all data types
Operational simplicity, with no index reloading or sharding complexity
DASE is what makes real-time, AI-native vector search possible at enterprise scale—without tradeoffs.
Let’s take a closer look at the before and after of vector infrastructure—through the lens of VAST.
BEFORE:
Manual sharding required: Large vector indexes had to be split across shards or instances, creating operational overhead and added complexity.
Memory-bound architectures: Legacy vector databases relied on RAM or GPU-resident indexes, degrading in performance once data exceeded memory limits.
Multi-store operations: Vectors and related data often lived in separate systems (e.g., object stores for raw media), requiring two-step retrieval and risking consistency drift.
AFTER:
Unified, memory-free architecture: VAST eliminates sharding and memory-bound limitations with its Disaggregated, Shared-Everything (DASE) architecture.
Single engine, single store: Vectors, metadata, and raw content live side by side in the multiprotocol Element Store—governed and queryable as one.
Real-time context, without orchestration: vector search, SQL filters, and metadata joins—all in a single query path.
Why Sharding Breaks AI and How VAST Fixes It
Sharding is the process of splitting data across nodes to scale out a system—but it comes at a cost. As vector datasets grow, sharded architectures introduce complexity, latency, and coordination overhead that quickly become bottlenecks for AI.
Traditional vector databases often rely on manual sharding or pod-based scaling. This might work at small scale, but it breaks down as data grows:
You must decide how to partition the vector space
Queries broadcast to all shards, then merge results
“Hot” shards create performance bottlenecks
GPU memory limits force constant rebalancing and trade-offs
VAST takes a different path. With our Disaggregated, Shared-Everything (DASE) architecture, compute and storage are decoupled but every compute node accesses the full dataset. That means:
Trillion-vector scale with no reconfiguration
Linear performance by simply adding compute (CNodes)
No east-west chatter—just direct NVMe access to all data on all-flash
Instant, low-latency access to large objects—no object store in the loop
With VAST, AI pipelines scale cleanly without shards, silos, or slowdowns.
Built-In Governance, End-to-End Security
AI systems that act on sensitive data must operate within strict, auditable guardrails—enforcing who sees what, and when, at every step.
That’s why VAST delivers consistent, fine-grained governance across the entire pipeline—from raw ingestion to vector indexing to AI-driven retrieval.
Most vector databases stop at performance. VAST goes further:
Row- and column-level permissions, inherited from systems like S3, SharePoint, or Google Drive
Filtering and masking based on roles, identities, and attributes
Full audit trails of queries, searches, and serverless functions—stored as queryable tables
Unified access control across files, objects, tables, and vectors—no silos, no drift
This level of built-in security is critical for secure RAG, IP protection, and compliance—because policy enforcement never breaks across formats or stages.
One Engine for Reasoning, Retrieval, and Real-Time AI
Whether you’re querying vectors, filtering with SQL, or orchestrating retrieval pipelines across text, images, and structured data, VAST executes it all through a single native engine—without orchestration layers, fragmented indexes, or handoffs.
Today, VAST powers real-time hybrid search. Tomorrow, the same engine will drive multimodal retrieval pipelines, structured reasoning, and intelligent data preparation—without fragmenting your AI infrastructure into disconnected systems.
Legacy stacks separate memory, analytics, and retrieval into siloed layers. VAST unifies them into a single, AI-native platform.
Ready to consolidate your AI, analytics, and data infrastructure? Join a demo to see VAST Vector Search and the VAST Data Platform in action. And join the vector search conversation on Cosmos, the community built for and by AI practitioners like you.
