Unify Transactions, Analytics, and AI at Scale

VAST DataBase delivers exabyte-scale performance with no sharding, rebalancing, or replicas. Powered by VAST's DASE architecture, it maintains near-zero latency and linear scaling, so you can grow without limits or complexity.

Achieve up to 3:1 reduction with VAST's global deduplication and proprietary similarity-based compression. Unlike traditional systems that only detect identical blocks, VAST identifies similar patterns across the entire namespace and optimizes columnar data—all in-line, with no impact to performance.

VAST DataBase's 32KB columnar payload—16,000× smaller than Parquet—dramatically reduces scan size for blazing-fast queries. Combined with all-flash storage, it delivers pinpoint filtration and instant insights for analytics and AI at any scale.

Accelerate highly selective queries with VAST DataBase's sorted tables. Sorted tables deliver near-logarithmic search times, enabling lightning-fast filtering on massive datasets while preserving the efficiency of sequential read I/O.

VAST DataBase accelerates queries with predicate pushdown, returning only the exact data requested. By eliminating full table scans and reducing network traffic, it cuts latency, lowers compute costs, and speeds analytics at any scale.

VAST enables instant Co-Located Joins on massive tables. By eliminating the data "shuffle" and complex partitioning, the VAST DataBase executes joins locally within the architecture, delivering dramatically faster query response times.

VAST DataBase enables SQL-native partitioning for large-scale analytics. By eliminating manual partition management and data shuffling, the VAST DataBase automatically prunes data and optimizes execution—delivering faster, more efficient queries on large datasets.

VAST DataBase breaks the tradeoff between transactions and analytics. Instantly capture row-based writes, then seamlessly query columnar data at scale—delivering real-time insights and powering AI without separate systems or performance loss.

VAST DataBase uses deep write buffers built on low-cost persistent memory to achieve full ACID compliance for rapid, high-volume transactions, storing data instantly for speed, reliability, and scalability in mission-critical, real-time AI workloads.

VAST DataBase intelligently migrates data to a columnar format on low-cost flash, optimal for high-performance analytics at exabyte scale. Run complex queries and AI workloads instantly across massive datasets without tradeoffs in speed or efficiency.

VAST DataBase is fully ACID compliant, ensuring transactional integrity, consistency, and reliability at any scale. It supports complex, high-volume workloads without sacrificing performance, making it ideal for real-time analytics and AI applications.

VAST DataBase eliminates partitioning, vacuuming, and compaction. Data is automatically optimized and managed, delivering high-performance results for analytics and AI—without the maintenance of legacy systems.

Built for enterprise AI, VAST DataBase supports trillions of vectors using proprietary hierarchical indexing, enabling large-scale training, inferencing, and retrieval without performance loss—ideal for operational AI at global scale.

Native vector storage within VAST DataBase streamlines AI pipelines by managing structured data and embeddings together, simplifying operations and eliminating the need for separate vector databases.

VAST's DASE architecture gives every compute node direct access to all vectors, eliminating bottlenecks. This delivers sub-second similarity search, even at massive scale, for fast, accurate AI responses.

Run powerful hybrid queries that combine vector similarity search with traditional SQL filters and joins using VAST's Native Query Engine, enabling richer, context-aware AI results from both unstructured and structured data in a single request.

VAST DataBase stores all data, vectors, metadata, and structured data, on one scalable layer, avoiding CPU memory limits while still leveraging GPUs for speed. This lowers costs, reduces complexity, and supports massive vector search.

Because all data—from raw objects to vectors—lives on the VAST AI OS, policies and access controls apply consistently across the entire AI pipeline, simplifying compliance, reducing risk, and ensuring the right data is always in the right hands.

Process up to 2 million messages per second with 10x the performance of Kafka. VAST's stateless design and independent scaling deliver unmatched throughput for the largest real-time workloads without bottlenecks or limits.

Stream events directly into VAST DataBase tables for instant SQL querying and deep correlation—eliminating ETL delays and enabling AI/analytics on the freshest data the moment it's created.

No brokers, partitions, ISR replication, or local storage limits. VAST's stateless architecture and global storage remove Kafka's tuning and scaling headaches, reducing infrastructure sprawl and operational overhead.

Power data, analytics, and AI pipelines instantly. VAST's Event Broker integrates with the DataEngine to trigger tailored workflows for AI inference, data enrichment, and automation in real time as events are ingested.

Retain full-fidelity event data long term with zero performance tradeoffs. Query real-time streams and historical archives together for complete, context-rich analytics and anomaly detection.

Run analytics directly where data lives. The VAST Native Query Engine executes aggregations and statistical functions in-place, eliminating data movement and external pipelines. This simplifies architecture and delivers faster insights on live data.

Combine vector search, SQL filtering, and analytics in a single execution path. The VAST Native Query Engine processes similarity search, structured data, and aggregations together, eliminating cross-system joins and accelerating hybrid AI and analytics workflows.

Eliminate external engines and complex pipelines. The VAST Native Query Engine runs analytics directly on data, reducing system sprawl and operational overhead while improving performance and reliability at scale.