Built-In Vector Database
SingleStore delivers built-in similarity search on vectors to add memory for your gen AI apps.
The database market is saturated with speciality vector databases, which are bought and plugged into data architectures — only for users to quickly regret introducing yet another component into their application environment. Even more, specialty vector databases (SVDBs) introduce recurring problems like redundant data, excessive data movement, increasing labor and licensing costs and limited query power — and that’s just the start.
The good news? You don’t have to use an SVDB, but instead leverage vector similarity search that empowers you to build your AI-powered applications on a modern database that meets all of your performance requirements, not just one.
SingleStore offers powerful vector database functionality perfectly suited for AI-based applications, chatbots (like our very own SQrL), image recognition and more, eliminating the need for you to run a speciality vector database solely for your vector workloads. Unlike traditional vector databases, SingleStore stores vector data in relational tables alongside other types of data. Co-locating vector data with related data allows you to easily query extended metadata and other attributes of your vector data with the full power of SQL.
SingleStore is designed with a scale-out architecture, ensuring you have the capacity to support your growing data needs.
Deep query capabilities
Hybrid search based on vector nearness and descriptive properties is easy in SingleStore, because all the query capabilities of SQL are available.
Semantic search
Semantic search capabilities allow you to build applications based on LLMs that are capable of finding text that matches the meaning of your query, not just the words it contains.
Uncompromised performance
High-performance and scale-out capabilities allow SingleStore to keep up with even the most demanding database needs.
Nearest neighbor search
Since SingleStore supports joins, you can do set-based nearest-neighbor search in place of doing multiple queries to find desired results.
Simplify implementation + management
Deploy a vector database without the added complexity, licensing costs or extra training requirements.
Deep metadata filtering
Query with SQL to allow for powerful metadata filtering through SQL filters, joins and other language features.
Production ready
SingleStore is a highly performant, highly available scale-out database, meeting any application performance and scale needs without additional complexity.
Hybrid vector + full-text search
Re-ranking semantic search results are made easy with `dot_product` and `match` support. Users can leverage a combination of vector and full-text search features.
Advanced query processor
SingleStore can do fast K-Nearest-Neighbor search with `order by/limit k` queries using `dot_product` and `euclidean_distance` metrics, combined with arbitrary SQL for metadata filtering.
Test drive SingleStore
Enjoy the ultra-high performance and elastic scalability of SingleStore.
