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SingleStore AI

SingleStore AI

SingleStore AI is a cloud-native data platform built for real-time, enterprise-grade AI applications. It uses a unified HTAP architecture that combines transactional processing and real-time analytics, and includes built-in vector search and AI functions to help teams quickly build and deploy high-performance, data-intensive applications while simplifying data architecture and streamlining processing.
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SingleStore AIreal-time data platformHTAP databasevector database for AIcloud-native databasereal-time analytics for AIenterprise AI data platformunified transactional and analytical processingvector search databasemultimodel database

Features of SingleStore AI

Unified HTAP engine that handles transactions and real-time analytics in a single engine, reducing data movement.
Built-in vector search with K-NN support for semantic search, recommendations and other AI-native applications.
AI & ML functions that let you invoke large language models directly from SQL for tasks like text analysis.
Support for multiple data models — relational, JSON, vectors, geospatial and time-series — in one platform.
Helios cloud database service with built-in support for retrieval-augmented generation (RAG).
Universal Storage architecture that blends memory and disk to optimize real-time ingestion and query performance.
Independent, elastic scaling of storage and compute, with high availability and multi-cloud deployment options.
Data API for executing SQL over HTTP, making it easy to integrate with serverless and microservices architectures.
Integrations with Jupyter Notebook, dbt and Apache Iceberg to streamline analytics workflows.
Kai™ API for MongoDB applications, optimized for faster JSON analytics without changing application queries.

Use Cases of SingleStore AI

Building low-latency BI dashboards that unify and analyze streaming data in real time.
Developing AI applications—such as intelligent virtual assistants or recommendation engines—that rely on fast vector similarity search and hybrid queries.
Real-time monitoring and analytics for high-volume IoT time-series data.
Creating retrieval-augmented generation (RAG) applications by combining vector search with large language models to provide contextual answers.
Real-time fraud detection in finance by analyzing fast-changing transactional data.
Personalized, real-time customer marketing that analyzes behavior to generate dynamic recommendations.
Modern SaaS development requiring multimodel data handling and support for high concurrency.
Model training and inference workflows that run ML workloads securely inside the database.
Cross-cloud or hybrid deployments that need elastic scaling and unified data management.
Combining batch analytics with real-time processing to simplify architecture and improve operational efficiency.

FAQ about SingleStore AI

QWhat is SingleStore AI?

SingleStore AI is a cloud-native, real-time data platform designed for enterprise AI applications. It uses a unified HTAP architecture to support transactions, real-time analytics, vector search and multimodel data, aiming to simplify data architecture and speed up the development and deployment of real-time AI applications.

QWhat are the main features of SingleStore AI?

Key features include a unified HTAP architecture, built-in vector search and AI functions, support for multiple data models, high-performance real-time processing, elastic cloud-native deployment, and seamless integration with common data ecosystem tools.

QWho should use SingleStore AI and for what scenarios?

It’s suited for enterprises and developers building real-time, data-intensive applications. Common scenarios include real-time BI, AI/ML applications, fraud detection, IoT analytics, personalized customer experiences and retrieval-augmented generation (RAG).

QHow does SingleStore AI handle AI-related workloads?

The platform provides built-in vector search for similarity queries and offers AI & ML functions that allow calling large language models directly from SQL. It also supports running compute securely inside the database via code execution engines for data-local processing.

QWhat deployment options does SingleStore AI support?

SingleStore AI supports multi-cloud deployments (AWS, Azure, GCP), hybrid and on-premises setups. Its Helios service offers a managed cloud database option, and the architecture supports separation of storage and compute for elastic scaling.

QHow does SingleStore AI integrate with an existing data stack?

It’s designed to plug into existing data architectures without replacing storage. Integration options include a Data API, dbt connector, Apache Iceberg support and Jupyter Notebook compatibility to work with existing analytics tools and workflows.

QDo I need to change existing MongoDB applications to use SingleStore AI?

No — the SingleStore Kai™ API can accelerate analytics for existing MongoDB applications without requiring changes to application queries or data conversion.

QWhat performance can I expect from SingleStore AI?

The platform is built for real-time workloads and advertises millisecond-level query latency and high concurrency. Actual performance depends on your deployment configuration and workload characteristics.

QWhat types of data does SingleStore AI support?

SingleStore AI supports multimodel data: traditional relational tables, JSON documents, vector embeddings, geospatial data and time-series data, enabling complex modern application use cases.

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