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The SQL Vector
Database for Scalable AI

MYSCALE

Enable every developer to build production-grade GenAI applications with powerful and familiar SQL. Minimal Learning, Max Value, and Cost-Effective.

Trusted SQL/Integrated Vector Database

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Fully SQL-Compatible Vector Database

Perform fast, powerful, and efficient vector search, filtered search, and SQL-vector join queries.

Index creation & search
Filtered search
Complex queries
  
    
  
Index creation & search
Filtered search
Complex queries
  
    
  

Enhanced RAG Effectiveness

  • Achieve superior RAG (Retrieval-Augmented Generation) results
  • Hybrid search capabilities for a comprehensive data retrieval experience
  • Advanced filter search options for precise data filtering
  • Flexible data modeling to adapt to evolving business needs

High-Performance and Cost-Efficient

  • 3x the speed, 3x the savings
  • Performant & powerful metadata filter search
  • Faster index building

SQL/Relational Vector Database

  • Manage and query AI-related data in one place
  • Supports complex SQL vector queries
  • Advanced text-to-SQL support

Integrates with Your AI Stack

  • Deep integration with popular development languages and frameworks

Security and Compliance

  • SQL-based RBAC
  • In compliance with SOC 2

The Generative AI Stack

Build and deliver quickly with the best integrations and tools

Work seamlessly with tools and popular languages adopted by leading Generative AI enterprises and LLM developers

AI integrations
Language SDK
File formats

FAQs

Your quick start guide to getting to know MyScale. We are here for you - reach out through the provided channels, and let's chat.

Explore the latest updates in MyScale

Stay informed about the newest developments in MyScale by browsing through our recent articles.

A Deep Dive into SQL Vector Databases

SQL databases are adept at storing and managing structured data in a table-based structure, while vector databases excel in handling unstructured data. The fusion of these features is realized in SQL vector databases, enabling efficient storage and querying high-dimensional vectors using SQL.

Optimizing Filtered Vector Search in MyScale

Filtered vector search is becoming increasingly vital for intricate retrieval scenarios in which vectors usually come with metadata, and users often need to apply filters to this metadata. MyScale optimizes filtered vector search using the proprietary algorithm, MSTG, together with ClickHouse for fast filtering of structural data.

& MYSCALE

Building a RAG-Enabled ChatBot with MyScale

Large Language Models (LLM) can be more reliable in truthfulness powered by Retrieval Augmented Generation (RAG). MyScale provides an advanced usage to the RAG pipeline: Building a chatbot with MyScale as your only data source and doing all the data hosting jobs, from vector search to chat history management.

A Deep Dive into SQL Vector Databases

SQL databases are adept at storing and managing structured data in a table-based structure, while vector databases excel in handling unstructured data. The fusion of these features is realized in SQL vector databases, enabling efficient storage and querying high-dimensional vectors using SQL.

Optimizing Filtered Vector Search in MyScale

Filtered vector search is becoming increasingly vital for intricate retrieval scenarios in which vectors usually come with metadata, and users often need to apply filters to this metadata. MyScale optimizes filtered vector search using the proprietary algorithm, MSTG, together with ClickHouse for fast filtering of structural data.

& MYSCALE

Building a RAG-Enabled ChatBot with MyScale

Large Language Models (LLM) can be more reliable in truthfulness powered by Retrieval Augmented Generation (RAG). MyScale provides an advanced usage to the RAG pipeline: Building a chatbot with MyScale as your only data source and doing all the data hosting jobs, from vector search to chat history management.

A Deep Dive into SQL Vector Databases

SQL databases are adept at storing and managing structured data in a table-based structure, while vector databases excel in handling unstructured data. The fusion of these features is realized in SQL vector databases, enabling efficient storage and querying high-dimensional vectors using SQL.

Optimizing Filtered Vector Search in MyScale

Filtered vector search is becoming increasingly vital for intricate retrieval scenarios in which vectors usually come with metadata, and users often need to apply filters to this metadata. MyScale optimizes filtered vector search using the proprietary algorithm, MSTG, together with ClickHouse for fast filtering of structural data.

& MYSCALE

Building a RAG-Enabled ChatBot with MyScale

Large Language Models (LLM) can be more reliable in truthfulness powered by Retrieval Augmented Generation (RAG). MyScale provides an advanced usage to the RAG pipeline: Building a chatbot with MyScale as your only data source and doing all the data hosting jobs, from vector search to chat history management.