Modern AI development requires more than just a database—it demands a unified data platform built for performance, simplicity, and seamless integration. MariaDB Enterprise Platform delivers a complete ecosystem of AI features and tools designed to accelerate your applications.
From our high-performance integrated Vector Embedded Search that outperforms pgvector, to the new MariaDB MCP Server that creates a frictionless bridge between your data and large language models (LLMs), MariaDB provides everything you need to build sophisticated RAG systems, semantic search, and other AI-powered applications without the complexity and cost of a fragmented data stack.
Vector Embedded Search
Store, Index, and Query Vector Embeddings Alongside Your Relational Data
The rise of artificial intelligence (AI) and machine learning (ML) has created a demand for databases that can efficiently handle both structured and unstructured data. MariaDB Enterprise Platform meets this demand by integrating vector search directly into its core database engine. This unlocks powerful new capabilities, allowing you to build sophisticated AI-driven applications using Retrieval-Augmented Generation (RAG) systems, semantic search, and recommendation engines.
By eliminating the need for separate vector databases, MariaDB streamlines your data infrastructure and allows you to leverage the synergy between traditional SQL operations and vector embeddings. By consolidating vector and relational data management, MariaDB Enterprise Platform simplifies development, enhances performance, and reduces costs, making it a cost-effective solution for organizations looking to harness the full potential of AI.
Simplified Data Stack for Vector Management
Streamline your data infrastructure by eliminating separate vector databases. MariaDB’s native support for vector search allows you to manage relational data and vector embeddings in one unified environment, reducing complexity, operational overhead, and data synchronization headaches.
High Performance for Demanding AI Workloads
Deliver high-performance vector search with fast, high-QPS similarity searches for your AI applications. By handling vector workloads natively, MariaDB ensures optimal performance and seamless integration without requiring specialized external databases.
Seamless Integration with Relational Data
Combine the power of vector search with your existing relational data. Perform complex hybrid queries that leverage both data types to uncover deeper insights. For example, query for visually similar products that also match structured criteria like price and inventory, all within a single SQL statement.
Cost-Effective Vector Search with MariaDB
Avoid the costs and complexity of separate vector databases. MariaDB Enterprise Platform lets you leverage your existing infrastructure for integrated vector search, reducing operational overhead and simplifying administration. Ditch the database-polyglot for a single, powerful solution.
Get Started with Vector Search
Featured Resources
VIDEO
Introduction to Vector Embeddings and Vector Search
WEBINAR
Introduction to building GenAI applications, LLMs, Vector Embedded Search and more
BLOG
Building AI Applications using frameworks with MariaDB Vector Store
MariaDB vs. pgvector
Vector Search Performance Benchmark
Small Datum LLC recently conducted rigorous benchmarks comparing MariaDB’s integrated vector search functionality with pgvector, a popular extension for PostgreSQL. The results offer compelling insights into the speed, efficiency, and ease of use of MariaDB’s vector search compared to pgvector.
High QPS (queries per second)
MariaDB consistently outperforms pgvector, delivering up to twice the QPS for a given recall target.
Fast index creation time
MariaDB is significantly faster, requiring less time to build an index while achieving comparable or superior recall (the percentage of truly relevant results, i.e., “true nearest neighbors,” that are returned by a query).
Ease of tuning
MariaDB simplifies the tuning process by eliminating the need for complex parameter configurations, which are required by pgvector.
AI Framework Integration
MariaDB’s native vector search capabilities allow it to act as a powerful and efficient backend for leading AI frameworks, simplifying the development of generative AI applications by unifying your data stack
Key Framework Integrations
LangChain
Build sophisticated chatbots and agents using MariaDB for both Vector Store similarity search using MariaDBStore and persistent ChatMessageHistory.
Spring AI
Easily add vector search to enterprise Java applications with the MariaDBVectorStore implementation, providing a familiar data layer for Spring developers.
LangChain.js
Integrate MariaDB as a vector store in JavaScript and TypeScript RAG applications using the MariaDBStore for efficient similarity search, enabling scalable, full-stack LLM workflows grounded in your data.
LlamaIndex
Use MariaDB as a native Vector Store to power Retrieval-Augmented Generation (RAG) pipelines, grounding LLMs in your data for more accurate and context-aware responses.
Learn how to use these AI frameworks with MariaDB’s vector store capabilities
MariaDB MCP Server
The MariaDB MCP Server enables seamless integration between MariaDB databases and AI-driven applications, thanks to its support for the Model Context Protocol (MCP). It provides both traditional SQL operations and modern vector-based semantic search, unlocking the power of embeddings from providers like OpenAI and HuggingFace. Ideal for building RAG systems, semantic search, or recommendation engines—directly on your existing MariaDB stack.
Understanding the Model Context Protocol (MCP)
The Model Context Protocol (MCP) provides a standardized way for language models and other AI systems to interact with external tools and data sources. By adhering to this protocol, the MCP Server ensures a consistent and reliable method for AI assistants and applications to request information and perform operations on your MariaDB databases, streamlining the development and deployment of AI-integrated systems.
Why the MCP MariaDB Server
The MariaDB MCP Server is engineered to provide a robust MCP interface specifically for MariaDB. Its primary objective is to facilitate seamless interaction between AI models and MariaDB databases, supporting both standard relational data operations and the increasingly vital vector search capabilities required for modern AI applications. Designed with AI assistants in mind, it simplifies data workflows and enhances the ability to integrate database interactions into intelligent systems.