Powering the Future of AI with Integrated Vector Search
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, eliminating the need for separate vector databases and streamlining your data infrastructure. This integration unlocks powerful new querying capabilities, allowing you to build sophisticated AI-driven applications that 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 Efficient Vector Embedding Management
Streamline your data infrastructure by eliminating the need for separate vector databases. With MariaDB’s native support for vector search, you can leverage your existing setup, reducing complexity and operational overhead. This streamlined approach allows you to manage both traditional relational data and vector embeddings within a single, unified database environment. No more data silos or complex synchronization processes – MariaDB simplifies vector embedding management.
Enhanced Performance for Demanding Vector Search Workloads
Deliver high-performance vector search capabilities directly within MariaDB’s trusted database environment. Experience blazing-fast similarity searches with QPS (queries per second) and concurrent data retrieval for your AI and machine learning applications, all without the need for specialized external databases. This ensures optimal performance and seamless integration with your existing data infrastructure.
Seamless Integration of Vector Search with Existing Data
Integrate vector search capabilities with your existing workloads, unlocking powerful new querying possibilities. Combine the strengths of both approaches to perform complex searches that leverage both relational and vector data. This allows you to uncover deeper insights and build more sophisticated AI-driven applications. For example, imagine a product catalog where each item has both structured data (price, category, inventory level) and a vector representation of the product’s visual features or description. With a single SQL query, you can now find similar products that are in stock and within a specific price range. This allows you to uncover deeper insights and build more sophisticated AI-driven applications.
Cost-Effective Vector Search with MariaDB
MariaDB’s native vector search capabilities offer a cost-effective solution for organizations looking to leverage the power of AI. By utilizing your existing infrastructure, you can avoid the added costs and complexities associated with managing separate vector databases. This streamlined approach reduces operational overhead and maximizes your database infrastructure investment. This also simplifies database administration: with one database, you have a single, unified system for authentication, authorization, security management, backups, and other administrative tasks.
What is MariaDB’s Embedded Vector Search?
MariaDB’s native embedded vector search functionality brings vector similarity search directly into the core database engine, eliminating the need for external vector databases and simplifying the development of AI-driven applications. This integration allows you to store, index, and query vector embeddings alongside your traditional relational data, all within a unified environment.
MariaDB vs. pgvector
Vector Search Performance Benchmark
In the realm of vector search, performance and efficiency are paramount. To provide a clear understanding of MariaDB’s capabilities, 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.
Recent benchmarks conducted by Small Datum LLC provide a detailed comparison between MariaDB and pgvector, a prominent vector similarity search extension for PostgreSQL. The results showcase MariaDB’s exceptional performance and ease of use:
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.
