Stop complexity and cost. Learn how to embed high-performance vector search directly into your relational database for faster AI deployment.

Implementing vector search, the core engine of modern AI, often means introducing a separate database, creating architectural sprawl, and increasing operational burden.

Download the "A Practical Guide to AI and Vector Search in Relational Databases" (2025 Edition) to discover a unified solution. This e-book outlines how relational databases, like the MariaDB Enterprise Platform, now natively support vector search, allowing you to run structured SQL and semantic similarity queries in a single platform.

Download the guide to learn:

• Hybrid Query Power: How to combine traditional SQL filtering with vector similarity for powerful, contextual applications, such as e-commerce recommendations and knowledge retrieval.
• Integrated Architecture Advantage: Why built-in vector support offers better performance, less latency, and lower complexity than managing external or bolt-on vector systems.
• The Path to Production: Strategic steps for leveraging your existing SQL expertise and infrastructure to accelerate your AI strategy and reduce database sprawl.

The future of AI is integrated. Get the guide and simplify your data architecture.