AI-native databases

An AI-native database is a purpose-built data system: it natively supports high-dimensional vector embeddings and executes real-time semantic similarity search, powering agentic AI applications.

This architecture shifts data management from traditional relational models to a platform optimized for AI/ML workflows. Unlike legacy systems focused on structured data and exact matches, AI-native databases prioritize unstructured data (text, images) by storing it as vector embeddings (numerical representations). They employ specialized indexing algorithms (e.g., Approximate Nearest Neighbor or ANN) to perform fast, context-aware retrieval across billions of vectors. This capability is critical for modern applications: Retrieval-Augmented Generation (RAG), recommendation engines, and fraud detection. Key players like Weaviate and Milvus deliver this unified, scalable infrastructure, eliminating complex ETL pipelines between traditional databases and AI tools.

https://weaviate.io/