Why AI-Native Databases Are Replacing Traditional Vector Stores For the past year, 'Vector Search' has been the buzzword of the AI engineering world. But as we move from RAG (Retrieval-Augmented Generation) prototypes to production systems, we are hitting a ceiling with traditional bolt-on vector extensions. The Problem with Retrofitting Adding vector search to an existing relational database (like Postgres/pgvector) is great for starting out. However, as your data scale hits millions of embeddings, the performance of Approximate Nearest Neighbor (ANN) search starts to degrade when combined with complex filtering and relational joins. Enter the AI-Native Database AI-native databases (like Pinecone, Weaviate, or Qdrant) are built from the ground up for high-dimensional data. They handle the storage, indexing, and retrieval pipeline as a first-class citizen. Key Advantages: Dynamic Metadata Filtering: Efficiently filtering by time, user ID, or category before running vector similarity search.…