The AI Database Landscape in 2026 - Four Architecturally Distinct Approaches

A comprehensive survey examining how AI capabilities are being integrated into the database layer. The analysis identifies four distinct architectural categories: vector databases (for embedding similarity search), ML-in-database systems (training and prediction directly in SQL), LLM-augmented databases (routing queries to LLMs as needed), and predictive databases (using Bayesian inference at query time without traditional model lifecycles). The post includes detailed inference mechanics for each approach, architecture diagrams, comparison tables, and explores gaps in the taxonomy including feature engineering considerations.