Author here. I wrote this because "AI database" keeps getting used as one category when there are at least four architecturally different approaches, each with a different inference mechanism. Vector DBs do embedding similarity, ML-in-database is train-then-predict in SQL, LLM-augmented routes to an LLM per query, and predictive databases do Bayesian inference at query time with no model lifecycle.
Full disclosure: I'm the co-founder of Aito, which falls in the predictive database category. I tried to cover all four fairly, including where our approach falls short (latency scales with dataset size, smallest ecosystem of the four). Happy to discuss any of the categories or the taxonomy itself.
One thing that stood out while writing: the major cloud platforms (Databricks, BigQuery, Snowflake) now span multiple categories at once, which makes "AI database" as a single label pretty useless. Also, the BayesLite work from MIT CSAIL deserves way more attention than it gets. Their BQL syntax is really nice.
