MindsDB
AI layer for databases — train and query ML models with plain SQL.
About
MindsDB brings machine learning directly into the data layer: developers and analysts can train, deploy, and query ML models using standard SQL, without moving data into a separate ML platform. Models appear as virtual AI-Tables that can be joined with regular tables, so a single SELECT can fetch customer records and join them against a churn-prediction model or a fine-tuned LLM.
It connects to PostgreSQL, MySQL, Snowflake, BigQuery, MongoDB, MariaDB, and 70+ data sources and ML frameworks, covering classification, regression, time-series forecasting, and LLM integrations, plus autonomous agents that react to database events. MindsDB has repositioned toward agentic analytics in 2026. The honest caveat: pushing heavy ML into the database layer trades one kind of complexity for another — governance, cost control, and model versioning still need deliberate design, and teams should evaluate whether in-database inference fits their scale.
What it can do
- Train and query ML models with standard SQL
- AI-Tables: models queryable as virtual tables
- 70+ data-source and ML-framework integrations
- Time-series forecasting, classification, regression
- LLM integrations and database-event-driven AI agents
Setup
Self-host via Docker (mindsdb/mindsdb) or use MindsDB Cloud; connect data sources then CREATE MODEL with SQL.
Prerequisites
A supported database and basic SQL knowledge.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 77; Trending score 80/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.