The platform for building AI from enterprise data using SQL and virtual AI Tables.
MindsDB is an open-source platform that enables developers to build, train, and deploy machine learning models directly within their existing data infrastructure using SQL. By abstracting machine learning models as 'AI Tables,' MindsDB allows for seamless integration of predictive analytics and generative AI into applications without moving data to separate ML environments. In the 2026 landscape, MindsDB serves as a critical orchestration layer between 100+ data sources (PostgreSQL, Snowflake, MongoDB) and AI frameworks (OpenAI, Hugging Face, Anthropic). Its technical architecture leverages an 'AI Logic Engine' that automates data preprocessing, feature engineering, and model selection. This 'AI-in-the-Database' approach significantly reduces the latency typically associated with inference pipelines and simplifies the maintenance of production-grade AI systems. For enterprise users, MindsDB provides a managed cloud environment that handles scaling, security, and real-time data streaming via Kafka or Kinesis, positioning it as the industry standard for data-centric AI development.
Virtual tables that represent a trained ML model, allowing standard SELECT JOIN queries to trigger inference.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Automatic detection of data types, normalization, and encoding for diverse datasets during training.
Native SQL integration with OpenAI, Llama 3, and Anthropic for prompt management and fine-tuning.
Optimized algorithms for handling multivariate time-series forecasting within the database.
Integration with Kafka and Redpanda to perform inference on data streams in real-time.
Provides metadata on feature importance and confidence scores for every prediction.
Ability to toggle between XGBoost, LightGBM, PyTorch, and various LLMs using a single interface.
Identifying users likely to unsubscribe based on usage patterns stored in PostgreSQL.
Registry Updated:2/7/2026
Predicting stock needs for 10,000+ SKUs across different regions.
Processing thousands of customer reviews daily to categorize sentiment.