MindsDB Query Engine

MindsDB Query Engine is a self-hosted SQL layer that reaches live data across more than 200 sources and adds semantic search for unstructured content. It suits teams building AI-agent data access or consolidating queries across databases, warehouses, applications, and files. Its federated access and search combination is compelling, but commercial adopters should examine its licensing before making it a standard platform.

Overview

MindsDB sends queries to connected systems in place through one SQL dialect rather than copying records into a separate store or requiring ETL pipelines. That can reduce duplicate data and keep results close to current source data, while still giving a distributed organisation one query surface. The underlying databases and services remain your responsibility to secure, govern, and keep available.

The project began in Berkeley in 2018, when Jorge Torres and Adam Carrigan released the first MindsDB version. It is presented as a standalone engine that gives AI agents live, SQL-queryable information alongside search over unstructured material.

Key features

Federated SQL and clients

An HTTP SQL editor provides a built-in interface. MySQL and PostgreSQL wire-protocol compatibility lets tools such as DBeaver and SQLAlchemy connect, preserving familiar SQL workflows instead of forcing a proprietary client. Named integrations include PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery, S3, and Slack, spanning databases, warehouses, files, and applications.

Semantic and hybrid search

Knowledge bases combine an embedding model, an optional reranker, and a vector store to index and retrieve unstructured content. Hybrid search runs vector similarity and BM25 keyword matching together, then merges their results. This is better suited to natural-language discovery where exact terms and conceptual similarity both matter than a SQL-only federation layer.

Projects, views, and jobs

Projects namespace work, views preserve transformations across sources, and scheduled jobs run SQL at intervals. Together they turn one-off federation into repeatable workflows, although teams still need to design schedules and monitor the source systems those jobs depend on.

Deployment and data handling

Docker and PyPI installation support either a containerised or Python-based deployment. The project states that connected data is queried in place and never copied. Vulnerability reports go through its security policy rather than public issues, while questions, bug reports, and commercial assistance are directed to Discord, GitHub Issues, and team contact respectively.

Pricing

Open-source Query Engine — free. This standalone open-source project has no subscription charge and supports self-hosting, SQL querying, API access, and scheduled queries. MindsDB Core uses the Elastic License 2.0, and individual directories may carry their own licenses, so legal review is important for redistribution or commercial deployment. There are no published seat, query, or usage quotas in this plan.

Platforms

MindsDB runs through an API, on Linux, as a self-hosted deployment, and via the web interface. Docker and PyPI installation give operators two documented ways to run it.

Who it's for

Choose MindsDB when an engineering or data team needs one SQL entry point over many live systems, particularly when AI agents must combine structured answers with semantic retrieval. It is also a sensible fit for organisations that want to keep source data in place and operate the engine themselves. It is less suitable for buyers seeking a managed commercial service, a single uniform license across every component, or a platform that removes responsibility for source-system operations.

Pros and cons

• Pro: Live federated queries across more than 200 sources avoid a mandatory central ETL copy.
• Pro: MySQL and PostgreSQL protocols, plus an HTTP editor, accommodate existing SQL clients.
• Pro: Knowledge bases and combined vector/BM25 search cover both semantic and keyword retrieval.
• Pro: Projects, reusable views, and scheduled SQL support repeatable cross-source work.
• Con: Elastic License 2.0 for Core and varying directory licenses require careful commercial assessment.
• Con: Self-hosting leaves deployment, security, and availability of connected systems with your team.

Alternatives

For a wider comparison, see Data Federation Software.

• Denodo Platform is a paid, self-hosted-capable option with a free Developer plan covering one server, four cores, 50 data products, and 2.5 TB per year.
• Stardog suits teams wanting a freemium platform; its free license renews annually and allows up to 1 million stored edges and three databases.
• Apache Drill is a free Apache-licensed engine for downloadable embedded or cluster deployments.
• Presto is a free open-source SQL engine designed for on-premises or cloud operation.
• QoreDB offers a free Core tier combining SQL and NoSQL engines with editors and a data grid.
• Teradata VantageCloud is a freemium commercial platform with a free trial and custom-priced Standard Lake plan.
• openLooKeng is a free Apache-licensed, self-hosted choice.
• Starburst Galaxy provides a free tier with up to three clusters and ad hoc query execution, plus paid expansion.

Verdict

MindsDB Query Engine is a strong choice for technical teams that need live, cross-system SQL and semantic search in a self-hosted engine, especially for AI-agent workloads. Its main reason to choose it is the combination of federation, hybrid retrieval, and scheduled SQL without copying source data. Look elsewhere if licensing variation or operating the infrastructure yourself is unacceptable.