Kiwi Fabric vs MindsDB Query Engine
Kiwi Fabric
kiwifylabs.comMindsDB Query Engine
github.comSide by side
| Figure | Kiwi Fabric | MindsDB Query Engine |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Federation Software | 7.5 (#15) | 8.1 (#7) |
| Federated queries | The engine queries sources in parallel, pushes supported filters and projections to each source, and joins or aggregates results in memory. | It queries connected sources live through one SQL dialect without copying the data or using ETL pipelines. |
| Founded | 2025 | 2018 |
| Headquarters | Bengaluru, India | Berkeley, California, United States |
| Integrations | The product page lists relational sources including PostgreSQL, SQL Server, MySQL, Oracle, SQLite, and others, plus MongoDB, Couchbase, Cassandra, Redis, REST APIs, and SOAP services. | The README names databases, warehouses, applications, and files as source categories and gives PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery, S3, and Slack as examples. |
| Purpose | Kiwi Fabric provides cross-database SQL federation and distributed-write orchestration across relational, NoSQL, REST, and SOAP sources. | MindsDB Query Engine provides semantic search over data through SQL and connects to more than 200 data sources. |
| Support | Kiwify Labs offers architecture and engineering teams help with use-case evaluation, proof-of-concept work, integration design, implementation, and production adoption. | The project points users to Discord for questions, GitHub Issues for bugs, and team contact for commercial support. |
Plans
In their own words
Kiwi Fabric
Kiwi Fabric has no write-up on Specifiction yet; its figures are above.
MindsDB Query Engine
MindsDB Query Engine provides SQL access to connected data sources and semantic search, including for AI agents that need live, queryable data. It connects to more than 200 sources and runs federated queries against them in place, using one SQL dialect without copying data or using ETL pipelines. Listed source examples include PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery, S3, and Slack. Users can work in an HTTP SQL editor or connect clients such as DBeaver and SQLAlchemy through MySQL and PostgreSQL wire protocols. For unstructured content, knowledge bases pair an embedding model with a vector store and, optionally, a reranking model. Hybrid search combines vector similarity with BM25 keyword matching. Projects organize work, views preserve cross-source transformations, and scheduled jobs run SQL at intervals. Deployment options include Docker and PyPI installation. The engine is available for Linux, web, API, and self-hosted use. The free plan is listed at free; MindsDB Core uses the Elastic License 2.0, and some repository directories have separate licenses.