DataDocs vs tbls
DataDocs
datadocs.aitbls
github.comSide by side
| Figure | DataDocs | tbls |
|---|---|---|
| Price from | $25/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 1 |
| Spec score · Database Documentation Tools | 8.1 (#10) | 8.2 (#9) |
| Purpose | DataDocs uses AI to generate database documentation and keep it updated as the database changes. | tbls is a CI-friendly tool written in Go for documenting databases. |
| Supported databases | The product page says DataDocs currently supports PostgreSQL and MySQL, with SQL Server and Snowflake listed as coming soon. | Documented data sources include PostgreSQL, MySQL, MariaDB, SQLite, BigQuery, Cloud Spanner, Redshift, SQL Server, DynamoDB, Snowflake, MongoDB, ClickHouse and Databricks. |
Plans
In their own words
DataDocs
DataDocs is a cloud database documentation tool that uses AI to generate documentation and keep it aligned with schema changes. It currently supports PostgreSQL and MySQL; SQL Server and Snowflake are listed as coming soon. One-click generation creates documentation, while search covers the documentation and data catalog. DataDocs scans for schema changes and reflects them in the documentation. If an asset’s documentation is wrong, users can regenerate it and provide feedback; manual editing is listed as planned. The service requests read-only metadata access and says it does not access or store the database’s actual data. Its web application and extraction service run in separate VPCs, with credentials stored in a restricted vault. DataDocs is aimed at small businesses, freelancers, and agencies, and is positioned as documentation software rather than data governance. The Free plan is free for one source, 25 assets, and weekly refresh. Growth costs $25 a month; Premium costs $100 a month.
tbls
tbls is a command-line tool for documenting databases and checking their schemas. It generates GitHub Flavored Markdown and can export schema information in formats including DOT, PlantUML, Mermaid, SVG, PNG, JPG, JSON, YAML, Excel, and configuration files. Its configurable lint rules can require column comments or set column-count limits. The `diff` command compares a database schema with generated documentation or another database, while `coverage` measures documentation for descriptions and comments. Documented data sources include PostgreSQL, MySQL, MariaDB, SQLite, BigQuery, Cloud Spanner, Amazon Redshift, SQL Server, Azure SQL, Microsoft Fabric Warehouse, DynamoDB, Snowflake, MongoDB, ClickHouse, Databricks, JSON, HTTP, and GitHub. Docker and a GitHub Action are documented for CI use, and external drivers can extend database support. It runs on Linux, macOS, and Windows, or in a self-hosted setup. The listed plan is free under the MIT License. SQL Server requires version 2017 or later and database compatibility level 110 or higher.