SQL Mocker

SQL Mocker is a web app that creates SQL from natural-language questions using a reviewed copy of database schema metadata. Users can provide a schema by uploading a file, pasting metadata, building it manually, or supplying output from an extraction script run in their own database. Its workspace can detect, review, edit, or add table relationships, including links missing from the original metadata. Existing SQL can also be submitted for explanation, review, improvement, formatting, conversion, troubleshooting, or mapping its columns and joins. The app generates dummy data from a schema so users can preview results and reason about query logic without production rows. The homepage lists 17 database systems, including SQL Server, PostgreSQL, MySQL, Oracle, Snowflake, BigQuery, and MongoDB. SQL Mocker says it does not need database credentials for schema-based generation and does not connect to a live customer database. It presents generated SQL for review and copying; users run it in their own tool. The free Explore plan includes 50 AI queries total and five saved projects. Listed paid plans are marked as coming soon.

SQL Mocker helps turn schema context and plain-language questions into SQL for users to inspect and run themselves. Its free plan has a fixed total query allowance, and listed paid tiers are not yet available.

SQL Mocker is intended for analysts, consultants, and report builders preparing SQL for Power BI or database tools. It may suit users who want to work from schema metadata without connecting a live database.

SQL Mocker is a web workspace that turns natural-language questions into SQL using database schema context. It suits analysts, consultants, and report builders preparing queries for Power BI or database tools. Its strongest case is schema-aware drafting without live database access; its main constraint is that every generated query remains your responsibility to check and run.

Overview

You provide the schema rather than credentials or a live connection. SQL Mocker accepts an uploaded schema file, pasted metadata, a manually built schema, or results from an extraction script run in your own database. That makes it practical for preparing queries against controlled structural information while keeping execution in your existing database environment.

The service presents SQL for review and copying. You run it yourself in a database tool or Power BI Desktop; SQL Mocker does not connect directly to Power BI and does not execute against a customer database. Its maker warns that AI-generated SQL can be wrong, so validation and testing remain essential. Saved projects synchronize to your SQL Mocker account and are cached in your browser, with a History option for saving a backup file to your PC. The security guidance also advises checking prompts and uploads before submitting sensitive content.

Key features

Schema-led generation

Reviewed schema metadata gives natural-language requests a defined set of tables and fields to work from. The workspace can detect table relationships, then lets you review, edit, or add them, including relationships absent from the source metadata. That is valuable when joins need correction or clarification before a query is drafted.

SQL explanation and repair

You can paste or upload existing SQL for explanation, review, improvement, formatting, conversion, troubleshooting, or mapping its output columns and joins. This broadens the tool beyond first-draft generation: it can help an analyst understand inherited SQL or prepare it for a different use, though the output still needs technical verification.

Safe preview workflow

SQL Mocker generates dummy data from the schema so you can preview result shapes and test query logic without using production rows. That is a useful check before copying SQL elsewhere, but dummy data cannot confirm behavior that depends on real values, data quality, permissions, or production-specific edge cases.

Database coverage and execution boundary

The homepage names 17 supported systems: SQL Server, PostgreSQL, MySQL, Oracle, SQLite, MariaDB, Snowflake, BigQuery, Amazon Redshift, Databricks, SAP HANA, IBM Db2, Teradata, Microsoft Access, ClickHouse, DuckDB, and MongoDB. Credentials are not required for schema-based generation, and generated SQL is for inspection and copying rather than automatic execution.

Pricing

SQL Mocker uses a freemium model. Explore costs nothing for one user and includes 50 AI queries total, five saved projects, and no credit card requirement. It is a sensible way to assess the schema workflow, but the fixed lifetime-style query allowance is better for occasional evaluation than regular reporting work.

Starter is $10 a month, billed $10/month, for one user, 100 AI queries per month, and 20 saved projects. Pro is $20 a month, billed $20/month, with one user, 300 monthly queries, and 100 saved projects. Business is $39 a month, billed $39/month, for one user, 1,000 monthly queries, and custom saved projects. All three are coming soon and require joining a waitlist, so they are not immediate upgrade paths. Enterprise uses custom pricing with multiple users, custom query limits, and custom saved projects, also coming soon via waitlist. Every paid tier listed is single-user until Business or Enterprise terms expand that scope; none provides a published renewal or trial term.

Platforms

SQL Mocker is delivered as a cloud-based web application. You need another database tool or Power BI Desktop to execute the SQL it produces.

Who it's for

Choose it if you regularly translate business questions into SQL, need to inspect joins before execution, or prepare Power BI queries while keeping production access outside the service. Analysts and consultants can benefit from editable relationships and query explanations; report builders can use schema-based drafts and dummy previews. It is a poor fit if you require live querying, automatic Power BI connectivity, or unattended execution.

Pros and cons

Pros

• Works from supplied schema metadata, so database credentials and live connections are unnecessary.
• Relationship detection plus manual editing helps correct incomplete or inaccurate join context.
• Existing SQL can be explained, formatted, converted, optimized, troubleshot, and mapped by columns and joins.
• Dummy-data previews let you inspect query logic without exposing production rows.
• The free Explore plan provides 50 total AI queries with no card required.

Cons

• SQL is not executed for you; copying and validating it in your own environment is mandatory.
• AI output may be incorrect, limiting use for high-stakes work without review and testing.
• Paid plans are coming soon and waitlist-only, while Explore has a fixed 50-query total.
• Projects sync to the account and browser cache, and sensitive prompts or uploads require user caution.
• Power BI preparation is supported as a workflow, not through a direct Power BI integration.

Alternatives

For a broader starting point, see AI SQL Generators. Choose Natural Language SQL instead if you want a free, GPL-3.0 open-source engine that can run locally through self-hosted, desktop, server, or web environments.

Outerbase AI is the better direction when you need a multi-user free tier, dashboards, and saved queries, although its free plan is limited to transactional databases and 10 EZQL queries per month. Savvina AI suits teams centered on PostgreSQL and MySQL with unlimited connections on its free Community plan. Wren AI is worth considering for developers who want an open-source context engine used through CLI and MCP, including an option without a user interface. NatureQuery fits users seeking a free web/API service with 50 monthly queries, one connection, exports, and 30-day history. QueryPlane is aimed at self-hosted users who want app and database-connection management alongside AI prompts. SQLer is the alternative for substantially larger published NL2SQL quotas on its paid tiers. IDERA AI SQL Query Generator is a free web option when a simpler, limits-apply offering is sufficient.

Verdict

SQL Mocker is a good choice for analysts, consultants, and report builders who want schema-grounded SQL drafts, relationship control, and dummy-data review without granting live database access. Choose it for that deliberate prepare-and-validate workflow; look elsewhere if you need live execution, direct Power BI integration, or paid capacity you can buy today.