SQL Mocker
Summary
The moment your team's SQL assistant asks for a connection string to production, the security review stops everything — SQL Mocker exists for exactly that moment.
SQL Mocker generates SQL from natural language against a schema replica you build from table names, column names, data types, and relationships — your real database stays disconnected throughout. You upload schema metadata, the tool generates dummy data from that structure, and you test and refine queries before they ever touch live records. The workflow covers generation, explanation, review, formatting, and troubleshooting. Where it breaks: the generated SQL is only as good as the schema you describe, so incomplete or stale metadata produces plausible-looking queries that fail on the real system. Teams with complex dynamic schemas spend meaningful time maintaining the replica before they get useful output.
Bottom line: Pick this when your bottleneck is schema-safe SQL prototyping — skip it when your real problem is validating queries against live data distributions you cannot safely replicate by hand.
Pricing Plans
SubscriptionLast verified 2 weeks ago- Free Tier
- 50 AI queries total, 5 saved projects, 1 user
Explore
For exploring SQL Mocker
- 1 user
- 50 AI queries total
- 5 saved projects
- No credit card required
- Create SQL from plain English
- Upload schema or table details
- Review and edit your schema
- View schema relationships
- Automatic relationship detection
- Review and improve pre-existing SQL
Starter
For occasional SQL help
- 1 user
- 100 AI queries/month
- 20 saved projects
- SQL generation
- SQL explanation
- SQL error support
- Query refinement
- Cancel anytime
Pro
For regular SQL users
- 1 user
- 300 AI queries/month
- 100 saved projects
- Advanced SQL generation
- Explain, optimize, and troubleshoot SQL
- Schema Discovery Chat
- Pre-existing SQL review
- Cancel anytime
Business
For heavier SQL workflows
- 1 user
- 1,000 AI queries/month
- Custom saved projects
- Schema Discovery Chat
- Higher query allowance
- SQL generation and refinement
- Pre-existing SQL review
- SQL error support
- Cancel anytime
Enterprise
For teams and organisations
- Multiple users
- Custom query limits
- Custom saved projects
- Schema Discovery Chat
- Cancel anytime
View full pricing on sqlmocker.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- No database credentials are ever requested or stored, so the tool clears security reviews that block direct-connection AI tools — teams in regulated environments can use it without a separate data-access approval.
- Schema-grounded generation uses your actual table names, column names, keys, and relationships rather than generic SQL patterns, which means fewer hallucinated column references and wrong join conditions compared to prompt-only generators.
- Dummy data is generated from your schema structure, so you can inspect query logic and test prompts against realistic-looking results without exposing production rows to a third-party service.
- Existing SQL can be uploaded for explanation, review, formatting, troubleshooting, or dialect conversion, so you avoid the separate tool-switching that slows down query debugging sessions.
- The vendor states compatibility with SQL Server, PostgreSQL, Oracle, MySQL, Snowflake, BigQuery, SQLite, and other SQL-style databases, so teams are not locked to a single dialect when prototyping across environments.
Cons
Sign in to edit- Query quality is bounded entirely by the accuracy of the schema metadata you provide: missing foreign keys, outdated column names, or undocumented relationships produce syntactically valid SQL that fails or returns wrong results on the real system — and the tool has no way to flag the discrepancy. Teams with large or frequently changing schemas end up maintaining a parallel metadata snapshot as a separate task.
- The tool does not execute queries or connect to live data, so any logic errors that depend on real row counts, NULL distributions, or referential integrity in production are invisible during prototyping and surface only after you run the query in your actual database. Teams whose primary need is catching semantic correctness before deployment switch to tools that offer a sandboxed live connection — typically a managed query environment with credential vaulting — when this gap becomes the blocking problem.
- The free tier caps total queries at fifty, which covers initial exploration but runs out during iterative refinement cycles. Teams that reach that ceiling without committing to a paid subscription lose access to the workflow mid-project, with no self-hosted or local alternative the vendor describes.
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About
- Platforms
- Web
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-06-30T16:17:30.116Z
Best For
Who it's for
- Developers needing SQL help without DB exposure
- SQL prototyping and testing
- Learning or improving SQL skills
- Teams avoiding direct AI-database connections
What it does well
- Generate SQL from natural language descriptions
- Review and refine existing SQL queries
- Troubleshoot SQL errors safely
- Explore schema relationships without live DB access
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Frequently Asked Questions
- Is SQL Mocker free?
- SQL Mocker has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is SQL Mocker open source?
- No — SQL Mocker is a closed-source tool. Source code is not publicly available.
- What platforms does SQL Mocker support?
- SQL Mocker is available on: Web.
Hours Saved & ROI Stories Community
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Curated lists that include this category
SQL Mocker accepts schema metadata — table names, column names, data types, primary keys, and relationships — and generates SQL from natural-language questions against a safe replica of that structure. The vendor states the tool does not request database credentials and never establishes a live connection; users run a metadata extraction script themselves inside their own database tool and paste or upload the results. From that point, the assistant handles generation, explanation, review, improvement, formatting, conversion, and troubleshooting against the replica, while dummy data generated from the schema lets you inspect query logic without exposing real rows.
The core differentiator is deliberate disconnection. Most SQL AI tools either request a live connection or generate queries from a prompt alone with no structural grounding. SQL Mocker sits between those two positions: it uses your schema and relationships to anchor generation, which reduces hallucinated column names and wrong joins, but it does so without storing credentials or touching production. Projects are saved locally in the browser, and the vendor offers a backup file option — no server-side storage of your schema metadata is described.
The tool fits developers and analysts who need to prototype SQL before a database access request clears, Power BI users who want to shape a dataset before connecting a report, and teams in regulated environments where direct AI-to-database connections are blocked by policy. It does not execute queries or validate results against live data, which means any logic that depends on actual row counts, distributions, or referential integrity in production will not surface as an error inside the tool — it surfaces later, in your database. The free tier caps queries at fifty, which is enough for a focused prototyping session but not for a sustained daily workflow without a paid subscription.