Which databases does it support?

It targets the major dialects — PostgreSQL, MySQL, SQLite, SQL Server (T-SQL), and Oracle PL/SQL. Standard SQL works across most of them, but functions like date_trunc versus DATE_FORMAT differ, so always tell the model which database you're using.

How accurate are the queries?

Common patterns (SELECT with WHERE, JOINs, GROUP BY, simple subqueries) come out correct most of the time. More involved queries — recursive CTEs, advanced window functions, multi-level subqueries — usually take a couple of iterations. Always test against sample data before relying on the output.

Can it understand my schema?

It can if you provide it. Without schema details, the model guesses based on table and column names you mention, which is hit or miss. For accurate JOINs and correct field references, paste CREATE TABLE statements alongside your question.

What about query performance?

The model produces queries that work but aren't always efficient. It doesn't know your indexes, table sizes, or query patterns, so it may pick suboptimal joins or miss obvious index opportunities. For anything heading to production, run EXPLAIN ANALYZE and verify the query plan before deploying.

Will it generate dangerous queries?

Occasionally. It has been known to produce UPDATE or DELETE statements without WHERE clauses, JOINs that turn into cartesian products, or operations that would lock large tables. Review every generated query carefully, and be especially cautious with anything that mutates data.

Can I describe the desired output format?

Yes. Phrasings like 'sales by month for 2024 as a pivot table' or 'top 10 customers by revenue with their order count' steer the structure of the result. The model attempts to match what you asked for — but verify the columns and aggregation match your expectation.

Is the data sent to a server?

Yes — generation requires an LLM API call. Don't paste real production data, passwords, or personally identifiable information into your prompts. Schema definitions and anonymized examples are fine.

AI SQL Query Generator

Server-powered

Describe your data query in plain English and get SQL code instantly with AI. Free online text-to-SQL generator for any database.

AI-Powered Generators Developer Tools

Instant results

Describe what data you want

Describe your query in plain English. Be specific about filters, sorting, and grouping. Press Ctrl+Enter to generate.

SQL Dialect

Try an example

How It Works

Describe Your Query

Write what data you need in plain English. No SQL knowledge required. You can optionally include your table schema for more accurate results.

Choose Your Dialect

Select the SQL dialect that matches your database. The generator will use dialect-specific syntax, functions, and best practices.

Get Your SQL Query

Receive a properly formatted SQL query along with an explanation of what it does. Copy it and use it directly in your database client.

SQL Dialects Supported

Standard SQL

ANSI SQL compatible with most databases

MySQL

World's most popular open-source database

PostgreSQL

Advanced open-source relational database

SQLite

Lightweight embedded database engine

SQL Server

Microsoft's enterprise database system

Oracle

Enterprise-grade relational database

Example Queries

Prompt: Show me all customers who haven't placed an order in the last 6 months

SELECT c.id, c.name, c.email, MAX(o.created_at) AS last_order
FROM customers c
LEFT JOIN orders o ON c.id = o.customer_id
GROUP BY c.id, c.name, c.email
HAVING MAX(o.created_at) < NOW() - INTERVAL '6 months'
   OR MAX(o.created_at) IS NULL
ORDER BY last_order ASC;

Prompt: Get monthly revenue totals for this year, compared to last year

SELECT
  EXTRACT(MONTH FROM created_at) AS month,
  SUM(CASE WHEN EXTRACT(YEAR FROM created_at) = EXTRACT(YEAR FROM NOW())
    THEN total ELSE 0 END) AS current_year,
  SUM(CASE WHEN EXTRACT(YEAR FROM created_at) = EXTRACT(YEAR FROM NOW()) - 1
    THEN total ELSE 0 END) AS previous_year
FROM orders
WHERE created_at >= DATE_TRUNC('year', NOW()) - INTERVAL '1 year'
GROUP BY month
ORDER BY month;

Prompt: Find duplicate email addresses across all users

SELECT email, COUNT(*) AS occurrences
FROM users
GROUP BY email
HAVING COUNT(*) > 1
ORDER BY occurrences DESC;

How to Use AI SQL Query Generator

Describe your query

Write what you want in plain English, something like 'total sales by region for last quarter, sorted descending'. Be specific about columns and ordering when it matters.

Provide schema (optional)

Paste CREATE TABLE statements when you can. Without schema context, the model guesses based on the table and column names you mention, which is less reliable.

Specify dialect

Tell the model which database you're targeting — PostgreSQL, MySQL, SQL Server, Oracle, SQLite. This affects date functions, string operations, and dialect-specific features like window function support.

Verify before running

Read the generated SQL closely. Check the WHERE clauses, the joins, and the aggregations. Test on a development database before pointing it at production, and be especially careful with anything that updates or deletes data.

When to Use AI SQL Query Generator

Non-SQL experts getting data

Marketing folks, sales leads, and product managers often need data but never learned SQL. You describe what you want in plain English, and the tool produces a query you can run yourself (or hand off to the data team). It removes the bottleneck of waiting on an engineer for every ad-hoc question.

Learning SQL by example

Type a question in English, see the SQL the model would write, then compare it to whatever you would have written. Going the other direction works too — paste a query someone else wrote and ask for an explanation. It's a useful companion for coursework, self-study, and getting comfortable with patterns like window functions or correlated subqueries.

Quick prototyping

Building a prototype and just need the data? Describe what you want, the tool generates the SQL, you run it. It's much faster than hand-writing complex JOINs and works well for hackathon projects, internal dashboards, and ad-hoc analysis where you don't need production-grade query tuning.

Cross-database adaptation

If you wrote a query for PostgreSQL but now need it on MySQL, the model can translate dialect-specific syntax (date functions, string handling, window function support). This is handy during migrations or when a single codebase supports multiple database backends.

AI SQL Query Generator Examples

Sales aggregation

Input

Total sales by region for 2024, sorted high to low

Output

SELECT region, SUM(amount) as total FROM sales WHERE YEAR(sale_date) = 2024 GROUP BY region ORDER BY total DESC

A standard aggregation query. The model infers a schema with a sales table containing region, amount, and sale_date columns. It also adapts to your dialect — YEAR() in MySQL, EXTRACT in PostgreSQL.

Top customers

Input

Top 10 customers by lifetime value with their last order date

Output

SELECT c.name, SUM(o.total) as ltv, MAX(o.order_date) as last_order FROM customers c JOIN orders o ON c.id = o.customer_id GROUP BY c.name ORDER BY ltv DESC LIMIT 10

A common business question rolled into one query — customers, lifetime value, recency. The JOIN, aggregation, ORDER BY, and LIMIT all come out reasonable on the first try.

Schema-aware

Input

Show me all employees in engineering with salary > 100k\n[Schema: employees(id, name, dept, salary)]

Output

SELECT name, salary FROM employees WHERE dept = 'engineering' AND salary > 100000

When you include the schema, the model uses your actual column names rather than guessing whether the column is called 'dept' or 'department'. A small amount of schema context dramatically improves accuracy.

Tips & Best Practices for AI SQL Query Generator

1.Always test generated SQL against development data before pointing it at production. A query that runs in milliseconds on a 100-row table can crawl on a 100-million-row one.
2.Be wary of destructive operations. The model has been known to produce UPDATE or DELETE statements without a WHERE clause, which is catastrophic in a real database.
3.Provide schema information whenever you can. Pasting CREATE TABLE statements alongside your question produces dramatically better SQL than the question alone.
4.Run EXPLAIN on generated queries before relying on them. Check that indexes are being used, large tables aren't being fully scanned, and JOINs aren't accidentally cartesian.
5.For complex queries, expect to iterate. The first attempt is rarely perfect — refine the description, regenerate, and retest until the output is right.
6.Keep a notebook of queries that worked. Patterns repeat, and saved generations make excellent starting templates for similar future questions.

Frequently Asked Questions

It translates natural language descriptions into SQL queries. You describe the data you want and the tool produces a query, or paste an existing query and get a plain-English explanation back. People reach for it when they're not strong in SQL, when they're learning, or when a complex query (lots of JOINs, window functions, nested aggregates) would otherwise take a while to write.

AI SQL Query Generator

Try an example

How It Works

Describe Your Query

Choose Your Dialect

Get Your SQL Query

SQL Dialects Supported

Example Queries

How to Use AI SQL Query Generator

Describe your query

Provide schema (optional)

Specify dialect

Verify before running

When to Use AI SQL Query Generator

Non-SQL experts getting data

Learning SQL by example

Quick prototyping

Cross-database adaptation

AI SQL Query Generator Examples

Sales aggregation

Top customers

Schema-aware

Tips & Best Practices for AI SQL Query Generator

Frequently Asked Questions

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