Free SQL Query Generator | Plain English to SQL Code

Free AI SQL Query Generator

Describe what you want from your database in plain English and get working SQL back. Paste your table structure and the query comes back using your real column names.

Free, no account, and nothing connects to your database.

What This Tool Does

You type a question. You get SQL.

"Show me every customer who placed an order in the last 30 days but has not placed one in the previous 30, with their email and total spend."

That sentence becomes a query with a join, two date filters, an aggregate and a subquery or a NOT EXISTS clause, depending on how the generator reads it. You copy it, check it, and run it.

The tool covers the statements people actually need day to day:

• SELECT with filtering, sorting and limiting
• JOIN across multiple tables, including left joins and self joins
• Aggregation with GROUP BY, HAVING and the usual functions
• Window functions for running totals, rankings and period comparisons
• Subqueries and CTEs for anything multi step
• INSERT, UPDATE and DELETE when you need to change data
• DDL such as CREATE TABLE, ALTER TABLE and index creation
• Explanation of a query you already have, when you tick the explain box

It is not a database client. It does not connect to anything, it does not run the query, and it never sees your data. It writes the statement and you decide what to do with it.

How to Generate a SQL Query (5 Fields)

[Developer note: the field JSON did not come through for this tool, so this section is written against the recommended field set at the top of this document. If your build differs, edit this section and the feature table to match. Everything else on the page holds regardless.]

Step 1: Describe What You Want (required)

Plain English. The more specific you are about the shape of the answer, the closer the first attempt lands.

Weak input: "top customers"

Better input: "the 10 customers with the highest total order value in the last 90 days, showing customer name, email and total spent, highest first"

The second version specifies the number of rows, the metric, the time window, the columns you want back and the sort order. The first leaves all five to guesswork.

Things worth stating explicitly:

• The time window. "Last month" is ambiguous. Do you mean the previous calendar month, or the last 30 days? Say which
• How to handle missing data. Should customers with no orders appear with a zero, or be excluded? That is the difference between a left join and an inner join, and it changes your answer
• Duplicates. If a customer can appear twice, say whether you want them collapsed
• The columns you want back. Otherwise you get whatever the generator considers reasonable
• Sort order and row limits. "Top" implies a sort, but not which direction or how many

Step 2: Database (optional, but set it)

MySQL, PostgreSQL, SQL Server, Oracle, SQLite, BigQuery, Snowflake or MariaDB.

This is not cosmetic. Row limiting, string functions, date arithmetic and identifier quoting all differ between engines, and a query written for one will often throw a syntax error on another. The next section but one covers the specifics.

Step 3: Table Schema (optional, and the most important field on the page)

Paste your table structure. Either full CREATE TABLE statements or something as simple as:

customers: id, name, email, created_at, country
orders: id, customer_id, total, status, created_at
order_items: id, order_id, product_id, quantity, unit_price

That is enough. It does not need to be complete, it does not need to be pretty, and it does not need to include every table in your database. Just the tables the question touches.

Leaving this empty is the single biggest cause of a query that does not run. Full explanation in the next section.

Step 4: Query Type (optional)

Auto detect works for most requests. Set it explicitly when you want to be certain:

• SELECT only is a useful guard rail. It guarantees the output cannot modify anything, which matters if you are working near production
• INSERT, UPDATE, DELETE when you genuinely need to change data
• Schema for CREATE TABLE, ALTER TABLE and index statements
• Analytics biases the output towards GROUP BY and window functions rather than row level results

Step 5: Explain the Query (optional checkbox)

Tick this and you get a plain English breakdown alongside the SQL: what each join does, what each filter excludes, and why the query is structured the way it is.

Worth ticking every time if you are learning SQL, and worth ticking even if you are not, because reading the explanation is the fastest way to catch a query that answers a slightly different question from the one you asked.

Why Pasting Your Schema Changes Everything

This is the part every other tool in this category glosses over, and it is the reason people conclude that AI generated SQL does not work.

Without your schema, the generator is guessing your column names.

It knows SQL grammar perfectly. It has no idea whether your customer email column is called email, email_address, contact_email, user_email or primary_email. It does not know whether your orders table stores a total, a total_amount, an order_value or a grand_total. It does not know whether your timestamps are called created_at, date_created, createdOn or ins_dt.

So it picks the most common convention and writes a query that is syntactically flawless and references columns that do not exist in your database. You run it, you get Unknown column 'customer_email' in 'field list', and you decide the tool is useless.

It was not useless. It was blind.

Paste the schema and the same request produces a query that runs first time, because now the generator is working from your actual table and column names rather than an educated guess about what they might be called.

What to paste, in order of usefulness:

1. Table and column names. The bare minimum, and it fixes the majority of failures
2. Data types. Helps with date handling, numeric casting and string comparisons
3. Primary and foreign keys. This is what lets the generator work out how tables join. Without it, join conditions are inferred from naming patterns
4. A note about anything unusual. Soft deletes, status flags, a deleted_at column that should always be filtered, a currency column that is stored in minor units

On privacy. You are pasting structure, not data. Column names and table names, not rows. If your schema itself is sensitive, rename the tables before pasting, generate the query, then rename them back in the output. Nothing here connects to your database and nothing here reads your records.

SQL Dialects: The Same Question, Written Six Ways

SQL is a standard in roughly the way English is a standard. The core is shared and the details differ enough to break things.

Cloud warehouses add their own differences. BigQuery uses backtick quoted, dot separated project.dataset.table names and has its own approach to arrays and structs. Snowflake is case insensitive for unquoted identifiers but case sensitive once you quote them, which surprises people regularly.

The practical takeaway: always set the dialect. A query that fails to compile makes the tool look broken even when the logic underneath it is exactly right, and dialect mismatch is the second most common cause of failure after missing schema.

COALESCE and standard JOIN syntax work almost everywhere, so when you need something portable, stick to the shared subset.

Example Prompts and What They Should Produce

Notice that the hardest ones for a person to write, window functions and multi step aggregations, are the ones where a generator saves the most time. The easy ones you could write yourself in the time it takes to describe them.

Before You Run It: The Safety Checklist

This section exists because no other page in this category has it, and because the failure mode is genuinely serious.

A generated UPDATE or DELETE with a missing or wrong WHERE clause will modify or delete every row in the table. There is no undo. On a production database without a recent backup, that is a very bad afternoon.

So, in order, every time you are dealing with a statement that changes data:

1. Read the WHERE clause first. Before anything else. If there is no WHERE clause on an UPDATE or DELETE, stop and work out whether that is genuinely what you intended
2. Convert it to a SELECT first. Take the same FROM and WHERE, run it as SELECT *, and look at exactly which rows come back. Those are the rows that are about to change. If the count surprises you, the query is wrong
3. Wrap it in a transaction if your engine supports one. BEGIN, run the statement, check the affected row count, then COMMIT or ROLLBACK. This is the difference between a mistake and an incident
4. Run it against a copy or a staging database first, if one exists
5. Check your backups exist before running anything destructive against production. Not "we have backups configured", but "a restorable backup exists from a known recent time"
6. Never paste a generated statement into a production console while distracted, in a hurry, or at the end of a long day. The classic incident report always includes at least one of those three

For SELECT statements, the risk is much lower but not zero. A query with an accidental cartesian join across two large tables can consume serious resources on a shared database. Add a LIMIT while you are testing.

On credentials and permissions. If you regularly query production for analysis, ask for a read only account. It removes an entire category of accident permanently, and any sensible database administrator will agree to it immediately.

This tool never connects to your database, never runs anything and never sees your data. Everything above is about what happens after you copy the query out, which is entirely in your hands.

How to Tell If a Generated Query Is Actually Right

Syntactically valid and logically correct are different things. A query can run perfectly and return the wrong answer, which is worse than an error, because an error tells you something is broken and a wrong number does not.

Checks worth doing:

• Does the row count make sense? If you expected roughly a hundred customers and got fourteen thousand, you probably have a join that is multiplying rows. This is the most common silent bug in generated SQL
• Check the joins. A one to many join fans rows out. If you join orders to order items and then sum the order total, every order total gets counted once per item. Aggregate before joining, or use a subquery
• Check NULL handling. NULL does not equal anything, including itself. A WHERE status != 'cancelled' filter silently excludes rows where status is NULL. If that is not what you wanted, you need OR status IS NULL
• Check the date boundaries. Is the range inclusive at both ends? A BETWEEN on a timestamp column typically excludes most of the final day, because the end of the range is midnight
• Check GROUP BY completeness. Every selected non aggregated column has to appear in the GROUP BY. Some engines enforce this, some silently return arbitrary values
• Run EXPLAIN on anything that will run more than once. It shows the execution plan and reveals full table scans before they become a problem
• Spot check against a known answer. If you already know one customer's total from another report, verify the query produces the same figure for that customer

The general rule: verify the query against something you already know to be true before you trust it on something you do not.

Performance Traps in Generated SQL

AI written SQL tends to be correct before it is fast. Common issues, and what to do about them:

• SELECT * pulls every column including large text and blob fields you do not need. Name the columns you actually want
• Functions on indexed columns in a WHERE clause. WHERE YEAR(created_at) = 2026 cannot use an index on created_at. Rewrite it as a range comparison against two dates and the index gets used
• Leading wildcard LIKE. LIKE '%thing' cannot use a standard index. If you need this often, you need full text search rather than a cleverer regex
• Implicit type conversion. Comparing a numeric column to a quoted string can silently disable an index and, on some engines, produce surprising comparison behaviour
• Correlated subqueries in the SELECT list. These can run once per output row. A join or a window function is usually far faster
• Missing join conditions. A join with no ON clause is a cartesian product. Ten thousand rows joined to ten thousand rows is a hundred million rows
• DISTINCT used to hide a bad join. If you needed DISTINCT to fix duplicates, the join is usually wrong. Fix the join instead
• OR across different columns often prevents index use. A UNION of two indexed queries is frequently faster
• No LIMIT while exploring. Add one while you are working out what the query does, remove it when you are confident

None of this makes a generated query wrong. It makes it slow, and slow queries on a shared production database affect everyone.

Features

What This Tool Will Not Do

Being clear about the edges is more useful than pretending there are none.

• It does not connect to your database. Deliberately. That is a feature for anyone whose security policy forbids third party connectors, and a limitation if you wanted a full client. If you need connected execution, SQLAI, Chat2DB and DBeaver all do that job
• It does not run the query. You copy it and run it in your own client
• It cannot see your data, so it cannot tell you whether a filter will return anything. It works from structure, not contents
• It cannot design your schema for you. It can write DDL from a description, but data modelling is a design decision that needs a human who understands the domain
• It will not tune a query against your indexes, because it cannot see your indexes or your query plan. EXPLAIN in your own environment is the tool for that
• It is not a substitute for understanding SQL if you work with databases daily. It is a very good accelerator and a very good teacher, and it is a poor thing to depend on blindly

Common Mistakes When Describing a Query

• Being vague about time. "Recent", "lately" and "last month" all get interpreted, and possibly not the way you meant. Give a definite window
• Not mentioning the schema. Covered above at length, and it remains the biggest cause of a query that does not run
• Assuming business logic is obvious. If "active customer" means someone who ordered in the last 90 days at your company, say that. The generator does not know your definitions
• Forgetting the dialect. Then wondering why LIMIT throws an error on SQL Server
• Asking for too much in one prompt. Six requirements in one sentence produces a query that satisfies four of them. Generate the core query, then refine
• Not saying what you want back. Specify the columns, otherwise you get a guess
• Ignoring NULL. If a column can be empty, say what should happen to those rows
• Running the first result immediately. Especially anything that writes. Read it first

SQL Quick Reference

The execution order explains two things that confuse everyone: why you cannot use a SELECT alias in a WHERE clause, since WHERE runs before SELECT, and why filtering on an aggregate needs HAVING rather than WHERE.

Who Uses a SQL Query Generator

• Developers who write SQL occasionally and never retain window function syntax
• Data analysts who know SQL well and want the boring first draft written for them
• Product managers and marketers who need one number from the database without filing a ticket and waiting three days
• Founders querying their own application database without a data team
• Students learning SQL, who benefit far more from the explanation than from the query
• Support and operations staff who need to look something up in an internal database
• QA engineers writing data setup and verification queries
• Anyone inheriting a codebase who needs to understand a 200 line query somebody left behind
• Spreadsheet users moving from Excel and Sheets to a real database for the first time

Frequently Asked Questions

What is an AI SQL query generator?
It is a tool that converts a plain English description of what you want from a database into a working SQL statement. You describe the result, it writes the query, and you run it in your own database client.

Is this SQL query generator free?
Yes. No account, no email address, no usage cap and no trial period.

Which databases are supported?
MySQL, PostgreSQL, SQL Server, Oracle, SQLite, BigQuery, Snowflake and MariaDB. Set the dialect before generating, because row limiting, string functions and date arithmetic differ between them.

Do I have to connect my database?
No, and you cannot. This tool never connects to anything. You optionally paste your table structure as text, which is column names rather than data. If your security policy forbids third party database connectors, that is exactly why this tool works the way it does.

Why does the generated query reference columns that do not exist?
Because it has not seen your schema and is guessing at naming conventions. Paste your tables and columns into the schema field and the problem disappears. This is the single most common reason a generated query fails to run.

Is AI generated SQL accurate?
Accurate enough to be genuinely useful, and not something to run unchecked. With a schema provided, straightforward queries are usually correct first time. Complex multi step logic often needs one or two refinements. Always read the query before running it, and always verify the result against something you already know.

Can it write UPDATE and DELETE statements?
Yes, and you should treat those with real care. Read the WHERE clause before running anything that modifies data, convert it to a SELECT first to see exactly which rows would be affected, and wrap it in a transaction where your database supports one.

Can it write window functions and CTEs?
Yes. Running totals, rankings, top N per group and period over period comparisons are all supported, and they are where a generator saves the most time, because they are the hardest to write from memory.

Will it optimise an existing query?
It can suggest common improvements such as replacing a correlated subquery with a join or avoiding functions on indexed columns. It cannot see your indexes, your data distribution or your query plan, so run EXPLAIN in your own environment for real tuning work.

Can it explain a query I already have?
Yes. Tick the explain option and paste the query. You get a plain English breakdown of what each part does, which is genuinely useful for inherited SQL that nobody has touched in years.

Is my schema stored?
Nothing you paste is used to build a dataset or a profile. You are pasting structure rather than records in any case. If your table names are themselves sensitive, rename them before pasting and rename them back in the output.

Do I need to know SQL to use this?
No, but you should read the output rather than pasting it blind, particularly for anything that changes data. Ticking the explain box helps a great deal, and using this tool regularly is a surprisingly effective way to learn SQL properly.

Can it generate test data?
No. That is a different category of tool, even though searches often mix the two. For fake rows and mock datasets, use a dedicated test data generator. This tool writes queries.

Generate Your Query

Describe what you need, paste your schema, pick your database, and read the query before you run it.