Question 1

What is Text-to-SQL AI Converter?

Accepted Answer

Text-to-SQL AI Converter is a free AI-powered tool that turns plain-language descriptions into working SQL queries. Describe what you want in English - or in French, German, Spanish, Italian, Portuguese, or another supported language - and the tool generates the corresponding SQL, ready to copy into your database client or edit further.

Behind the scenes, the tool uses AI language models to interpret your description, understand your intent, and translate it into syntactically valid SQL for the database dialect you select. You can provide as much or as little context as you like: a quick one-line description for simple queries, or a fuller brief with table structures (either described in plain language or pasted as CREATE TABLE statements) for complex queries involving joins, filters, and aggregations.

Common use cases include:

Generating queries faster when you know what you want but don't remember the exact syntax.
Bridging the SQL gap for analysts, product managers, or domain experts who think in business terms rather than database terms.
Learning SQL by seeing how natural-language requests translate into queries.
Modifying existing queries by pasting in your current SQL and describing the change you want.

The tool is free, requires no signup, and supports 13 database dialects, including PostgreSQL, MySQL, Oracle, SQL Server, Snowflake, BigQuery, and ClickHouse.

Question 2

Is Text-to-SQL AI Converter free?

Accepted Answer

Yes! Text-to-SQL AI Converter is 100% free.

Question 3

Can I use Text-to-SQL AI Converter even if I have no prior knowledge of SQL?

Accepted Answer

Yes, Text-to-SQL AI Converter is designed to be user-friendly and intuitive, even for users with no prior knowledge of SQL. The tool provides a simple interface for users to input their natural language query and generate the corresponding SQL query.

Question 4

Does Text-to-SQL AI Converter support multiple database types?

Accepted Answer

Yes, Text-to-SQL AI Converter supports multiple database types, including PostgreSQL, MySQL, Oracle, MariaDB, SQLite, SQL Server, ClickHouse, MongoDB, OpenSearch, Snowflake, BigQuery, and Elasticsearch, plus a generic "Other" option. Users can select their desired database type and Text-to-SQL AI Converter will generate queries that are specific to that database. It is also possible to define the database with the provided text.

Question 5

Can I edit the SQL queries generated by Text-to-SQL AI Converter?

Accepted Answer

Yes, Text-to-SQL AI Converter provides the option to edit the generated SQL query in the browser before executing it. This allows users to fine-tune the query to their specific needs.

Question 6

Is my data secure when using Text-to-SQL AI Converter?

Accepted Answer

Yes - but because this tool uses AI, the privacy story is a bit different from non-AI tools, and worth explaining in full.

What we don't do. We don't store the text you provide or the SQL the tool generates. There's no database of past queries, no account history, and no record of your sessions on our side.

What happens to your input. Your description and any schema context you provide are sent to a third-party AI provider to generate the SQL response. Your input is processed for that single request and is not used to train AI models. Once the response is returned, the request data is no longer needed and is discarded.

What you should avoid sending. Don't paste real production schemas containing confidential column names, real customer data, credentials, API keys, internal table names that reveal business logic, or anything else you wouldn't want shared with a third-party service. The tool works just as well - often better - when you describe your schema generically. Instead of pasting the real customers_pii_export_2024_q3 table, describe it as "a table of customers with id, name, email, and signup date." You'll get accurate SQL without exposing sensitive details.

Best practice. Treat Text-to-SQL the same way you'd treat any AI assistant: useful for structure, syntax, and patterns; not a place to paste production data.

Question 7

Can Text-to-SQL AI Converter handle complex queries?

Accepted Answer

Yes, the tool can generate complex queries involving multiple tables, joins, subqueries, common table expressions (CTEs), window functions, aggregations, and conditional logic. In practice, the quality of complex output depends heavily on how much context you provide:

For simple queries against a well-described table, results are usually accurate the first time. "Show me the top 5 customers by total order value" against a clearly defined orders and customers schema works reliably.
For multi-table queries, providing the table structures - either as plain-language descriptions or as CREATE TABLE statements - dramatically improves accuracy. Without that context, the tool has to guess at column names and relationships, which is where most errors come from.
For advanced patterns like recursive CTEs, complex window functions, or analytical queries with multiple aggregation levels, expect to review and refine the output. The tool typically gets the structure right but may need adjustments on edge cases like NULL handling, tie-breaking in window functions, or specific filtering logic.

A reliable rule of thumb: the more specific your description and the more schema context you provide, the more accurate the output. Always review generated queries before running them in production - especially for queries that modify data or that you'll embed in application code.

Question 8

Can I use Text-to-SQL AI Converter to generate reports or visualize data?

Accepted Answer

Text-to-SQL AI Converter is a tool that converts natural language text to SQL queries. It does not provide report generation or data visualization features. However, the SQL queries generated by Text-to-SQL AI Converter can be used with other tools to generate reports or visualize data.

Question 9

Which languages does it support?

Accepted Answer

Text-to-SQL AI Converter supports multiple languages, including English, French, German, Spanish, Italian, Portuguese and others. Users can use their desired language to generate the SQL.

Question 10

Can I use Text-to-SQL AI Converter to modify existing SQL queries?

Accepted Answer

Yes, you can provide your original query as an input and ask Text-to-SQL AI Converter to modify it. For example use this query "SELECT * from Songs" and modify it to get only the top 10 most listened songs.

Question 11

How accurate are the generated SQL queries, and what should I do if one doesn't work?

Accepted Answer

Accuracy depends on three things: how clearly you describe what you want, how much schema context you provide, and how complex the query is.
Simple queries against well-described tables, such as single-table SELECTs with basic filters, sorting, or aggregation, are usually accurate the first time. Medium-complexity queries, like joins across two or three tables or basic CTEs, are generally accurate when you provide table structures, either as plain language or as CREATE TABLE statements. Complex queries, such as recursive CTEs, nested window functions, or multi-level aggregations, usually need review. The tool typically generates the right overall structure but may need adjustments on edge cases like NULL handling, deduplication, or exact aggregation grain.
If a generated query doesn't run correctly against your database, common causes include:

Hallucinated table or column names. The most frequent issue. If you didn't specify your schema exactly, the tool guessed. Fix by pasting CREATE TABLE statements or describing your schema explicitly.
Wrong dialect selected. If the dropdown doesn't match your actual database, the query may use functions or syntax that don't exist there (STRING_AGG vs. GROUP_CONCAT, LIMIT/OFFSET vs. TOP, etc.).
Version-specific features. The tool may generate syntax valid in a newer version than the one you're running. Mention your version in the prompt if you're on an older release.
Vendor-specific extensions. Features like ClickHouse table engines, Snowflake's QUALIFY, or BigQuery's STRUCT/ARRAY literals aren't always handled correctly. Review these carefully against your database's documentation.
Ambiguous descriptions. A prompt like "recent orders" can be interpreted several ways. Be explicit about time windows and filter criteria.
Missing context. Things like computed columns, views vs. tables, or custom data types aren't visible from aprompt alone. Spell out anything non-obvious.

The tool also does not optimize for performance, only correctness, so a generated query may be right but inefficient. When something doesn't work, the fastest fix is usually to refine the prompt with more context and try again, rather than debugging by hand. Always review complex queries, and especially write operations, before running them against your database.

Question 12

What's the best way to describe my query for the best results?

Accepted Answer

Good prompts share three qualities: they're specific about what you want, they provide schema context, and they call out edge cases.

Be specific about the output. Instead of "show me customer data," try "list each customer's name, email, and total amount spent in 2024, sorted by total spent descending."
Provide schema context. This is the single biggest accuracy improvement. You can do this in plain language ("a customers table with id, name, email, and signup_date") or with actual CREATE TABLE statements for more precision.
You don't need to share your real schema. Generic or anonymized table and column names work just as well. For example, describe "a table of orders with customer ID, order date, and total amount" instead of pasting your real production schema. There's no need to share confidential details to get accurate results.
Specify joins and relationships. State how tables relate, for example "orders.customer_id references customers.id," rather than leaving the tool to guess.
Call out edge cases. Details like "exclude customers with no orders" or "treat NULL values as zero" prevent the tool from making default choices that miss your intent.
Specify the database dialect. Set the dropdown to your actual database, since syntax differs between PostgreSQL, MySQL, BigQuery, and others.
Iterate rather than restart. If the first result isn't quite right, refine the prompt, for example "same query, but only for customers in the US."

Question 13

Can the tool generate INSERT, UPDATE, or DELETE statements?

Accepted Answer

Yes. While SELECT queries are the most common use case, the tool can also generate INSERT, UPDATE, DELETE, CREATE TABLE, ALTER TABLE, and other DML/DDL statements. Always review write operations carefully before running them - especially DELETE and UPDATE without WHERE clauses.

Question 14

How does Text-to-SQL compare to writing SQL by hand?

Accepted Answer

Text-to-SQL is fastest for routine queries, exploratory analysis, and learning. For business-critical queries, performance-sensitive workloads, or queries that need to handle subtle edge cases, hand-written SQL - possibly reviewed against the AI output - is still the safest approach. Think of the tool as a productive starting point, not a replacement for SQL knowledge.

Question 15

Do you offer an API version of Text-to-SQL?

Accepted Answer

We're exploring an API version for teams who want to integrate natural-language-to-SQL into internal tools, dashboards, chatbots, or BI applications. If you're interested, get in touch through our contact page.

Question 16

What other free SQL tools does Aiven offer?

Accepted Answer

Alongside Text-to-SQL, Aiven offers a SQL Syntax Checker, SQL Formatter, SQL Optimizer, SQL Compare, and a PostgreSQL Playground. A natural workflow: generate a query with Text-to-SQL, format it with the SQL Formatter, validate it with the SQL Syntax Checker, then test it in the PostgreSQL Playground.

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