Make Slow SQL queries execute in milliseconds and spend time elsewhere

A slow query is rarely slow for an obvious reason. The optimizer may choose a different join order, an index may be missing, a function may prevent an index from being used, or the query may be pulling far more rows than the report needs. SQLAI's SQL optimizer reviews the query in the context of your engine, schema and rules, then proposes a rewrite you can inspect before changing production code.

Review a slow query

Paste the query, choose the database engine and version, and optionally attach the schema or rules that describe your tables, indexes and conventions. SQLAI analyses the query for the selected dialect and returns a rewritten version focused on faster execution and lower resource use.

The input can be a report query, an application query, a dashboard query or a one-off analysis. If the query is part of a larger workflow, include the relevant schema and any constraints that matter, such as a required tenant filter or a date range. The more context the optimizer has, the less likely it is to suggest a rewrite that is faster but changes the result.

Try the SQL optimizer on a sample query.

Get a rewritten query you can review

The optimizer returns a complete query rather than a list of hints. Each suggested change is explained, so you can see what moved, what was removed and why it should help. Typical improvements include removing unnecessary subqueries, replacing OR conditions with a UNION ALL where appropriate, reducing repeated work with a CTE, adding selective predicates earlier, or changing a join strategy.

Where the engine supports it, SQLAI can also recommend indexes for the filters and joins in the query and provide the SQL to create them. Index recommendations are proposals: write cost, existing indexes, data distribution and write workload all affect whether an index is a net improvement.

Optional deep analysis takes longer and uses a disposable sandbox to compare a baseline with a proposed rewrite for supported engines. It can provide an estimated impact and additional tuning notes. Deep analysis is not a substitute for testing against your own data, statistics and server configuration.

See the changes before you run them

A rewrite is only useful if you can verify it. The optimizer shows the original and optimized queries side by side, so you can review every change before deployment. Use the explanation to check that the result set is preserved, then run both versions on a copy or with a read-only role and compare the output and execution plan.

SQLAI does not run the optimized query against your database. Deep analysis, where available, runs in a disposable environment rather than your production system. Result rows are not added to the conversation automatically. See the privacy policy for details on how prompts and schema information are handled.

Schema-aware optimization

The same query can be fast on one schema and slow on another. SQLAI can use your tables, columns, indexes and database rules to make the advice specific to your system. That context helps with small details that generic advice misses: a function that is not sargable in your engine, a composite index whose column order matters, a partition key that should be part of the filter, or a dialect-specific rewrite that is not available in your version.

Database rules are a good place to record constraints such as read-only replicas, mandatory tenant filters, naming conventions, forbidden constructs and the indexes that already exist. Learn more about schema-aware context and supported databases.

Supported databases

The SQL optimizer works across the engines SQLAI supports, including PostgreSQL, MySQL, MariaDB, SQL Server, Oracle PL/SQL, SQLite, IBM DB2, Snowflake, BigQuery, Redshift, ClickHouse, Trino, Presto, Vertica, SAP HANA, Teradata, Firebird, InterBase, Sybase and Microsoft Access. The exact version coverage and public API names are on the supported databases page.

Dialect matters. A rewrite that is valid in PostgreSQL may not be valid in T-SQL or Oracle, so select the correct engine and version before optimizing. If you need to move a query between engines, start from the Text to SQL generator and review the dialect-specific parts.

Who it is for

• Backend and application developers dealing with high-latency queries in production.
• Analytics engineers and data analysts improving dashboards and reporting queries.
• DBAs reviewing expensive queries and index choices.
• Data teams comparing alternative rewrites in one conversation.

Frequently asked questions

How does the SQL optimizer work?

SQLAI analyses the query for the selected engine and version, uses the attached schema and rules as context, and returns a rewritten query with explanations. For supported engines, optional deep analysis compares a baseline and a proposed rewrite in a disposable sandbox. You review the result and decide what to change.

Does SQLAI run the query against my database?

No. The optimizer does not execute the query against your database. Deep analysis, where available, runs in a disposable environment. You run the final query yourself, ideally on a copy or with a read-only role, and compare the results and execution plan.

Can it recommend indexes?

Yes. Where the engine supports it, the optimizer suggests indexes for the filters and joins in the query and provides SQL to create them. Treat every index recommendation as a proposal and check write cost, existing indexes and data distribution before applying it.

Which databases are supported?

The optimizer supports the SQL and analytical engines listed on the supported databases page, including PostgreSQL, MySQL, SQL Server, Oracle, SQLite, BigQuery, Snowflake, Redshift, ClickHouse and others. Select the engine and version so the rewrite uses the right dialect.

What is deep analysis?

Deep analysis is an optional, slower review that uses a disposable sandbox to compare a baseline with a proposed rewrite for supported engines. It can provide estimated impact and additional tuning notes. It is guidance, not a guarantee: test the rewrite against your own data and workload before production.

Can I ask follow-up questions?

Yes. The optimizer is conversational. You can ask why a change was made, request an alternative rewrite, compare two approaches or go deeper on an index recommendation without starting over.

Review before production

AI-generated rewrites can change behaviour. Check that the result set is identical, review the execution plan and test on production-like data before deploying. Verify index recommendations against your existing indexes, write workload and storage constraints. Performance estimates are estimates; the only reliable measurement is your own workload.