Integrations
AI services are available through APIs so the query generation can be embedded in your own application. Databases connect directly, including MySQL, PostgreSQL, MongoDB, Redis, Snowflake, BigQuery, ClickHouse and others.
Chat2DB
An AI SQL client that turns plain questions into working queries across 30+ databases.
Intermediate API
What is Chat2DB?
Chat2DB is an AI SQL client that converts natural language into queries, manages 30+ relational and non relational databases, and adds dashboards, migration and schema aware chat in one toolkit.
Chat2DB is an AI powered SQL client that blends a traditional database GUI with a natural language layer. You connect to a data source, describe what you want in plain language, and the tool drafts SQL that you can run, edit or debug. It supports more than 30 engines, spanning relational systems such as MySQL, PostgreSQL, SQL Server, Oracle and DB2 as well as non relational stores like MongoDB, Redis, Cassandra, Neo4j and Elasticsearch. A desktop client keeps local and cloud databases in one place so analysts stop switching between separate tools. The product is aimed at a wide audience rather than pure engineers. Developers get an editor, schema driven code generation and one click fixes for broken SQL. Data analysts and business managers can ask questions and receive auto generated charts and BI dashboards without writing joins by hand. The AI side is grounded in the schema: the tool reads table structures and lets teams add business terms in column comments so the model understands what a field like revenue actually means. A context aware chatbot keeps the conversation going, and a choice of 10+ language models is offered. Beyond querying, Chat2DB covers the routine database admin work: viewing and editing rows, smart sorting, advanced filters, ER diagrams, import and export of CSV, XLSX, XLS and SQL files, structure synchronization and migration handling millions of rows. Dashboards and charts can be shared with teammates through links, and the AI service is exposed through APIs for embedding in your own application. Security messaging centers on local query processing, schema only exposure to models, AES and RSA encryption, SSH tunneling, two factor login and SQL audit for administrators. The project is open source under Apache 2.0. It sits between classic SQL IDEs such as DBeaver and newer chat based analytics tools. Compared with a plain GUI client it adds text to SQL, dashboards and migration in one package; compared with a pure chatbot it keeps a full editor and admin surface. Teams that need tightly governed, audited access to many heterogeneous databases are the clearest fit.
How do you use Chat2DB?
1Create an account or open the web client
Start at the app subdomain to sign in. The site advertises third party logins such as Google and GitHub, which double as a two factor step for account security.
2Connect your database
Add a data source using the connection details for your engine. If you use an uncommon system, check the per database client page first, for example the MongoDB or Snowflake pages.
3Let the AI read the schema
Once connected, the client inspects table structures. Add business definitions in column comments so generated queries match how your team actually talks about the data.4Ask a question in plain language
Use the Text to SQL panel or the chatbot to describe the result you want. Review the generated statement in the SQL editor before running it against production data.5Fix errors and build a dashboard
If a query fails, apply the one click error fix and rerun. Turn the result into a chart or BI dashboard, then share it with the team through a link.6Export or migrate data
Use the import and export tools to move CSV, XLSX, XLS or SQL files, and structure synchronization when schemas need to match across environments.
Pros and cons
Pros
- Text to SQL works across a very broad list of engines, from MySQL and PostgreSQL to MongoDB, Redis and Neo4jAI
- The AI reads your schema and supports custom business terms in column comments, which improves query accuracyAI
- Combines a full database GUI (data editing, ER diagrams, import and export) with AI analysis and shareable BI dashboardsAI
- Open source under Apache 2.0, with local query processing and schema only exposure to the modelsAI
- SQL audit, SSH tunneling and two factor login give administrators real governance leversAI
Cons
- The homepage lists no plan prices, seat limits or usage caps for the AI features, so budgeting needs a sales conversationAI
- Deployment appears desktop and web focused, with no native mobile or Linux client named on the siteAI
- Reliance on AI generated SQL can still produce plausible but wrong joins on large schemas, and nothing on the page describes how accuracy is verifiedAI
- The feature list is wide, but public documentation on advanced migration edge cases is thin on the homepageAI
- Security claims are marketing statements; buyers in regulated sectors will still need independent audit evidenceAI
How much does Chat2DB cost?
Features
Text to SQL, a context aware AI chatbot with a choice of 10+ LLMs, schema based code generation and one click SQL error fixes. The GUI adds data viewing and editing, ER diagrams, filtering, sorting, import and export of CSV, XLSX, XLS and SQL files, structure synchronization and migration at millions of rows. AI analysis produces charts and BI dashboards that can be shared by link, and an API exposes the AI services to your own app.
Frequently asked questions about Chat2DB
Does Chat2DB have an API?
Yes, Chat2DB offers an API.How do you use Chat2DB?
The walkthrough on this page covers 6 steps: 1. Create an account or open the web client 2. Connect your database 3. Let the AI read the schema 4. Ask a question in plain language 5. Fix errors and build a dashboard 6. Export or migrate data.What platforms does Chat2DB support?
Chat2DB is available on Web App, Windows and MacOS.What does Chat2DB integrate with?
AI services are available through APIs so the query generation can be embedded in your own application. Databases connect directly, including MySQL, PostgreSQL, MongoDB, Redis, Snowflake, BigQuery, ClickHouse and others.What are the limitations of Chat2DB?
The homepage lists no plan prices, seat limits or usage caps for the AI features, so budgeting needs a sales conversation. Deployment appears desktop and web focused, with no native mobile or Linux client named on the site. Reliance on AI generated SQL can still produce plausible but wrong joins on large schemas, and nothing on the page describes how accuracy is verified.
