Chat2DB

AI-native SQL client and Text-to-SQL copilot for 30+ databases

Freemium· Community (free, open source) / Local / Professional (30-day free trial) / Team / Enterprise (custom). Annual billing saves up to 25%. 50% discount for students, teachers, and non-profits. Exact tier prices not publicly listed on the pricing page.CodingModel-agnostic: OpenAI GPT-4o, Anthropic Claude, Google Gemini, DeepSeek, Qwen, and self-hosted Ollama/vLLM endpoints

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Best for

Developers, analysts, and data teams who want an open-source, multi-database GUI client with a serious Text-to-SQL copilot and the option to route prompts to their own LLM.

Skip if

Non-technical users who need a fully managed BI dashboard, or regulated teams that require a vendor-signed SaaS with published SOC 2 pricing and no self-hosted footprint.

Chat2DB is an open-source, AI-powered database client and SQL copilot that lets developers, analysts, and business users query, explore, and manage relational and non-relational databases using natural language. It bundles a full-featured GUI (schema browser, query editor, ER diagrams, import/export, visual data editor) with a Text-to-SQL layer that turns plain English (or Chinese) questions into executable, dialect-aware SQL, then explains, optimizes, or one-click-fixes errors in the returned queries.

The tool supports 30+ engines out of the box, including MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, ClickHouse, Snowflake, BigQuery, Redshift, MongoDB, Redis, and Elasticsearch, so mixed-stack teams can standardize on a single client instead of juggling DBeaver, TablePlus, Mongo Compass, and vendor consoles. Chat2DB reads column comments and DDL to give the LLM real business context, which materially improves query accuracy over generic ChatGPT prompting, and it can auto-generate dashboards and charts directly from result sets for lightweight analytics.

It ships in several shapes: a free Community edition (Apache 2.0, self-hostable, hundreds of thousands of GitHub stars), a Local desktop app that keeps data on-device, a hosted Professional/Team plan with premium models and collaboration, and an Enterprise deployment with SSO, RBAC, audit logging, and private-model routing. Ten-plus LLM backends are supported, letting teams route requests to OpenAI, Claude, Gemini, DeepSeek, Qwen, or a self-hosted Ollama/vLLM endpoint depending on cost and data-residency requirements. There is also an HTTP API so Chat2DB's Text-to-SQL and analysis can be embedded into internal tools or customer-facing apps.

Common workflows: an analyst asks a question in English and iterates on the generated SQL; a developer pastes a slow query for an EXPLAIN-plus-rewrite; a PM opens a table, filters visually, and exports to CSV; a data team wires the API into a Slack bot so anyone can self-serve numbers without pinging the on-call analyst.

Editor's take

Chat2DB is the most credible open-source answer to 'what if DBeaver had a good AI copilot.' The 30+ database coverage plus bring-your-own-model support make it a rare tool that scales from a solo developer on the free Community build to an enterprise team routing through a private LLM. Just be ready to test Text-to-SQL accuracy on your own schema before trusting it in production, and expect to email sales to find out what Professional actually costs.

— The AI Tool Bible editorial team

Pros

✅ Genuinely open source under Apache 2.0 with a large, active GitHub community and a self-hostable server edition
✅ Broadest database coverage of any AI SQL client - 30+ engines spanning OLTP, OLAP, NoSQL, and cloud warehouses
✅ Model-agnostic: works with OpenAI, Claude, Gemini, DeepSeek, Qwen, Ollama, and custom endpoints so you can control cost and data residency
✅ Uses schema, DDL, and column comments as context, which produces noticeably better SQL than generic ChatGPT prompting
✅ Full GUI client (ER diagrams, visual editor, import/export, dashboards) means it can replace DBeaver or TablePlus, not just supplement them
✅ Local desktop build keeps queries and results on-device for teams with strict data-egress rules
✅ Public HTTP API lets you embed Text-to-SQL into internal tools and end-user products

Cons

⚠️ Pricing for paid tiers is not published on the site - you have to start a trial or contact sales to see numbers
⚠️ Text-to-SQL accuracy still degrades on very wide schemas, poorly named columns, or heavy joins across denormalized warehouses
⚠️ The Electron-based desktop app can feel heavy compared to native clients like TablePlus, and updates occasionally break plugins
⚠️ Advanced governance features (SSO, audit, RBAC, private model routing) are gated behind Enterprise
⚠️ Documentation and UI translations lag the Chinese-language versions in places, which shows in some menus and error messages

Use cases

Natural-language SQL queryingMulti-database GUI clientSQL error diagnosis and one-click fixesQuery optimization and EXPLAIN rewritingAuto-generated dashboards from query resultsSchema exploration and ER diagram generationCSV/Excel import and exportEmbedded Text-to-SQL via APISelf-hosted AI SQL copilot with private LLMsCross-database migration and comparison