Chat2DB

Sweet spot: an engineer or analyst who already uses a SQL IDE every day and is tired of writing the same boilerplate JOIN a hundred times. Chat2DB slots in where DBeaver sits — you gain AI speedup, lose nothing you already had. Failure modes. NL-to-SQL quality collapses on sprawling, poorly-named schemas — budget time to write clear table / column comments or you will fight the model. For SaaS teams with strict data-handling rules, sending even schema metadata to a hosted LLM may require the enterprise on-prem tier, which changes the cost conversation. What to pilot. Point Chat2DB at a dev-replica of your real database. Ask the 20 questions your team actually asks weekly ("MAU by plan," "top refund reasons last month," etc). Measure how many answers are correct on first try. Above ~70% and it will save real time; below that and you need schema comments / semantic layer work first.

Chat2DB is a desktop and web SQL IDE with an LLM built into the query layer. Connect to Postgres, MySQL, SQL Server, SQLite, Oracle, Redis, MongoDB, Snowflake, BigQuery, or ClickHouse and ask questions in natural language — "top 10 customers by revenue last quarter" — and the model generates, explains, and executes the SQL against your schema. Beyond natural-language querying, it includes the standard SQL IDE features (schema browser, query history, result export, ER diagrams) plus AI-specific ones: query optimisation suggestions, schema-aware autocomplete, data-anonymisation before sending schema to the LLM, and a visual dashboard builder. The desktop app is free and open-source; paid tiers add team sharing, audit logs, and an on-prem option. Chat2DB positions itself against DBeaver (richer SQL IDE, no AI) and Hex / Mode (analytics-focused, cloud-only). Its niche is engineers and analysts who live in a SQL IDE every day and want the LLM as a coworker, not a replacement.