Vanna.AI vs Avanty

Vanna.AI lets business users query databases in plain English and get SQL-backed answers without writing queries themselves. The open source framework connects to PostgreSQL, MySQL, Snowflake, BigQuery, SQLite, and other stores, then pairs with Anthropic, OpenAI, Gemini, or Ollama models. Vanna 2.0 adds multi-turn agents with access controls so each user only sees permitted data.

Most text-to-SQL tools stop at a single question. Vanna ships as an agent framework with hosted admin features for observability, audit logs, agent memory, and file storage. You can self-host with pip install vanna or use Vanna Cloud when you want managed access control and same-day support.

Vanna.AI fits data teams, product managers, and analysts who need governed natural-language access to warehouse data. Explorer costs $50 per month with 20 questions per day, Team is $500 per month with 300 daily questions, and Enterprise offers unlimited scale with SAML SSO and on-prem deployment.

Avanty sits inside Metabase as a Chrome extension and writes SQL from plain English prompts. It reads your connected schema, drafts queries in the editor, and explains existing SQL step by step so analysts spend less time fighting syntax.

Generic SQL copilots work in a blank editor. Avanty is scoped to Metabase: it knows your tables, respects custom schema names like datawarehouse.users, and opens from a command panel with Cmd Shift period on macOS or Ctrl Shift period on Windows. An inline diff viewer shows proposed edits before you accept them.

Data analysts and analytics engineers who live in Metabase get the most from Avanty. Business questions that used to mean a Slack ping and a half-written query can start as a natural language prompt, land in Metabase as a draft question, and move through review faster. A preview Slack integration even surfaces matching Metabase questions from channel messages.