AI analytics assistants · Vanna AI

Vanna AI

Open-source RAG framework that trains an LLM on a database's schema and past queries to turn questions into SQL.

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Vanna AI is an open-source Python framework (MIT-licensed) that uses retrieval-augmented generation to convert natural-language questions into SQL. A team 'trains' Vanna by feeding it schema definitions, documentation, and example question/SQL pairs; at query time it retrieves the most relevant training context and asks an LLM (hosted or self-run via Ollama) to write the SQL, which is then run against the connected database. Because it is retrieval-based, answer accuracy depends directly on how much and how well-curated the training context is, not on Vanna itself understanding the database. The open-source core can be self-hosted end-to-end with a local LLM and no data leaving the customer's environment. Vanna Cloud adds a hosted admin UI, access control, observability, and audit logs on top of the same open-source engine, sold as Explorer, Team, and Enterprise subscriptions.

At a glance

Vendor Vanna AI
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (MIT)
Best for Engineering teams that want a self-hostable, open-source text-to-SQL engine they can train on their own schema.

Pricing

Open-source library is free to self-host with your own LLM; Vanna Cloud is a per-month subscription metered by daily question volume, plus a custom-priced Enterprise tier.

Plan Price Notes
Open source (Vanna Core) Free Self-hosted, bring-your-own LLM and database
Explorer $50/month 20 questions/day, admin features, API access; for teams of 2-3
Team $500/month 300 questions/day, setup assistance, same-day live support
Enterprise Custom Unlimited questions, on-prem deployment, SAML SSO, admin API

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Retrieval-augmented generation trained on schema, docs, and example queries
  • Open-source Python library, MIT-licensed
  • Works with hosted LLMs or local models via Ollama
  • Multi-database support (Snowflake, BigQuery, PostgreSQL, and others)
  • Multi-turn follow-up questions
  • Access-controlled hosted admin UI (Vanna Cloud)
  • Automated chart generation from query results

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Wren AI Open-source 'GenBI' engine that gives AI agents a governed semantic layer (MDL) for turning questions into SQL against a warehouse. Defog.ai AI data analyst built on Defog's own fine-tuned text-to-SQL model, SQLCoder, for querying warehouses and uploaded CSVs. Querio AI data platform built around version-controlled 'analytics as code' notebooks that answer questions against a defined context layer.

Vanna AI in the index now

Terms to know

Text-to-SQL Technology that translates a plain-language question directly into a runnable SQL query against a database. Natural language query Asking a data question in plain language and getting an answer without writing code or a query language. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. Retrieval-augmented generation (RAG) An architecture that retrieves relevant documents at query time and feeds them to a language model so its answers are grounded in sources.

Related guides

How to choose an AI analytics assistant AI analytics assistants are only as accurate as the context they're given — choose by what they query and how that context is governed, not the demo. How to use AI for data analysis safely Text-to-SQL and AI data assistants can speed up analysis or produce fluent wrong answers — the difference is what sits between the model and your data.