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Vanna AI

data-analysis
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Open-source AI framework for generating accurate SQL from natural language - train it on your schema and query any database in plain English.

Vanna AI is an open-source Python library that uses retrieval-augmented generation to convert natural language questions into accurate SQL queries. Unlike generic text-to-SQL tools, Vanna trains on your specific database schema, DDL statements, and sample query-answer pairs, which dramatically improves accuracy on complex schemas. It supports 15+ databases including PostgreSQL, MySQL, BigQuery, Snowflake, and Databricks, and works with any LLM backend including OpenAI, Anthropic, and local models via Ollama. A hosted Vanna Cloud offering provides a web UI for teams who prefer not to self-host.

#sql
#text-to-sql
#data-analysis
#open-source
#developer-tools
#llm
Freemium

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vanna.ai
Freemium
Pricing Model
Data & Analytics
Category
2023
Since
Free Plan
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Key Features

  • RAG-based SQL generation trained on your specific schema, DDL, and sample queries
  • Support for 15+ databases including PostgreSQL, MySQL, BigQuery, Snowflake, and Databricks
  • Python library embeddable directly into applications or Jupyter notebooks
  • Model-agnostic - works with OpenAI, Anthropic, Groq, Ollama, and other LLM providers
  • Follow-up question support for iterative, conversational data exploration
  • Vanna Cloud hosted UI for teams who prefer a no-infrastructure option

Use Cases

  • Data analysts querying complex databases without memorizing SQL syntax
  • Developers embedding natural language database interfaces into internal tools
  • Business users self-serving data requests without relying on engineering
  • Data teams building schema-aware SQL generation tools for company-specific databases

Pros

  • Open-source and self-hostable - no vendor lock-in and full control over data
  • Schema-specific RAG training reduces hallucination rate significantly vs generic tools
  • Model-agnostic design allows switching LLM providers without code changes

Cons

  • Initial setup requires training on your schema, which takes time for large databases
  • Accuracy degrades with very large or poorly documented schemas without curation
  • Python-only - no native SDK for other languages

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