Vanna

4.5 (2 reviews)

Developer Tools , Automation , Data Extraction

Vanna is a Python-based RAG framework that translates natural language into SQL by training on your specific database schema and documentation.

Excellent for developers building custom internal data tools, weaker for non-technical teams looking for a standalone BI platform.

Sql Python Rag Database Open-Source Data Analysis Llm Nlp

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

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Who Should Use Vanna?

Typical users

Data engineers, Python developers, and analytics teams who need to build custom text-to-SQL interfaces for their organizations.

Maturity fit

scaling to advanced

Choose this if…

You want to build a custom AI data assistant within an existing Python application
Your priority is high accuracy on complex, non-standard database schemas
You need to keep data processing within your own infrastructure or VPC
You want the flexibility to swap between different LLMs like GPT-4, Claude, or local models

Skip this if…

You need a no-code, plug-and-play dashboard for non-technical users
Your team lacks Python expertise to implement and maintain the framework
You have a very simple database where basic LLM prompting is sufficient without RAG

About Vanna

Vanna is an open-source Python framework designed to solve the accuracy issues of generic text-to-SQL tools. It uses Retrieval-Augmented Generation (RAG) to store your DDL, metadata, and 'Golden SQL' queries in a vector database, providing the LLM with the specific context needed to generate valid queries for your unique schema.

Official profiles

X (Twitter) LinkedIn GitHub

What it actually does

Vanna allows users to 'train' a model on their database structure without sending the actual data to the LLM. Once trained, it provides a simple interface where users ask questions in plain English, and the framework generates, executes, and visualizes the resulting SQL data.

What makes it different

Unlike many AI data tools that rely on zero-shot prompting, Vanna's architecture is built entirely around a RAG workflow. It is highly modular, allowing developers to choose their own LLM (OpenAI, Anthropic, Ollama) and vector store (ChromaDB, Pinecone, Marqo) rather than being locked into a single provider's stack.

RAG-based SQL generation Schema and metadata training Self-correction of SQL errors Plotly-based data visualization Multi-database connector support Slack and Microsoft Teams integration Streamlit web interface deployment Local LLM support for high privacy

Ratings across the web

4.5 (2 reviews)

G2 2 reviews

4.5/5

Ratings aggregated from independent review platforms.

Key Features

Training on DDL

Feeds your table structures into a vector store for precise schema awareness.

Golden SQL Pairs

Allows you to provide 'correct' query examples to improve accuracy over time.

Modular LLM Support

Connect to any major AI model or run local models via Ollama.

Self-Healing Queries

Automatically attempts to fix SQL syntax errors by feeding database error messages back to the AI.

Metadata Documentation

Incorporates business logic and column descriptions into the retrieval context.

Open Source Core

The MIT-licensed framework ensures no vendor lock-in for the core logic.

Interactive Visualizations

Automatically suggests and generates charts based on the query results.

Vanna Cloud

An optional managed service for those who don't want to manage their own vector database.

Pricing

Popular

Open Source

Free

MIT Licensed framework
Self-hosted vector store
Connect to any LLM
Unlimited training data
Full customization

Vanna Cloud (Free Tier)

Free

Managed vector store
Up to 100 questions per month
Public or private models
Slack/Teams integration

Vanna Cloud (Paid)

Usage-based month

Increased question limits
Priority support
Custom enterprise features
Managed infrastructure

Pricing checked 4 months ago

Pricing guidance

Best plan for most users: The Open Source plan is best for most developers as it offers total control over the stack and avoids recurring subscription fees.

Free plan enough? Yes, the Open Source version is fully featured; the Cloud free tier is only necessary if you want a managed vector store for quick testing.

Upgrade when:

When you need a managed vector database to reduce infrastructure overhead
When you exceed 100 questions per month on the Cloud tier
When you require enterprise-grade support and SLAs

Watch out for:

LLM costs are separate (you pay OpenAI/Anthropic directly)
Self-hosting requires managing your own vector database like ChromaDB
Cloud tier limits are based on query volume, not user seats

Developer-centric pricing that favors open-source adoption with a low-friction managed path for scaling.

Pros & Cons

Strengths

High accuracy for complex schemas
By using RAG to retrieve relevant DDL and documentation, it handles joins and niche business logic better than generic prompts.
Data privacy and security
Vanna only sends metadata and schemas to the LLM, while the actual data stays in your database and is processed locally.
Highly extensible for developers
The Python-first approach makes it easy to integrate into existing workflows, CI/CD pipelines, or custom web apps.
Cost-effective scaling
The open-source nature allows you to run the framework for free, paying only for your LLM tokens and vector store usage.

Weaknesses

Significant setup effort
Unlike 'chat with your data' SaaS tools, Vanna requires manual training on DDL and documentation to reach high accuracy.

Affects: Small teams looking for instant results
Requires Python proficiency
There is no GUI for the initial configuration; everything from database connection to training is handled via Python code.

Affects: Non-technical analysts
Performance depends on documentation quality
If your database schema is messy and lacks clear documentation or 'Golden SQL' examples, the output quality drops significantly.

Affects: Organizations with legacy or poorly maintained databases

Real User Sentiment

Users generally praise Vanna for its transparency and the control it gives over the RAG process, though some find the initial 'training' phase tedious.

Users tend to like

The ability to use 'Golden SQL' to force correct answers
Modular design that doesn't lock you into one LLM
Privacy-first approach where data never leaves the VPC
Active open-source community and frequent updates

Users commonly complain about

Steep learning curve for those not comfortable with Python
Initial training can be time-consuming for large schemas
Error messages can sometimes be cryptic when the LLM fails

Recurring tradeoffs

You trade 'ease of setup' for 'long-term accuracy' compared to SaaS competitors.

Happiest users

Data engineers building internal self-service analytics tools for their companies.

Often frustrated

Business analysts who expected a web-based tool they could set up without writing code.

Use Cases

Internal Analytics

Building a Slack bot that lets executives ask sales questions in plain English.

Customer-Facing Dashboards

Adding a 'search your data' feature to a SaaS application using Vanna as the backend.

Data Migration

Using Vanna to help map and query data during complex database transitions.

Prototyping

Quickly building a Streamlit app to demonstrate data insights to stakeholders.

Local Data Analysis

Querying sensitive healthcare or financial data using a local LLM via Ollama.

Frequently Asked Questions

Is Vanna AI free to use?

Yes, the core Vanna framework is open-source (MIT license) and free to use. You only pay for the LLM tokens (e.g., OpenAI API costs) and any infrastructure you use to host your vector database. They also offer a Vanna Cloud service with a free tier for up to 100 queries per month.

How does Vanna compare to LangChain for SQL?

While LangChain is a general-purpose AI framework, Vanna is specifically optimized for SQL. Vanna's RAG implementation is more focused on database-specific metadata and 'Golden SQL' pairs, which typically results in higher SQL accuracy for complex schemas compared to LangChain's more generic SQL agents.

Does Vanna see my actual database data?

No. Vanna only processes your database schema (DDL), documentation, and sample queries. When a query is generated, it is executed locally on your infrastructure. The actual row-level data is never sent to Vanna or the LLM provider unless you explicitly configure it to do so for summarization.

Which databases does Vanna support?

Vanna supports any database that has a Python connector. It has built-in support for popular warehouses like Snowflake, BigQuery, and Redshift, as well as relational databases like Postgres, MySQL, and SQLite.

Can I use Vanna with local LLMs?

Yes, Vanna is modular and supports local LLMs through integrations like Ollama. This is a popular choice for organizations with strict data privacy requirements who want to keep the entire text-to-SQL pipeline on-premises.

What is 'Golden SQL' in Vanna?

Golden SQL refers to a set of verified, correct SQL queries paired with their natural language equivalents. By adding these to Vanna's training data, you provide the model with high-quality examples to follow, which significantly improves accuracy for complex or non-obvious queries.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2023

Stage

Bootstrapped

Total Raised

Bootstrapped

Latest Round

—

Vanna AI is a bootstrapped company that has not raised any external venture capital funding. [1, 2, 4] The company is self-funded and has focused on building an open-source community around its product.

Full funding report high confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 0
Global rank: —
Snapshot: May 2026
Traffic trend: Surging