Vanna AI

Vanna AI is an open-source and commercial AI agent framework designed to help users interact with databases through natural language. Its primary purpose is to turn questions written in ordinary language into SQL queries, execute those queries against connected databases, and return results through tables, charts, and natural-language explanations. The platform is designed for organizations that want to make business data accessible without requiring every employee to have advanced SQL knowledge.

Vanna 2.0 expands the original text-to-SQL framework into a production-oriented, user-aware AI agent framework. It connects large language models to databases and tools while adding identity management, permissions, agent memory, streaming responses, audit logging, and row-level security. This makes the platform suitable not only for individual data-analysis experiments but also for applications where multiple users need controlled access to organizational data.

A central capability of Vanna is its natural-language-to-SQL workflow. A user can ask a question such as how sales performed during a particular period or which products generated the most revenue. The AI agent interprets the request, determines which database information is relevant, generates SQL, executes the query, and returns the result. Vanna 2.0 can stream the process through its web interface, including data tables, charts, SQL for authorized users, and natural-language summaries.

Vanna uses agent memory to improve how it handles recurring questions. Successful interactions can be stored as tool-use patterns, allowing the agent to retrieve relevant previous SQL queries and adapt them to new questions. This approach helps the system learn an organization’s database schema, business terminology, query patterns, and business logic over time rather than relying only on the general knowledge of an underlying language model.

The platform is designed to work with multiple large language model providers. Current Vanna 2.0 documentation provides integrations for providers including Anthropic, OpenAI, Azure OpenAI, Google Gemini, and Ollama, while the broader framework supports additional providers such as AWS Bedrock and Mistral. This provider flexibility allows developers to choose models based on their application requirements and deployment environment.

Database connectivity is another major part of Vanna AI. Current documentation provides integrations for PostgreSQL, MySQL, SQLite, Snowflake, BigQuery, and other database systems. Vanna can also be extended with custom database connectors, allowing developers to connect databases that are not included among the built-in integrations.

Vanna 2.0 places particular emphasis on security and user-aware access. User identity can flow through the agent, tool execution, and SQL process. Developers can define access groups and permissions for tools, while row-level security can restrict database results according to the user making the request. Audit logs and rate limiting can also be incorporated into production deployments.

The framework also includes a pre-built web component called <vanna-chat>. Developers can embed this interface into websites and applications and connect it to their own backend. The component supports streaming tables, charts, SQL results, and summaries and can work with existing authentication systems, including cookies and JWT-based authentication. Vanna supports FastAPI and Flask-based application architectures and can also be integrated into React, Vue, or plain HTML applications.

Vanna is not limited to SQL generation. Its agent architecture can be extended with custom tools, allowing developers to give agents additional capabilities beyond database querying. Visualization, data export, custom functions, authentication, lifecycle hooks, logging, and other tools can be incorporated into an application.

Deployment flexibility is another important characteristic. Vanna can be self-hosted using its open-source framework or used through Vanna Cloud. Enterprise customers can also receive deployment support for private cloud, VPC, or on-premises environments. The platform therefore caters to developers who want complete control as well as organizations that prefer a managed service.

The open-source Vanna framework is released under the MIT license, providing developers with considerable freedom to inspect, modify, and integrate the technology. The original Vanna repository was archived in March 2026 as the project moved toward its newer Vanna 2.0 architecture, while the Vanna 2.0 framework continues to provide the current agent-based approach.

Vanna AI is particularly useful for data analytics applications, internal business intelligence tools, customer-facing data interfaces, multi-tenant SaaS products, and enterprise applications that need natural-language access to structured data. Its combination of text-to-SQL, agent memory, database connectivity, authentication, permissions, visualization, and deployment flexibility makes it more of a developer framework for building AI-powered data applications than a simple chatbot.

Pricing

Vanna AI currently offers paid hosted plans alongside its open-source framework. The Explorer plan costs $50 per month and includes up to 20 questions per day, API access, administrative features, and same-day email support. The Team plan costs $500 per month and increases the limit to 300 questions per day while adding setup support and same-day live support. Enterprise pricing is customized and provides unlimited scale, on-premises deployment support, SAML SSO, an administrative API, advanced integrations, and custom work. Annual subscriptions receive a 20% discount.

Review

Vanna AI is a strong option for developers and organizations that want to build natural-language interfaces for databases. Its biggest advantage is that it combines text-to-SQL generation with an actual application framework, allowing teams to control authentication, permissions, tools, memory, database access, and the user interface.

Vanna 2.0 is particularly notable for its focus on multi-user and enterprise deployments. Row-level security, user-aware tool execution, audit logging, rate limiting, and configurable access groups address issues that can become difficult when moving an AI data prototype into production.

Its open-source foundation is another advantage. Developers can self-host Vanna, select their own LLM provider, connect their preferred database, and customize the agent instead of being locked into a single hosted AI service.

The main limitation is that Vanna is primarily a developer-oriented framework. Building a complete production application still requires Python development, database configuration, authentication, deployment, and potentially additional infrastructure. Users looking for a ready-made business intelligence dashboard may find dedicated BI platforms easier to deploy.

The accuracy of generated SQL also depends heavily on the quality of database context, schema information, business definitions, and previous examples available to the agent. Vanna’s agent memory and retrieval approach are designed to address this, but organizations still need to configure their data environment carefully.

For developers building AI-powered analytics applications, database chat interfaces, or secure enterprise data agents, Vanna AI offers a flexible combination of open-source development and managed enterprise capabilities.