Vanna AI is an open-source SQL Agent framework that transforms natural language into SQL queries. It connects LLMs with databases, enabling non-technical users to query data independently. The Tool Memory mechanism learns from successful interactions, making the system smarter over time.




The fundamental challenge facing enterprises today is the disconnect between those who understand the data and those who need it. Business analysts, customer service representatives, and department managers frequently require immediate data insights, yet they lack the technical skills to write SQL queries. Traditionally, this gap has been bridged by data teams acting as intermediaries—a process that introduces delays, creates bottlenecks, and diverts skilled analysts from high-value work. Meanwhile, granting direct database access to non-technical users presents significant security risks and makes compliance auditing nearly impossible.
Vanna AI addresses these challenges through an open-source SQL Agent framework that enables non-technical users to query databases using natural language. Built on large language model technology, Vanna acts as an intelligent intermediary between users and your existing data infrastructure. When a user asks "What were our top-selling products last quarter?" in plain English, Vanna's LLM-powered engine understands the intent, generates the appropriate SQL query, executes it against your database, and returns actionable results—all without the user needing to understand database schemas or query syntax.
The platform has gained traction among industry leaders, with adoption by companies including Apple, NVIDIA, Tesla, and Samsung. Microsoft has officially integrated Vanna with Azure SQL Database, with Azure's product management team providing direct endorsement. This combination of enterprise adoption and official platform recognition positions Vanna as a trusted solution for organizations seeking to democratize data access while maintaining security and compliance standards.
Vanna's primary capability transforms how users interact with databases. The system leverages large language models to comprehend user intent from natural language queries, then generates precise SQL statements based on your specific database schema. This isn't simple keyword matching—Vanna understands table relationships, column contexts, and business logic to produce accurate queries. Non-technical users gain self-service access to data insights without dependency on data teams, dramatically reducing turnaround time from days or weeks to seconds.
A distinguishing feature of Vanna is its Tool Memory mechanism. The system employs a vector database to store successful interaction patterns, enabling it to learn from each query. When similar questions arise in the future, Vanna retrieves relevant historical examples, resulting in faster and more accurate responses. This self-improving capability means the system becomes progressively smarter without requiring manual training or model retraining—a significant advantage over static query builders.
Security remains paramount in enterprise data environments. Vanna implements permission flows that propagate throughout the entire system. When generating SQL, the LLM automatically applies appropriate permission filters based on the authenticated user's role and data access rights. This architecture supports multi-department data isolation scenarios common in large organizations, ensuring each user sees only what they're authorized to access.
Vanna provides unified database abstraction supporting SQLite, PostgreSQL, MySQL, Snowflake, BigQuery, and additional database platforms. This flexibility allows organizations with heterogeneous data sources—common in enterprises running ERP, CRM, and financial systems—to create a unified querying layer across all systems.
Organizations can select from multiple language model providers based on their specific requirements: OpenAI (GPT models), Anthropic (Claude), Google Gemini, or Ollama for local on-premises deployment. This choice enables balancing privacy requirements, cost considerations, and performance needs without vendor lock-in.
The Admin Dashboard provides real-time metrics and distributed tracing capabilities. Operations teams can monitor agent behavior, track query patterns, identify bottlenecks, and optimize performance systematically.
Complete activity tracking supports compliance requirements in regulated industries such as finance and healthcare. Every query and operation is logged with full attribution, enabling comprehensive audit trails and regulatory reporting.
Business users often need quick answers to operational questions—"What's our inventory turnover rate?" or "Which customers have overdue payments?" Previously, these requests required submitting tickets to data teams, waiting for prioritization, and accepting delays. With Vanna, business personnel pose questions in natural language and receive immediate results. Data teams benefit from reduced ad-hoc request volume, enabling focus on strategic analysis and complex modeling.
Enterprises typically operate multiple disconnected systems: ERP for supply chain, CRM for customer relationships, financial systems for accounting. Each system maintains its own database, making holistic reporting difficult. Vanna connects to all systems simultaneously, providing a unified query interface. Users asking "Show me revenue by customer region for Q3" receive results that aggregate across all platforms, breaking down data silos.
Direct database access for business users introduces substantial risk—accidental or intentional data exposure, compliance violations, and audit trail gaps. Vanna acts as a controlled intermediary, returning only permission-filtered results. Financial institutions and healthcare organizations—subject to SOX, HIPAA, and similar regulations—benefit from this security layer while maintaining user productivity.
Organizations can build dedicated AI agents for customers to query their own data—order status, account balances, appointment history. This reduces customer support ticket volume while improving response times. The agent operates within strict data boundaries, ensuring customers access only their own information.
New employees often require SQL skills to perform analysis roles. The learning curve can be steep, delaying productive contribution. Vanna enables new team members to begin querying data immediately using natural language, building intuition about data structures before tackling SQL syntax. This approach creates smoother onboarding and faster time-to-productivity.
Organizations in regulated industries (finance, healthcare) should prioritize audit logging capabilities when evaluating Vanna. Enterprises with strict data sovereignty requirements should consider the Enterprise deployment option for on-premises or private cloud installations.
Before deploying Vanna, ensure you have: a Python environment (3.8 or higher), database connection credentials with read-only access, and an API key from your chosen LLM provider (OpenAI, Anthropic, or Google).
Install Vanna via pip:
pip install vanna
Alternatively, clone the GitHub repository for the latest development version:
git clone https://github.com/vanna-ai/vanna.git
cd vanna && pip install -e .
The following code demonstrates a complete query workflow:
from vanna.openai import OpenAI_Chat
from vanna.vannadb import VannaDB_VectorStore
import os
# Configure your LLM
vn = OpenAI_Chat(api_key=os.environ["OPENAI_API_KEY"], model="gpt-4")
# Connect to your database
vn.connect_to_sqlite(" Chinook.sqlite")
# Train on your schema
vn.train()
# Query using natural language
question = "Which artists have the most albums?"
sql = vn.generate_sql(question)
results = vn.run_sql(sql)
print(f"Question: {question}")
print(f"SQL: {sql}")
print(f"Results: {results}")
For production deployments handling sensitive data, consider running Ollama locally to keep all data within your infrastructure. This approach eliminates external API calls and ensures complete data privacy. When deploying for multiple users, configure fine-grained access controls from the outset to establish proper permission boundaries.
Production environments handling sensitive data should leverage Ollama for local LLM deployment. This ensures data never leaves your infrastructure while maintaining natural language query capabilities.
Vanna's pricing philosophy provides an open-source foundation with paid enhancements for enterprise requirements. This structure enables individual developers to explore the platform freely while supporting organizational scale.
| Plan | Price | Daily Queries | Key Features | Target Users |
|---|---|---|---|---|
| Explorer | $50/month | 20 questions/day | Core SQL Agent, Email support, Community access | Individual developers, small projects |
| Team | $500/month | 300 questions/day | Priority processing, Team management, Slack support | Growing teams, departmental deployment |
| Enterprise | Custom | Unlimited | SAML SSO, On-premises deployment, Dedicated support, SLA guarantees | Large organizations, regulated industries |
Explorer ($50/month): Designed for individual exploration and small-scale implementations. Includes the core SQL Agent functionality, access to community support channels, and email assistance. Suitable for evaluating the platform's fit for your use case.
Team ($500/month): Supports team collaboration with higher query limits and priority processing. Includes team management features, direct Slack support for faster issue resolution, and expanded storage for Tool Memory.
Enterprise (Custom): Tailored to large organizations requiring unlimited queries, SAML Single Sign-On for enterprise identity management, on-premises or private cloud deployment options, dedicated support representatives, and service level agreements. Pricing depends on deployment complexity, SLA requirements, and support tier.
All paid plans offer a 20% discount for annual billing. Switch to annual payment during checkout or contact the sales team to apply the discount to your account.
All Vanna tiers share the same core framework, enabling seamless upgrades without code modifications. Organizations can begin with Explorer and progress to Team or Enterprise as requirements evolve.
Existing open-source deployments can upgrade to any paid tier without rewriting integration code. The same Vanna core powers all plans, ensuring compatibility as your requirements grow.
Daily limits reset every 24 hours. When exceeded, queries are rate-limited until the next reset period. You can upgrade your plan at any time to increase limits.
Yes. All plans share the same core framework, enabling seamless migration to paid tiers without code changes. Simply upgrade your account to access additional features and higher limits.
Enterprise pricing is customized based on your deployment requirements: infrastructure type (on-premises, VPC, private cloud), SLA guarantees, support level, and user count. Contact the sales team for a tailored quote.
Yes, annual billing provides a 20% discount across all paid plans. Select annual payment during checkout or reach out to sales to apply this to your account.
Vanna supports Anthropic (Claude), OpenAI (GPT), Google Gemini, and Ollama for local on-premises deployment. Each provider offers different capabilities for privacy, cost, and performance optimization.
Vanna supports major SQL databases including PostgreSQL, MySQL, Snowflake, BigQuery, and SQLite. Additional database support is continuously expanding through community contributions.
Vanna AI is an open-source SQL Agent framework that transforms natural language into SQL queries. It connects LLMs with databases, enabling non-technical users to query data independently. The Tool Memory mechanism learns from successful interactions, making the system smarter over time.
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