Vanna 2.0 vs Wren AI

Vanna 2.0 is an open-source SQL Agent framework that lets users query company systems of record using natural language. It supports multiple databases and LLM providers, enables multi-turn conversations, and includes access controls, helping organizations reduce time writing SQL and make data insights more accessible.

Wren AI is a governed data agent platform that gives both human teams and AI agents a single, consistent way to ask questions across enterprise data. Instead of treating text-to-SQL as a simple tool call, Wren runs GenBI agents in an isolated sandbox where it reasons over versioned context (MDL definitions, knowledge and skills) before generating SQL. The result can be rendered as charts, dashboards or structured JSON, with every query traceable and governed by row- and column-level policies. Wren's key differentiator is the context layer it calls "definitions you own." Teams define metrics, policies and semantic models in git, then share them with both natural-language chat interfaces and AI assistants such as Claude, ChatGPT and Gemini over MCP. Because both paths use the same context and policy layer, answers are consistent and auditable - Wren even fingerprints results so a human and an agent can confirm they are looking at the same answer. On the deployment side, Wren connects to 20+ databases in place with no ETL, and can run in Wren AI Cloud, your VPC, or fully air-gapped. The core context engine is open source, while commercial tiers add agentic dashboard mode, git sync and advanced governance. It is aimed at data teams and product/platform teams who need governed self-service analytics and a way to make AI agents safe to use on internal data.

• Open source framework
• Works with any LLM provider and database
• Multi-turn conversation support
• Access-controlled querying

• Same governed answer for human users and AI agents, reducing metric drift and policy violations.
• Broad database support with in-place querying - no ETL or data migration.
• Open-source core gives developers transparency, customizability and no vendor lock-in.
• Strong governance: row/column-level policies, audit logs and SOC 2.

• Setup likely requires integration and configuration
• No pricing or deployment details visible on the landing page
• May require careful access control setup for enterprise use

• Requires initial semantic modeling/onboarding to get accurate answers; MDL definitions and policies must be maintained.
• Advanced agentic dashboard mode, Git Sync and enterprise security features are locked behind the commercial platform.
• Homepage does not publish transparent pricing, so teams must contact sales for enterprise/usage-based quotes.
• Custom agent setup via MCP and embedding still requires engineering effort despite the plug-and-play integrations.