AI analytics assistants · Canner (Wren AI)

Wren AI

Open-source 'GenBI' engine that gives AI agents a governed semantic layer (MDL) for turning questions into SQL against a warehouse.

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Wren AI is an open-source semantic-layer engine, built on Apache DataFusion, that lets an organization define its own Modeling Definition Language (MDL) describing tables, joins, and business metrics, so a question in natural language is translated into SQL against that defined context rather than raw, undocumented schema. The open-source core ships as a CLI and context engine with no UI, aimed at developers building their own agents or bringing their own LLM. Wren AI Cloud adds a hosted UI, dashboards, an embeddable API, and dbt integration, billed on a monthly credit allowance; Enterprise Plus packages the full platform for self-hosting inside a customer's own infrastructure, including air-gapped deployments, with session-based licensing. Because answer quality depends on the MDL a team defines, results are only as good as that semantic model.

At a glance

Vendor Canner (Wren AI)
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (AGPL-3.0)
Best for Engineering teams that want an open-source, self-hostable semantic layer under their AI agents rather than a closed SaaS.

Pricing

Free open-source CLI/engine with no UI; hosted Cloud plans are credit-metered monthly subscriptions, and self-hosted Enterprise Plus is custom-quoted with session-based licensing.

Plan Price Notes
Free (Cloud) $0/month 20 monthly credits, plus 80 bonus credits for first 14 days; 2 projects, 2 members, 10 tables/project
Essential Cloud $179/month, billed annually 13,200 annual credits; unlimited dashboards, projects, and members; embedded AI API; dbt integration; basic RBAC
Enterprise Cloud $559/month, billed annually 24,000 annual credits; row/column-level data control; advanced RBAC, audit logs, custom LDAP/AD, OIDC
Enterprise Plus (self-hosted) Custom Session-based licensing, ~5 active users per concurrent session; on-prem/air-gapped, SSO, SCIM 2.0

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Open-source MDL semantic layer defining tables, joins, and metrics
  • Natural-language-to-SQL via a governed context layer, not raw schema
  • Rust/Apache DataFusion query engine, 20+ data source connectors
  • Agentic and classic query modes
  • dbt integration for existing model definitions
  • Embeddable AI API for building custom interfaces
  • Self-hosted deployment for on-prem or air-gapped environments

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Zenlytic Governed semantic layer and AI data analyst that lets Claude, ChatGPT, Slack, or its own app query a company's metrics consistently. Vanna AI Open-source RAG framework that trains an LLM on a database's schema and past queries to turn questions into SQL. Querio AI data platform built around version-controlled 'analytics as code' notebooks that answer questions against a defined context layer.

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Terms to know

Semantic layer A layer that defines business metrics and dimensions once, centrally, so every tool and report uses the same numbers. Text-to-SQL Technology that translates a plain-language question directly into a runnable SQL query against a database. Natural language query Asking a data question in plain language and getting an answer without writing code or a query language. dbt (data build tool) An open-source tool from dbt Labs that lets analytics teams write data transformations as version-controlled, tested SQL. Query engine The software component that parses, plans, and executes queries against one or more data sources.

Related guides

How to choose an AI analytics assistant AI analytics assistants are only as accurate as the context they're given — choose by what they query and how that context is governed, not the demo. How to use AI for data analysis safely Text-to-SQL and AI data assistants can speed up analysis or produce fluent wrong answers — the difference is what sits between the model and your data.