# Supaboard vs Wren AI > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Wren AI's own pages. HTML version: https://supaboard.ai/compare/wren-ai ## At a glance | Row | Supaboard | Wren AI | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Free (OSS) / $179-$559/mo** — Cloud is a flat org fee plus usage credits, unlimited seats, not per-seat pricing (https://getwren.ai/pricing, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **Yes** — built from the ground up as a text-to-SQL GenBI agent with a semantic modeling layer (https://getwren.ai, checked 2026-09-10) | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Partial** — answers business questions via governed text-to-SQL grounded in MDL definitions and stored business knowledge, not a broader analysis agent (https://docs.getwren.ai/oss/concepts/what_is_mdl, checked 2026-09-10) | | Deep reasoning | **Yes** — Deep Dive answers why a number moved and what to do next, not only what it is | **No** — own architecture docs describe single-question retrieval, prompting and SQL validation, not root-cause or forecasting reasoning | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **Yes** — GenBI Apps builds interactive dashboards and reports from a prompt on Cloud Agentic projects (https://docs.getwren.ai/cp/guide/agentic/querying/genbi-apps, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Yes** — GenBI Apps or Classic Dashboards generated from natural-language prompts (https://docs.getwren.ai/cp/guide/agentic/querying/genbi-apps, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **No** — no documented end-to-end automation chaining detection, analysis, export and routing | | Alerts | **Yes** — Threshold and anomaly alerts described in plain English, which analyse the change rather than only announce it | **No** — no threshold or anomaly alerting feature found in Wren AI's own docs | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **No** — GenBI reports are built on demand in chat; no scheduled delivery via email, Slack, or file export is documented | | Cross-source data query | **Yes** — One question spanning every connected source | **No** — each Wren AI project connects to one data source at a time, confirmed in its own connector docs (https://docs.getwren.ai/oss/guide/connect/overview, checked 2026-09-10) | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **No** — querying is SQL-only across all documented surfaces; no Python execution feature is described | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Partial** — ships in Slack and Microsoft Teams (Cloud, Interactive Mode only) and a Claude.ai connector on Enterprise Cloud; no ChatGPT integration found (https://docs.getwren.ai/cp/guide/integrations/slack, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Partial** — serves an MCP server for clients like Cursor and Claude Code (OSS CLI or hosted on Enterprise Cloud); no evidence it consumes other MCP servers (https://docs.getwren.ai/oss/guides/mcp, checked 2026-09-10) | | Query bench | **Yes** — SQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approve | **No** — SQL is AI-generated and shown read-only after the fact ("View Full SQL"), not an editable schema-aware workbench (https://docs.getwren.ai/cp/guide/agentic/querying/wren_sql, checked 2026-09-10) | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **MDL semantic layer** — version-controlled business models, metrics and relationships in files, plus a separate knowledge base of instructions (https://docs.getwren.ai/oss/concepts/what_is_mdl, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **18-20** — BigQuery, Snowflake, Databricks, Redshift, Postgres, MySQL, SQL Server, Oracle, ClickHouse, Trino, Athena, Spark and more; one data source per project (https://docs.getwren.ai/cp/guide/connect/overview, checked 2026-09-10) | | Answer accuracy | **97.8%** — On LegendEHR's tuned agent in production, with a confidence score on every answer | **None** — no accuracy or correctness benchmark is published; correctness is argued architecturally (MDL, memory, validation), not measured | Last checked 2026-09-10. Wren AI pricing page: https://getwren.ai/pricing ## Why choose Supaboard over Wren AI Wren AI is a genuinely strong governed text-to-SQL layer, but every one of its own docs confirms it stops at a single query against a single connected source, answered on demand with no alerts, no scheduled delivery, and no Python. Supaboard is built for the same company to ask one question across all 700+ connected sources, get a scheduled report or a live dashboard back, and have an agent explain why a number moved - as one system, not a query tool with a chat window bolted on. ## Supaboard vs Wren AI, in our own words Wren AI is an open-source "GenBI" engine: a text-to-SQL agent grounded in a semantic layer it calls MDL (Modeling Definition Language), which turns raw warehouse schemas into version-controlled business definitions so the agent generates SQL against approved models and metrics instead of guessing at joins. The open-source core is Apache 2.0, free, and self-hosted, built for one engineer or agent working through a CLI and SDK. Wren AI Cloud adds a hosted web UI, team features, and a credit-metered price on top: Free, Essential Cloud at $179/month billed annually, Enterprise Cloud at $559/month billed annually, or a custom self-hosted Enterprise Plus contract. The question worth asking is whether a governed SQL layer built for engineers is the same thing as an analytics system built for a whole company to run on. ### Where the cost actually lands Wren AI Cloud bills a flat org-wide credit pool, not per seat: Essential and Enterprise Cloud include unlimited projects and members, but usage draws down annual credits at $0.10 each once you exceed the rollover cap, so cost scales with query volume rather than headcount. That's cheap for a small team of heavy SQL users and can get unpredictable as a whole org starts asking questions through it. Supaboard is flat per seat ($99/mo, or $83 billed annually) with usage credits already inside the seat price, so cost scales with people, not query volume. ### What is actually different Wren AI's actual strength, verified on its own docs, is governed text-to-SQL: MDL defines models, relationships, and metrics as files, so the agent reuses approved calculations and joins rather than inventing them per question. That's a real, developer-friendly idea, especially for a team that wants full ownership of its semantic layer in git. But the depth stops at the query. Wren AI's own architecture page describes correctness as retrieval, prompting, and validation of a single SQL answer - there's no root-cause or forecasting mode for "why did this metric move," which is what Supaboard's Deep Dive mode is built for. Wren AI's GenBI Apps can build a dashboard or report from a prompt on Cloud Agentic projects, comparable to Supaboard's one-prompt dashboards and data apps - but Wren AI's own connector docs confirm each project binds to one data source, so a GenBI App can't span sources the way a Supaboard dashboard or Deep Dive can, and there's no published support for alerts, scheduled report delivery, or Python execution anywhere in Wren AI's docs - querying is SQL-only, on-demand, and single-source. On chat surfaces, Wren AI ships real integrations: Slack and Microsoft Teams (Cloud, Essential plan and up, Interactive Mode only, no RLS support yet) and a Claude.ai connector gated to Enterprise Cloud. It also ships an MCP server (`wren serve mcp` in open source, or hosted MCP on Enterprise Cloud) that tools like Cursor and Claude Code can connect to - but that MCP server only exposes Wren's own tools outward; nothing in its docs shows it consuming other MCP servers as a client. Supaboard ships natively in Slack, Teams, Claude, ChatGPT, and Cursor, and its MCP support runs both directions: it exposes an MCP server and reads other MCP servers, including sources like Notion. On the query surface itself, Wren AI shows generated SQL read-only after the fact ("View Full SQL") rather than offering an editable, schema-aware SQL workbench - Supaboard's query bench pairs SQL and Python in one editor, with AI-proposed edits shown as a diff you approve. And where Wren AI connects to roughly 18-20 databases and warehouses, one at a time per project, Supaboard connects to 700+ sources and can span every connected source in a single question. ### What to test if you trial both If you trial both, don't just check whether each tool writes correct SQL for a known question - check what happens when the honest business question requires more than one source, or more than one query. Ask Wren AI something that needs joining data across two of its connections, or ask it why a number moved rather than what the number is, and see whether the answer requires you to go build the follow-up query yourself. Then ask Supaboard the same two questions and see whether Deep Dive and cross-source querying actually close that gap, or just claim to. ## FAQ ### How does Wren AI's pricing compare to Supaboard's? Wren AI's open source core is free and self-hosted. Wren AI Cloud is a flat org-wide fee plus usage credits: Free, Essential Cloud at $179/month (billed annually), or Enterprise Cloud at $559/month (billed annually), with extra usage billed at $0.10/credit - unlimited seats included, so cost scales with query volume. Supaboard is $99/seat/month (or $83/seat/month billed annually), with usage credits already inside the seat price, so cost scales with headcount instead. ### Can Supaboard connect to the same data Wren AI is already querying? Yes. Supaboard connects independently to warehouses and databases like Snowflake, BigQuery, Redshift, Databricks, and Postgres - the same categories Wren AI supports - so running Supaboard alongside or instead of Wren AI doesn't require migrating data anywhere. ### What does Supaboard actually produce that Wren AI doesn't? Wren AI's outputs are SQL answers and charts in chat, plus on-demand GenBI Apps within a single connected source. Supaboard adds live dashboards and data apps generated on your own design system, scheduled reports delivered as PDF/PowerPoint/Excel, threshold and anomaly alerts, and answers that can span every connected source in one question - none of which are documented as Wren AI capabilities today. ### Do we still need someone maintaining a semantic model with Supaboard, the way Wren AI needs someone maintaining MDL? Some ongoing curation, yes, on both sides. Wren AI's MDL is files your data team authors and version-controls to define models, metrics, and relationships. Supaboard's equivalent is rulesets, knowledge, and dropped files that shape the agent's answers - it's less SQL/schema authoring and more business-rule upkeep, but it isn't zero-maintenance either. ### Can Supaboard self-host or run in our own VPC, like Wren AI's open source edition? Yes, on Supaboard's Enterprise plan: single-tenant containers, your own model provider keys, and no data egress. Wren AI can also run fully self-hosted since its core is open source (Apache 2.0), with a paid Enterprise Plus tier adding SSO/SCIM and air-gapped options - it's a genuine overlap, and teams that specifically want to own their own text-to-SQL stack may prefer Wren AI's self-hosted path for that reason alone. ## Other comparisons - [Supaboard vs Microsoft Power BI](https://supaboard.ai/compare/power-bi) - [Supaboard vs Metabase](https://supaboard.ai/compare/metabase) - [Supaboard vs ThoughtSpot](https://supaboard.ai/compare/thoughtspot) - [Supaboard vs Looker](https://supaboard.ai/compare/looker) - [Supaboard vs Qlik](https://supaboard.ai/compare/qlik) - [Supaboard vs Domo](https://supaboard.ai/compare/domo) - [Supaboard vs Sigma](https://supaboard.ai/compare/sigma) - [Supaboard vs Omni](https://supaboard.ai/compare/omni) - [Supaboard vs Zenlytic](https://supaboard.ai/compare/zenlytic) - [Supaboard vs WisdomAI](https://supaboard.ai/compare/wisdom-ai) - [Supaboard vs TextQL](https://supaboard.ai/compare/textql) - [Supaboard vs Sundial](https://supaboard.ai/compare/sundial) - [Supaboard vs Basedash](https://supaboard.ai/compare/basedash) - [Supaboard vs Hex](https://supaboard.ai/compare/hex) - [Supaboard vs Lightdash](https://supaboard.ai/compare/lightdash) - [Supaboard vs Julius AI](https://supaboard.ai/compare/julius-ai) - [Supaboard vs Bruin](https://supaboard.ai/compare/bruin) - [Supaboard vs Snowflake Cortex Analyst](https://supaboard.ai/compare/snowflake-cortex) - [Supaboard vs Databricks Genie](https://supaboard.ai/compare/databricks-genie) - [Supaboard vs Amazon QuickSight](https://supaboard.ai/compare/amazon-quicksight) - [Supaboard vs ChatGPT](https://supaboard.ai/compare/chatgpt) - [Supaboard vs Claude](https://supaboard.ai/compare/claude) --- - Site index for agents: https://supaboard.ai/llms.txt - Sitemap: https://supaboard.ai/sitemap.xml - Blog feed: https://supaboard.ai/blog/rss.xml - Product documentation: https://docs.supaboard.ai Any page on https://supaboard.ai is available as markdown: append `.md` to its path, or send `Accept: text/markdown`.