Supaboard vs Wren AI
Supaboard vs Wren AI on price, AI analysts, deep reasoning, generated dashboards, MCP, connectors and accuracy — every figure sourced to Wren AI's own pages.
Which to choose
Choose Supaboard when you want alerts, scheduled reports and questions that span more than one source.
Choose Wren AI when you want an open-source text-to-SQL layer you can self-host, over one data source per project.
Why choose Supaboard over Wren AI
Supaboard is one agent for the whole analyst job. Wren AI's own docs describe a text-to-SQL layer that stops at a single query against a single connected source, answered on demand, with no alerts, no scheduled delivery and no Python. Supaboard combines every connected source in one question, sends the scheduled report, keeps the dashboard live and works out why a number moved, as one system rather than a query tool with a chat window.
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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 priced per seat ($19 to $249 a month, or $16 to $208 billed annually). Each paid seat includes a weekly AI allowance. When it runs out, new AI requests pause until the reset, or you can add credits. Extra storage is billed separately.
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 Pro and Max modes are 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 question 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 answers in Slack and, through its MCP server, in Claude, and its MCP support runs both directions: it exposes an MCP server and reads other MCP servers, including apps 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, a Supaboard agent can hold several of its 12 databases and warehouses at once and combine them in a single answer, alongside any tool with an MCP server, read live.
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 combining 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 its Pro and Max modes and cross-source querying close that gap.
Frequently asked questions
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 at $0.10/credit. Seats are unlimited, so cost scales with query volume. Supaboard has a free plan, and paid plans run $19 to $249 per editor a month ($16 to $208 billed annually). Each paid seat includes a weekly AI allowance. When it runs out, new AI requests pause until the reset, or you can add credits. Extra storage is billed separately.
Can Supaboard connect to the same data Wren AI is already querying?
Yes. Supaboard connects on its own 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 moving data anywhere.
What does Supaboard 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 PDF, PowerPoint or Excel reports, alerts on conditions you set, and answers that span every connected source in one question. Wren AI doesn't document any of these 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 writes and version-controls to define models, metrics and relationships. In Supaboard, the equivalent is the rules, knowledge and dropped files that shape the agents Chat answers from. It's less SQL and schema authoring and more upkeep of business rules, but it still takes some maintenance.
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. Supaboard receives no platform data; inference follows the model endpoint you configure. Wren AI can also run fully self-hosted, since its core is open source (Apache 2.0), and a paid Enterprise Plus tier adds SSO/SCIM and air-gapped options. This is a real overlap: teams that specifically want to own their text-to-SQL stack may prefer Wren AI's self-hosted path for that reason alone.
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