# Supaboard vs Bruin > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Bruin's own pages. HTML version: https://supaboard.ai/compare/bruin ## At a glance | Row | Supaboard | Bruin | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Free CLI + usage-based Cloud** — open-source CLI is free forever; Cloud free tier gives $100 credits and 50 AI tasks/mo before usage-based pricing kicks in (https://getbruin.com/, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **No** — core engine shipped as an open-source SQL/Python/R pipeline tool; the AI analyst, dashboards, and chat surfaces were added later as a Cloud layer on top | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Yes** — agent answers business questions in chat, shows the SQL it ran, and can act on live warehouse data (https://getbruin.com/ai-data-analyst/, checked 2026-09-10) | | Deep reasoning | **Yes** — Deep Dive answers why a number moved and what to do next, not only what it is | **Partial** — examples show cross-source validation across tools but no named multi-step 'why did this move' mode (https://getbruin.com/ai-data-analyst/, checked 2026-09-10) | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **Yes** — Bruin Data Apps turns a chat description into an operational app wired to live, governed warehouse queries (https://getbruin.com/blog/bruin-data-apps-vs-lightdash-retool-streamlit-hex/, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Yes** — full interactive dashboard with KPIs, charts, filters, and date pickers generated from one prompt (https://getbruin.com/dashboards, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **Partial** — shows real detect-then-act examples like an auto-paused overspending campaign, but no one-prompt workflow builder; 'workflow' in the open-source core means a pipeline DAG (https://getbruin.com/, checked 2026-09-10) | | Alerts | **Yes** — Threshold and anomaly alerts described in plain English, which analyse the change rather than only announce it | **Partial** — ships blocking pipeline/schema-change alerts plus example agent actions like auto-pausing campaigns, not a configurable plain-English threshold builder (https://getbruin.com/product/quality/, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Yes** — scheduled reports plus PDF, image, and chart exports are shipped delivery formats (https://getbruin.com/solutions/ceos, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **Yes** — one question can join unlimited connected sources in a single answer (https://getbruin.com/ai-data-analyst/, checked 2026-09-10) | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **Yes** — SQL, Python, and R are first-class in the transformation engine across connected warehouses (https://getbruin.com/product/transformation/, checked 2026-09-10) | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Slack, Teams + 6 more** — ships Slack, Microsoft Teams, Discord, Google Chat, WhatsApp, Telegram, email, and browser; no native Claude or ChatGPT surface, Cursor is dev-only via MCP (https://getbruin.com/, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Yes** — ships an MCP server so AI editors like Cursor and Claude Code can query warehouses and build pipelines through Bruin (https://getbruin.com/product/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** — primary interface is CLI plus YAML/SQL/Python asset files with a VS Code extension, not a hosted schema-aware editor (https://getbruin.com/docs/bruin/, checked 2026-09-10) | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **Yes** — a version-controlled glossary defines shared entity and column definitions; the agent can also pull context from Notion or Confluence over MCP (https://getbruin.com/learn/ai-data-analyst/improve-context/, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **100+** — 100+ sources and destinations via the open-source ingestr engine (https://getbruin.com/product/ingestion/, 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 percentage published; Bruin argues trust comes from shown SQL, lineage, and stable metric definitions instead of a number (https://getbruin.com/blog/how-do-i-know-the-ai-answer-is-correct/, checked 2026-09-10) | Last checked 2026-09-10. Bruin pricing page: https://getbruin.com/ ## Why choose Supaboard over Bruin Supaboard's agent is tuned on your own business rules and metric definitions from day one, with SQL shown beside every answer and a published 97.8% accuracy figure with a confidence score on each response — Bruin publishes neither a comparable accuracy number nor a self-serve alert or workflow builder, relying instead on marketing examples. And Supaboard's flat $99/seat pricing (usage included) is a fixed number where Bruin Cloud's usage-based pricing beyond its free tier has no public price list. ## Supaboard vs Bruin, in our own words Bruin is not the pure data-pipeline tool its open-source roots suggest. The Bruin CLI (Apache-2.0, self-hostable, a real ELT/orchestration framework: ingestion from 100+ sources, SQL/Python/R transforms, quality checks, lineage) is still there and still free. But getbruin.com has repositioned the whole product as "Your AI Data Team," and layered a managed Cloud product on top with an AI analyst, prompt-generated dashboards, "Data Apps," and chat delivery across eight channels. So the honest framing isn't "pipeline tool vs. BI tool" anymore. It's whether an AI-analyst layer built on top of a data-engineering product, and marketed mostly through example screenshots rather than a documented, priced, self-serve feature set, holds up against one built as the product from the start. ### Where the cost actually lands Bruin's CLI is free forever, and Bruin Cloud's free tier ($100 in compute credits plus 50 AI tasks a month, no card required) is a real way to kick the tires. Past that it's usage-based, metering compute and "AI tasks" separately, and getbruin.com publishes no price list for what happens once you're past the free tier. Supaboard is a flat $99/seat ($83 billed annually) with usage credits bundled into the seat, so the cost of the AI itself is fixed before anyone asks a question, not something you discover on an invoice. ### What is actually different Both products now put an AI agent in front of a warehouse, but they got there from opposite directions, and it shows in the details. **AI-native vs. AI-added.** Bruin's GitHub description still reads "build data pipelines with SQL and Python, ingest data from different sources, add quality checks" — that's the actual open-source core. The AI analyst, dashboards, and Data Apps are a Cloud layer added on top of that engine. Supaboard has no pipeline layer underneath; the agent is the entire product. **The AI analyst itself is real, not vaporware.** Bruin's AI analyst answers questions in Slack, Teams, and other chat surfaces, shows the SQL it ran, and can join across connected sources in one question (their own example: Meta Ads + TikTok Creative Center + Singular in a single query). That's a genuine overlap with Supaboard's core claim. Where it's thinner is multi-step reasoning: Bruin's examples show cross-source validation (checking AppsFlyer against RevenueCat and Firebase), but there's no named "why did this number move" mode comparable to Supaboard's Deep Dive. **Dashboards and Data Apps both exist and both look real.** "Full dashboard from one prompt" is on Bruin's own dashboards page, and Bruin Data Apps ("a small, interactive application you build by describing it in chat") is a shipped, documented feature, not a roadmap item. This is a closer match to Supaboard than the pipeline-tool assumption gave it credit for. **Alerts and workflows exist as examples, not as a configurable product.** Bruin's homepage shows a schema-change alert and an auto-paused overspending ad campaign ("$1,847 saved") as marketing examples of its "Acts" pillar. That's a real detect-then-act pattern, but there's no dedicated page describing a self-serve, plain-English threshold or anomaly builder the way there is for dashboards or data apps — and "workflow," in the open-source CLI, still primarily means a pipeline DAG, not a business automation. Automated reporting is more concrete: scheduled reports and PDF/image/chart delivery are explicitly listed outputs. **Python is a first-class citizen, arguably more so than Supaboard's.** SQL, Python, and R are all native to Bruin's transformation engine — that's inherited directly from its data-engineering roots. **Chat surfaces are broader but miss the AI-assistant channels.** Bruin ships Slack, Microsoft Teams, Discord, Google Chat, WhatsApp, Telegram, email, and browser. It does not ship a native Claude or ChatGPT surface, and Cursor only shows up as a developer integration via MCP, not as a place a business user asks questions. **MCP is real and documented.** Bruin ships its own MCP server so Cursor and Claude Code can query warehouses through Bruin's infrastructure — a genuine, dedicated product page, not a mention in passing. **The query bench is still a CLI, not a browser editor.** There's no claim anywhere on Bruin's site of a hosted SQL/Python editor with schema-aware autocomplete or AI-proposed diffs. The primary way to write Bruin logic is YAML/SQL/Python asset files, edited locally or through a VS Code extension. **Context holding is a real, named feature.** Bruin's "Glossary" is a version-controlled dictionary of business entities and attributes, and its AI analyst can also pull context from Notion or Confluence over MCP — a legitimate answer to how it keeps definitions consistent over time. **Connectors and accuracy.** Bruin publishes "100+ Sources and Destinations." On accuracy, Bruin explicitly argues against a single percentage, making the case instead that showing the SQL, the lineage, and a stable metric definition matters more than a claimed number — so there is no published accuracy figure to compare against Supaboard's 97.8%. ### What to test if you trial both If you're evaluating both, don't just compare the chat answer quality, since both now do that reasonably well. Test the parts that reveal which one is the actual product: ask each to build a threshold alert on a business metric in plain English and see whether that's a real, saved, editable object or a one-off example; ask for a scheduled report with a specific export format; and check whether the SQL/Python editing experience is a hosted, autocomplete-assisted bench or a set of local files. If your team also needs the ingestion and pipeline layer Bruin's CLI provides, the realistic evaluation isn't Bruin instead of Supaboard, it's Bruin's open-source core feeding a warehouse that Supaboard's agent then sits on top of. ## FAQ ### Is Bruin cheaper than Supaboard? The Bruin CLI is free and open-source (Apache-2.0). Bruin Cloud, where the AI analyst and dashboards live, has a free tier ($100 in compute credits plus 50 AI tasks a month) and then usage-based pricing with no published price list. Supaboard is a flat $99/seat/mo ($83 billed annually) with usage credits included, so you know the ceiling before you commit. ### Does Supaboard replace Bruin, or do they work together? They can work together. Bruin's open-source CLI handles ingestion, transformation, and quality checks upstream of a warehouse; Supaboard's agent sits on top of whatever warehouse you already have and answers business questions against it. If you're using Bruin purely for its Cloud AI-analyst layer, that's the part Supaboard competes with directly. ### What does Supaboard produce that Bruin's AI layer doesn't clearly show? A tuned agent built on your own business rules and metric definitions, a Deep Dive mode for multi-step 'why did this move' analysis, and a documented, self-serve threshold and anomaly alert builder in plain English. Bruin's alerting and 'workflow' automation are demonstrated through marketing examples rather than a dedicated, described product surface. ### If we already use Bruin's pipeline CLI, do we still need it alongside Supaboard? Yes, if you rely on it for ingestion, transformation, or quality checks. Supaboard doesn't move or transform data; it connects to sources you've already made queryable. Bruin's open-source core and Supaboard's agent layer solve different problems and aren't mutually exclusive. ### Can Supaboard self-host or run in our own VPC the way Bruin's CLI does? Yes, on the Enterprise plan: single-tenant containers, your own model provider keys, and no data egress. Bruin's CLI is self-hostable by default since it's open-source; Supaboard's equivalent is a paid Enterprise deployment option rather than a free self-hosted core. ## 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 Wren AI](https://supaboard.ai/compare/wren-ai) - [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 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`.