# Supaboard vs Databricks Genie > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Databricks Genie's own pages. HTML version: https://supaboard.ai/compare/databricks-genie ## At a glance | Row | Supaboard | Databricks Genie | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Usage-based (DBUs)** — No per-seat fee; billed via standard Databricks compute rates for the warehouse Genie queries run on (https://www.databricks.com/product/business-intelligence, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **No** — Genie is a feature added onto the existing Databricks Lakehouse/Unity Catalog platform, not a standalone AI-first product (https://docs.databricks.com/aws/en/genie/, 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 with generated SQL and charts, tuned with curated tables and example queries, not a dedicated business-rules agent (https://docs.databricks.com/aws/en/genie/, 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** — Genie asks clarifying questions within a conversation but has no dedicated root-cause or forecasting mode | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **No** — Databricks Apps are built by developers with Python frameworks like Dash, Gradio and Streamlit, not generated by Genie from a prompt (https://www.databricks.com/product/databricks-apps, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Partial** — AI-assisted authoring helps configure data and charts; not a full dashboard generated from a single prompt (https://docs.databricks.com/aws/en/dashboards/, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **No** — Alerts, dashboards and jobs exist as separate features you assemble yourself; no single-prompt detect-analyze-export-route builder | | Alerts | **Yes** — Threshold and anomaly alerts described in plain English, which analyse the change rather than only announce it | **Yes** — SQL Alerts run a query on a schedule and notify on email, Slack, Teams, webhook or PagerDuty when a condition is met (https://docs.databricks.com/aws/en/sql/user/alerts/, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Partial** — Dashboards can push scheduled PNG/PDF snapshots to a Slack channel; no PowerPoint/Excel export or agent-written report (https://docs.databricks.com/aws/en/ai-bi/admin/slack-subscriptions, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **No** — Genie only queries data already inside Unity Catalog; other sources must be ingested first via a separate tool (https://docs.databricks.com/aws/en/genie/, checked 2026-09-10) | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **No** — Genie spaces generate SQL only; Python lives separately in Databricks notebooks, not inside a Genie space (https://docs.databricks.com/aws/en/genie/set-up, checked 2026-09-10) | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **None native** — Genie can be added to Slack, Teams or Glean only by building the integration yourself against its API (https://docs.databricks.com/aws/en/genie/conversation-api, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Partial** — Managed MCP servers expose Genie/SQL/Unity Catalog to other agents, and external MCP servers can be registered, but Genie itself cannot be registered as an MCP service (https://docs.databricks.com/aws/en/generative-ai/mcp/external-mcp, checked 2026-09-10) | | Query bench | **Yes** — SQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approve | **Yes** — SQL editor has schema-aware autocomplete for columns, aliases and joins; Python/Scala/R live in notebooks (https://docs.databricks.com/aws/en/sql/user/sql-editor/write-queries, checked 2026-09-10) | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **Curated tables + example SQL + instructions** — Analysts add up to 30 tables/views, example SQL queries and functions, and written instructions per space (https://docs.databricks.com/aws/en/genie/set-up, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **0 (uses Unity Catalog data)** — Genie has no connector catalogue of its own; data must first be ingested into Unity Catalog, e.g. via Lakeflow Connect's 100+ connectors (https://www.databricks.com/product/data-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 company-wide accuracy figure is published; Genie ships a customer-run benchmark tool to test a space's own accuracy privately | Last checked 2026-09-10. Databricks Genie pricing page: https://www.databricks.com/product/business-intelligence ## Why choose Supaboard over Databricks Genie Genie only ever answers questions about data that's already been ingested into a Databricks lakehouse, and its output is a SQL answer to one question at a time. Supaboard connects directly to 700+ sources including the operational systems that never reach a lakehouse, and turns the same context into dashboards, alerts, reports, and workflows, at a flat per-seat price that doesn't move with query volume. ## Supaboard vs Databricks Genie, in our own words Genie is not a BI platform. It's a natural-language query feature inside Databricks' AI/BI product, sitting on top of Unity Catalog and billed the same way every other Databricks workload is billed: by compute (DBUs), not by seat. If your company's analytical data already lives in a Databricks lakehouse and the question is "can our team ask it questions in plain English," Genie answers that question well. If the question is "can one agent be our company's analyst, reaching data wherever it actually lives and producing dashboards, alerts, and reports as one system," that's a different, broader problem, and it's the one this comparison is actually about. ### Where the cost actually lands Databricks says it plainly: "no additional license fees to use AI/BI. Standard Databricks Lakehouse $DBU rates apply," and dashboards and Genie both promise "no license limits" and no "per-seat or per-license fees." That's real, but it means the bill scales with warehouse usage, not headcount, so a heavy quarter of ad hoc questions costs more than a quiet one. Supaboard charges $99 per seat per month ($83 annually), with usage credits already included, so the invoice doesn't move when a team asks more questions. ### What is actually different Genie only ever sees data inside Unity Catalog. Databricks is explicit that "your data [needs to be] managed in Unity Catalog" for Genie to work, and getting data there is a separate step, done through Lakeflow Connect's ingestion connectors, not something Genie itself does. Supaboard connects directly to 700+ sources, including operational systems (Stripe, Salesforce, Zendesk, product databases) that many companies never pipe into a lakehouse at all, so a question can span every connected source in one answer, not just what's already landed in one catalog. Genie spaces are SQL-only. The setup docs talk entirely in terms of tables, SQL functions, and warehouses, with no mention of Python inside a Genie space. Databricks' Python strength lives in its notebooks, a separate part of the platform, developed independently of Genie. Supaboard runs SQL and Python in the same editor over every connected source. On dashboards, Databricks is upfront that AI/BI is "AI-assisted authoring" for configuring "the data and charts that you want," not one prompt producing a finished dashboard. Supaboard generates a live dashboard from a single prompt. The same gap shows up in Databricks Apps: Databricks describes them as built by "developers... using familiar Python frameworks such as Dash, Gradio and Streamlit," with no connection to Genie generating them. Supaboard generates live data apps on the customer's own design system from a prompt. Alerts do exist in Databricks SQL, running "on a schedule" and notifying "when a condition that you define is met," with delivery to email, Slack, Teams, webhook, or PagerDuty, and dashboards can push scheduled PNG/PDF snapshots to Slack. That's real, useful plumbing, but it's several separately configured features, not one agent that decides what to watch and routes the output. There's also no built-in workflow layer chaining detection to analysis to export to routing; you'd assemble that yourself from alerts, dashboards, and jobs. Genie has no chat-surface app of its own. Databricks says plainly you can "add Genie to an existing app like Microsoft Teams, Slack or Glean" only "through Genie's API" — meaning you build the integration. Supaboard ships as a native presence in Slack, Teams, Claude, ChatGPT, and Cursor out of the box. On MCP, Databricks does ship managed MCP servers giving other agents governed access to Genie, AI Search, SQL, and Unity Catalog functions, and it can register external MCP servers too, through Unity Catalog. But its own docs are explicit: "Registering Genie, Apps, or Unity Catalog entity sources as an MCP Service is not currently supported" the other way, meaning Genie can't itself act as a client pulling in outside MCP context the way Supaboard does in both directions. To be fair to Genie, its grounding mechanism, curated tables plus example SQL queries plus written instructions, is a real and reasonably strong way to keep natural-language SQL accurate, and its SQL editor has genuine schema-aware autocomplete. If your entire company's data already lives in one Databricks lakehouse and the job is disciplined self-service SQL over it, Genie is a legitimate, well-built answer to that specific problem. ### What to test if you trial both Point both tools at a question that requires joining a Databricks table with something that isn't in your lakehouse, an operational SaaS tool, a spreadsheet, another database, and see which one can actually answer it in one step. Then ask each to produce a finished dashboard and a finished alert from a single prompt, and compare what you had to build by hand versus what showed up already assembled. ## FAQ ### Is Databricks Genie cheaper than Supaboard? It depends on usage, not seats. Genie has no separate license fee; you pay standard Databricks compute (DBU) rates for the warehouse that runs its queries, so cost scales with how much you query, not headcount. Supaboard is a flat $99/seat/mo ($83/seat/mo billed annually) with usage credits included, so the bill doesn't move with query volume. ### Can Supaboard read the same data that's in our Databricks lakehouse? If your data is accessible through a database or warehouse connection Supaboard supports, yes. Supaboard's 700+ connector catalogue is built to reach data wherever it lives, including sources that never make it into a lakehouse at all, which is the gap Genie can't close since it only queries what's already in Unity Catalog. ### What does Supaboard actually produce, beyond a SQL answer? Dashboards, alerts, scheduled reports, and multi-step workflows, all generated from the same prompt and the same business context. Genie's core output is generated SQL plus a chart for a single question; dashboards, alerts, and Slack delivery exist in Databricks but as separately configured features, not one output from one agent. ### Does Genie work if our data lives outside Databricks? No. Databricks requires the data to be managed in Unity Catalog before Genie can query it, so anything outside the lakehouse has to be ingested first, a separate step, before Genie can see it at all. ### Can Supaboard run in our own VPC instead of a shared cloud? Yes, on the Enterprise plan: single-tenant containers, your own model provider keys, and no data egress. ## 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 Bruin](https://supaboard.ai/compare/bruin) - [Supaboard vs Snowflake Cortex Analyst](https://supaboard.ai/compare/snowflake-cortex) - [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`.