# Supaboard vs Sigma > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Sigma's own pages. HTML version: https://supaboard.ai/compare/sigma ## At a glance | Row | Supaboard | Sigma | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Quoted** — no list price published, sigmacomputing.com/pricing redirects straight to a sales contact form (https://www.sigmacomputing.com/pricing, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **No** — spreadsheet-native BI on a warehouse launched in 2018, AI (Assistant, Ask Sigma) shipped later as an add-on toolkit | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Partial** — Ask Sigma shows its reasoning step by step but explicitly requires a human to review and approve each step before proceeding (https://www.sigmacomputing.com/blog/announcing-ask-sigma, 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** — Sigma calls Ask Sigma a starting point for analysis, not a root-cause or forecasting engine, deeper work happens by hand in a workbook (https://www.sigmacomputing.com/blog/announcing-ask-sigma, checked 2026-09-10) | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **Partial** — input tables let teams build governed write-back forms on warehouse data, but they're built by hand, not generated by AI from a prompt (https://www.sigmacomputing.com/product/write-back, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Partial** — Assistant turns a chat into workbook elements like charts and tables but frames it as a starting point to keep building manually (https://www.sigmacomputing.com/product/ai, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **Partial** — Agents (public beta) chain a detection to a Slack, Salesforce, or Jira action, but are assembled step by step in a configuration UI, not from one prompt (https://help.sigmacomputing.com/docs/build-agents, checked 2026-09-10) | | Alerts | **Yes** — Threshold and anomaly alerts described in plain English, which analyse the change rather than only announce it | **Yes** — conditional scheduled exports support threshold and anomaly alerts delivered by email, Slack, Teams, or webhook (https://help.sigmacomputing.com/docs/schedule-a-conditional-export-or-alert, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Yes** — scheduled exports ship as PDF, Excel/CSV, or PowerPoint (beta) to email, Slack, Teams, SharePoint, or Google Drive (https://help.sigmacomputing.com/docs/share-and-export-reports, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **No** — tables can't be joined across separate connections, Assistant can only bridge sources already linked in a data model ahead of time (https://help.sigmacomputing.com/docs/create-and-edit-joins-in-data-models-and-workbooks, checked 2026-09-10) | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **Yes** — a Python element runs real code with full libraries, but only against a Snowflake or Databricks connection, not every connected source (https://www.sigmacomputing.com/product/python-sql, checked 2026-09-10) | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Partial** — Slack ships a native Analysis Assistant bot and Claude, ChatGPT, and Cursor connect via MCP, but Teams only receives exports, not chat (https://community.sigmacomputing.com/t/ask-your-data-questions-in-slack-analysis-assistant/6945, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Yes** — Sigma runs an MCP server for outside AI tools and its agents can call external MCP tools, both directions (https://help.sigmacomputing.com/docs/use-sigma-mcp-server, checked 2026-09-10) | | Query bench | **Yes** — SQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approve | **Yes** — Python and SQL elements share an autocomplete-enabled editor alongside the spreadsheet interface in every workbook (https://www.sigmacomputing.com/product/python-sql, checked 2026-09-10) | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **No** — Assistant selects context fresh each time from data models and metadata, no persistent ruleset, memory, or knowledge base carries across sessions (https://help.sigmacomputing.com/docs/ask-natural-language-queries-with-assistant, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **~10 warehouses** — warehouse-native by design: Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, AlloyDB, Azure SQL, SQL Server, and Starburst, no direct operational-system connectors (https://help.sigmacomputing.com/docs/connect-to-data-sources, 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 figure for AI answers published on Sigma's own site | Last checked 2026-09-10. Sigma pricing page: https://www.sigmacomputing.com/pricing ## Why choose Supaboard over Sigma Sigma's own docs say tables can't be joined across separate connections and that Ask Sigma is a starting point you keep building by hand, while its agent automation is still in beta and configured step by step in a UI. Supaboard answers cross-source questions in one query, finishes the dashboard, app, or report from the same prompt that answered the question, and publishes its price instead of routing you to sales. ## Supaboard vs Sigma, in our own words Sigma's pitch is a spreadsheet interface that runs live against your warehouse (Snowflake, Databricks, BigQuery, and a short list of others) with no extracts, plus input tables that let teams write governed data back into that same warehouse. It's built for finance and ops people who think in formulas, not SQL. Pricing follows the same enterprise-BI script: sigmacomputing.com/pricing redirects straight to a sales contact form, so every number is negotiated rather than posted. The real question in 2026 isn't spreadsheet-vs-chat, both vendors now ship an AI layer, it's whether that layer was built into the product from day one or added to an older one, and how far the automation actually goes once you've asked your question. ### Where the cost actually lands Sigma publishes no seat price, no tier pricing, and no separate figure for AI usage anywhere on its own site, the pricing page exists only to route you to a contact form. What you pay depends on seats, which edition you're sold into, and how the sales conversation on warehouse compute goes, none of which you can check yourself before talking to sales. Supaboard publishes both numbers up front: $99/seat/mo monthly or $83/seat/mo billed annually, with usage credits already inside the seat, no separate AI metering to negotiate. ### What is actually different Sigma's AI, Sigma Assistant and Ask Sigma, will show you its reasoning step by step and produce real workbook elements (charts, tables, input tables) from a chat, but Sigma's own materials frame it explicitly as "a starting point for your analytical thought process," something you review, edit, and then keep building manually in a workbook. It's a guided query assistant with visible logic, not an analyst that finishes the job. Supaboard's agent is the whole product: the same agent that answers a question also finishes the dashboard, sets the alert, and writes the report, because it's one system rather than an assistant that hands off to a human at every step. That difference shows up concretely in workflows: Sigma Agents (still in public beta) can chain a threshold trigger to a Slack ping, a Salesforce opportunity, or a Jira ticket, but they're assembled step-by-step in a configuration UI, not built from one prompt. Supaboard's "one prompt builds detect, analyze, export, route" is a single instruction, not a UI you configure. Sigma is genuinely capable on distribution: scheduled exports go out as PDF, Excel/CSV, or PowerPoint (in beta) to email, Slack, Teams, SharePoint, or Drive, and conditional alerts on thresholds or anomalies are a real, documented feature. Supaboard matches the delivery formats (PDF, PowerPoint, Excel) but writes the report itself rather than exporting a workbook you already built. The sharpest technical gap is cross-source querying. Sigma's own docs state plainly that tables can't be joined across separate connections, Assistant can bridge two sources only if a relationship was already modeled between them ahead of time. Supaboard is built to let one question span every connected source at query time. Python tells a similar story: Sigma runs real Python with full libraries, but only against a Snowflake or Databricks connection, not your other sources. Supaboard runs SQL and Python together over everything connected. Both ship MCP: Sigma runs an MCP server for outside tools and its agents can call external MCP servers, a real bidirectional setup. Supaboard does the same in both directions, plus reads sources like Notion directly into its own context. On chat surfaces, Sigma has a native Slack bot and MCP access from Claude, ChatGPT, and Cursor, but Teams only receives exported files and notifications, not a conversation. Supaboard ships a native presence in Slack, Teams, Claude, ChatGPT, and Cursor. On connectors, be honest about the tradeoff: Sigma needs a warehouse first, roughly ten supported platforms (Snowflake, Databricks, BigQuery, Redshift, Postgres, and a few more), and everything else has to land in one of those before Sigma can see it. Supaboard connects to 700+ sources, including operational systems that never make it to a warehouse. Neither approach is free: Sigma's model means your data is already governed and modeled before AI touches it; Supaboard's means broader reach but requires trusting the agent's read of systems a warehouse team might not otherwise touch. ### What to test if you trial both Give each tool the same real question that spans two systems, one already in your warehouse and one that isn't, for example warehouse revenue against a support-ticket count in Zendesk or a form tool. Watch whether Sigma can answer it in one step or tells you the sources need to be modeled together first, and watch whether Supaboard's agent handles it, builds the dashboard for it, and sets a working alert on it, all from the same prompt. ## FAQ ### Is Supaboard cheaper than Sigma? Impossible to say for certain, because Sigma doesn't publish a price. Supaboard is $99/seat/mo monthly or $83/seat/mo billed annually, with usage credits included. Sigma is sold entirely through a sales conversation, so the only way to compare is to get a quote and weigh it against a published number. ### Can Supaboard read the same data Sigma is already connected to? Yes. If your Sigma instance runs on Snowflake, Databricks, or BigQuery, Supaboard connects to the same warehouse, and to the 700+ other sources, including operational systems, that never reach that warehouse. ### What does Supaboard actually produce, versus Sigma's workbooks? Supaboard's agent produces finished outputs from a prompt: a live dashboard, a data app on your own design system, a scheduled report as PDF, PowerPoint, or Excel, or a working alert. Sigma's Assistant produces workbook elements you then keep building by hand. ### Do I still need an analyst if I switch to Supaboard? For the questions Supaboard's agents are tuned on, no, the agent shows its SQL and reasoning next to every answer so you can verify it. For genuinely novel modeling work, most teams still keep an analyst; the difference is how much of the routine load Supaboard takes off their plate. ### Can Supaboard run in our own VPC instead of the cloud? Yes, on the Enterprise plan. Supaboard can run as single-tenant containers inside your own VPC, using your own model provider keys, with 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 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 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`.