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Supaboard vs every major BI tool: a sourced comparison

Supaboard against 24 BI tools and AI analysts — from Tableau and Power BI to Qlik, ChatGPT and Claude — on seventeen capabilities: AI analysts, deep reasoning, generated dashboards, alerts, MCP, connectors and accuracy, each figure sourced to the vendor's own pages.

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Compare Supaboard with up to three tools

Pick the tools you are weighing.

  • Supaboard vs Tableau

    Choose Supaboard when you want answers from your database in minutes, without publishing a data source and building a workbook first.

    Choose Tableau when your analysts build visual workbooks by hand and rely on Tableau's chart design and cross-database blends.

    Read our Tableau alternatives guide

  • Supaboard vs Power BI

    Choose Supaboard when you want anyone to ask questions without a semantic model built in Desktop first, or Premium seats and Fabric capacity for the AI.

    Choose Power BI when your company is standardised on Microsoft, and a data team already maintains semantic models in Power BI Desktop.

    Supaboard vs Power BI, in full

  • Supaboard vs Metabase

    Choose Supaboard when one question needs data from more than one database, or you want a dashboard generated from a prompt.

    Choose Metabase when you want open-source BI you can self-host at no licence cost, with Metabot included on every plan.

    Supaboard vs Metabase, in full

  • Supaboard vs ThoughtSpot

    Choose Supaboard when you want answers before anyone models worksheets, and questions that span more than one connection.

    Choose ThoughtSpot when a data team already maintains ThoughtSpot worksheets and models, and you want Spotter's agent on top of them.

    Supaboard vs ThoughtSpot, in full

  • Supaboard vs Looker

    Choose Supaboard when you want to start asking before a LookML model is written, or to ask across more than one connection.

    Choose Looker when governance through a reviewed LookML model matters more to you than time to the first answer.

    Supaboard vs Looker, in full

  • Supaboard vs Qlik

    Choose Supaboard when one question needs to span more than one source, and you want the dashboard, alert and report from the same prompt.

    Choose Qlik when your team already builds in Qlik Sense and wants Qlik Answers' agentic reasoning inside those apps.

    Supaboard vs Qlik, in full

  • Supaboard vs Domo

    Choose Supaboard when you want one agent for answers, dashboards, alerts and reports, at a price published up front.

    Choose Domo when you need 1,000+ pre-built connectors and Magic ETL to move and blend data before it is analysed.

    Supaboard vs Domo, in full

  • Supaboard vs Sigma

    Choose Supaboard when you want questions answered across separate connections, with the dashboard or app finished from the same prompt.

    Choose Sigma when your data lives in one cloud warehouse and your analysts want a spreadsheet interface with write-back input tables.

    Supaboard vs Sigma, in full

  • Supaboard vs Omni

    Choose Supaboard when your questions reach operational databases and apps that never land in a warehouse.

    Choose Omni when everything you analyse is already in a warehouse, and you want a semantic layer kept in sync with dbt.

    Supaboard vs Omni, in full

  • Supaboard vs Zenlytic

    Choose Supaboard when one question needs to combine several sources, and you want a published accuracy figure and seat price.

    Choose Zenlytic when your data sits in one warehouse with a git-managed semantic layer you want an agent to validate every query against.

    Supaboard vs Zenlytic, in full

  • Supaboard vs WisdomAI

    Choose Supaboard when you want the dashboard, data app, alert and scheduled report built by the agent that answered, at a published price.

    Choose WisdomAI when your business context lives across Notion, Confluence, Slack and Jira, and a context engine over all of it is the main need.

    Supaboard vs WisdomAI, in full

  • Supaboard vs TextQL

    Choose Supaboard when you want a per-seat price, with sources queried live and nothing synced first.

    Choose TextQL when you would rather pay for compute than for seats, and want an ontology your agents propose changes to for review.

    Supaboard vs TextQL, in full

  • Supaboard vs Sundial

    Choose Supaboard when your data sits beyond Snowflake and BigQuery, and one question has to combine sources.

    Choose Sundial when you are on Snowflake or BigQuery and want an analyst agent fed by playbooks and context from Notion, Slack, Linear and GitHub.

    Supaboard vs Sundial, in full

  • Supaboard vs Wren AI

    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.

    Supaboard vs Wren AI, in full

  • Supaboard vs Basedash

    Choose Supaboard when you want generated data apps, file-based reports and Python alongside SQL.

    Choose Basedash when you mainly need dashboards and Slack reports, and a flat price for up to 25 users suits your team.

    Supaboard vs Basedash, in full

  • Supaboard vs Hex

    Choose Supaboard when the people asking are not analysts, and you want dashboards, apps and automations from a prompt.

    Choose Hex when your data team works in SQL and Python notebooks and publishes the results for everyone else.

    Supaboard vs Hex, in full

  • Supaboard vs Lightdash

    Choose Supaboard when your questions reach operational systems that are not in a dbt project.

    Choose Lightdash when your metrics are defined in dbt and should drive every chart, with an open-source core you can self-host.

    Supaboard vs Lightdash, in full

  • Supaboard vs Julius AI

    Choose Supaboard when a whole team needs the same business rules, dashboards, alerts and reports over live connected data.

    Choose Julius AI when one person wants to turn a file or a single dataset into charts quickly.

    Supaboard vs Julius AI, in full

  • Supaboard vs Bruin

    Choose Supaboard when you want a self-serve alert and workflow builder, a published accuracy figure and a published price.

    Choose Bruin when you want an open-source pipeline tool for ingestion and transformation, with an analyst layer on top of it.

    Supaboard vs Bruin, in full

  • Supaboard vs Snowflake Cortex Analyst

    Choose Supaboard when your data is not all in Snowflake, or you want a dashboard, alert or report rather than a SQL answer.

    Choose Snowflake Cortex Analyst when all of your data is already in Snowflake and you want text-to-SQL inside it, billed in Snowflake credits.

    Supaboard vs Snowflake Cortex Analyst, in full

  • Supaboard vs Databricks Genie

    Choose Supaboard when your data is not all in a Databricks lakehouse, or you want finished dashboards and workflows from the answer.

    Choose Databricks Genie when your data already lives in Unity Catalog and your analysts curate Genie spaces with example SQL.

    Supaboard vs Databricks Genie, in full

  • Supaboard vs Amazon QuickSight

    Choose Supaboard when you want one system that generates the dashboard and reasons about why a number moved.

    Choose Amazon QuickSight when your company runs on AWS and wants low-cost dashboard viewers with built-in ML forecasting.

    Supaboard vs Amazon QuickSight, in full

  • Supaboard vs ChatGPT

    Choose Supaboard when the same question comes back every week and needs a shared metric definition, a live source, and a dashboard or alert that stays.

    Choose ChatGPT when one person needs a one-off analysis of a file, or a broad research question answered.

    Supaboard vs ChatGPT, in full

  • Supaboard vs Claude

    Choose Supaboard when the question repeats across your team and needs one governed definition, a live connection, and alerts that fire without a prompt.

    Choose Claude when you need a careful one-off analysis of a file, or help with writing, coding and research. Supaboard is reachable from Claude over MCP.

    Supaboard vs Claude, in full

Open a row for the reason behind each verdict and its source.

Supaboard compared with the BI tools and AI analysts you pick, across seventeen capabilities, from price and AI analysts to alerts, scheduled reporting, MCP support, connector count and answer accuracy, each competitor figure linked to the vendor page it was read from
Free, then $19–$249/seat/moOr $16–$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.$75 / $42 / $15Creator, Explorer and Viewer per user per month, billed annuallysource, checked $14 / $24Pro and Premium Per User, per user per month paid yearly, plus a free individual tiersource, checked Free / $100 / $575Open source free; Starter per month for 5 users, Pro per month for 10source, checked From $25Essentials per user per month billed annually, 5 to 50 users; Pro at $0.10 per creditsource, checked QuotedNo list price. Standard, Enterprise and Embed, each including 10 Standard and 2 Developer userssource, checked $300/mo (Starter)capacity-based tiers, not per seat, $300 buys 10 users and 10 GB annually and still requires Contact Ussource, checked Custom (consumption-based)unlimited users, credit-based billing metered by usage, quote only after a demosource, checked Quotedno list price published, sigmacomputing.com/pricing redirects straight to a sales contact formsource, checked Quote-basedNo published price anywhere on omni.co, every path leads to a demo requestsource, checked Quotedseat-based plus query-based pricing and platform fees, quoted only via a sales call, per Zenlytic's own comparison postsource, checked Not publishedno plan or seat price anywhere on the site; the only entry point is a demo requestsource, checked $0–$250+/mo + usageAnalyst tier is free plus compute, Team is $250/mo plus $0.003/ACU, Enterprise is custom — billed by compute, not seatssource, checked Quote-onlyusage-based, priced on data volume and queries, tailored per customer with no public numbersource, checked Free (OSS) / $179-$559/moCloud is a flat org fee plus usage credits, unlimited seats, not per-seat pricingsource, checked $1,000/mo flatFlat rate for up to 25 users plus a shared AI usage pool, not per seatsource, checked $0-$75/editor/mofree Community tier, Professional $36/editor/mo, Team $75/editor/mo, Enterprise custom, all confirmed USDsource, checked Free (OSS) / $3,000/mo (Cloud Pro)self-hosted core costs nothing forever; Cloud Pro is one flat unlimited-seat fee, not per usersource, checked $20–$450/mofour individual/team tiers monthly (Plus $20, Pro $45, Max $200, Business $450), about 20% less billed annually; Business is a flat fee for up to 50 members, not per-seatsource, checked Free CLI + usage-based Cloudopen-source CLI is free forever; Cloud free tier gives $100 credits and 50 AI tasks/mo before usage-based pricing kicks insource, checked 67 credits / 1,000 messagesStandalone API billed per message in platform credits, plus separate warehouse compute, not a seat pricesource, checked Usage-based (DBUs)No per-seat fee; billed via standard Databricks compute rates for the warehouse Genie queries run onsource, checked $3–$40/user/morole-based pricing plus a flat $250/mo fee once Amazon Q features are turned onsource, checked Varies by regionPricing varies by region; check the vendor's pricing page for your market.source, checked $17-125/seat/moPro is $17/mo annual ($20 monthly); Team runs $20-125/seat/mo; Enterprise is $20/seat plus usage-based costssource, checked
YesThe agent is the product, not a panel added to a dashboard toolNoTableau Agent and Pulse are AI added to a visual analytics tool built in 2003NoCopilot needs paid Fabric capacity (F2 or higher) or Premium; free SKUs are not supportedsource, checked NoBut Metabot ships in every plan including open source, with no AI tier or per-seat AI feePartialClosest of the five: search-first since 2012, and Spotter is a genuine agentNoConversational Analytics is Gemini added on top of LookMLNoassociative engine dates to QlikView in 1993, with Insight Advisor and Qlik Answers layered on laterNoa broad BI/ETL/apps/workflow platform with Domo AI and Agent Catalyst layered on as a product line, not built around one agentNospreadsheet-native BI on a warehouse launched in 2018, AI (Assistant, Ask Sigma) shipped later as an add-on toolkitPartialBuilt around a workbook and semantic layer first, with an AI agent layered on top as one of several query modessource, checked Yesbuilt around Zoë, an AI analyst agent, positioned as the governed layer that makes Claude reliable on your datasource, checked Yesbuilt and marketed as an agentic analytics platform, not BI software with AI added onsource, checked YesAna the AI analyst agent is the core product, built on a self-improving ontologysource, checked Yesbuilt as an agentic analytics platform from the outset, not a BI tool with AI added onsource, checked Yesbuilt from the ground up as a text-to-SQL GenBI agent with a semantic modeling layersource, checked YesBasedash markets itself as 'the AI-native business intelligence platform'source, checked NoHex launched in 2019 as a SQL/Python/no-code notebook; Magic, Threads, and Context Studio are AI layered on top of that productPartialagents answer questions by querying metrics and dimensions already defined in a dbt semantic layersource, checked Yesan AI workspace built chat-first, generating code, charts, decks, and sites from one prompt threadsource, checked Nocore 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 topNoDocumented as a Cortex feature added to the Snowflake platform, not a standalone AI-first productsource, checked NoGenie is a feature added onto the existing Databricks Lakehouse/Unity Catalog platform, not a standalone AI-first productsource, checked NoQuickSight launched in 2016 as traditional BI; Amazon Q's generative layer arrived in April 2024, eight years latersource, checked YesGeneral-purpose assistant across many tasks, not purpose-built for business intelligencesource, checked PartialClaude is a general-purpose AI assistant for coding, writing, and research, not a product built for business intelligence
YesAgents tuned on your business rules and metric definitions, with the SQL shown beside every answerPartialTableau Agent gives insights inside Pulse and Ask Q&A; Pulse is Tableau Cloud onlysource, checked PartialCopilot answers inside a semantic model somebody has already publishedPartialMetabot answers questions, writes SQL and explains charts; it sees only what the user can seesource, checked YesSpotter breaks down questions, tests assumptions, checks results and reruns the analysissource, checked PartialConversational Analytics answers against a modelled ExploreYesQlik Answers agents generate answers from data using agentic reasoning, not just query assistsource, checked YesAI Chat answers natural-language questions from connected data with visualizations and recommendationssource, checked PartialAsk Sigma shows its reasoning step by step but explicitly requires a human to review and approve each step before proceedingsource, checked PartialAn agentic coordinator plans, queries, and evaluates results, but it's not tuned on a customer's specific business rulessource, checked YesZoë investigates, explains, and delivers cited answers to business questions, not just query assistancesource, checked YesAnalytics Agents reason over connected data and take actions like creating tickets or triggering APIssource, checked YesAna answers business questions end to end and delivers charts, reports, data apps, and alertssource, checked Yesan Analysis Agent answers business questions end to end, from lookups to multi-step investigationssource, checked Partialanswers business questions via governed text-to-SQL grounded in MDL definitions and stored business knowledge, not a broader analysis agentsource, checked YesAI data analyst validates generated SQL against your schema and enforces shared metric definitionssource, checked PartialThreads lets anyone ask questions in plain language, but is scoped to endorsed, modeled data the data team curatessource, checked Partialan agent picks the relevant dbt metrics and dimensions and runs the query, not open-ended business analysissource, checked Partialcustom agents can be shared for repeatable team Q&A, but the core loop is one person chatting with one connected datasetsource, checked Yesagent answers business questions in chat, shows the SQL it ran, and can act on live warehouse datasource, checked PartialGenerates SQL and returns results for structured questions over data already in Snowflakesource, checked PartialAnswers business questions with generated SQL and charts, tuned with curated tables and example queries, not a dedicated business-rules agentsource, checked PartialAmazon Q answers natural-language questions and builds visuals from topics, but doesn't reason end to end like a tuned analystsource, checked NoAnalyzes files uploaded per conversation, not an ongoing agent tuned to your business rulessource, checked Session-onlycode execution analyzes whatever file or connection is attached in that conversation, not an ongoing governed system over company datasource, checked
YesDeep Dive answers why a number moved and what to do next, not only what it isNoPulse surfaces changes and outliers rather than reasoning through a questionNoNot documented as multi-step reasoning over a questionNoNot documentedYesSpotter does multi-step reasoning and delivers recommended actionssource, checked YesWith the Code Interpreter: forecasting, cohort analysis and driver analysissource, checked Yesown pages promise complex, multi-step agentic reasoning and chain-of-thought follow-upssource, checked Partialships prebuilt forecasting by default; root-cause analysis exists only in a custom-built Agent Catalyst templatesource, checked NoSigma calls Ask Sigma a starting point for analysis, not a root-cause or forecasting engine, deeper work happens by hand in a workbooksource, checked PartialCoordinator plans multi-step queries and evaluates results, but no published root-cause or forecasting modesource, checked Yesruns root cause investigations, not just alerts, and produces trend-based forecasts per its own product pagesource, checked Partialagents run multi-step workflows with conditions and loops, but root-cause or forecasting analysis isn't describedsource, checked PartialFans out to parallel subagents for scale and multi-source joins, but no published root-cause or forecasting modesource, checked Partialroot-cause investigation is documented; no forecasting capability is describedsource, checked Noown architecture docs describe single-question retrieval, prompting and SQL validation, not root-cause or forecasting reasoningPartialChat and daily Insights explain anomalies and trends but there is no named multi-step root-cause modesource, checked PartialAgent Tasks can proactively investigate a metric and send a root cause analysis, but this runs on a schedule rather than on demand in every answersource, checked YesDeep research runs a multi-step investigation with a coordinator and up to two data workers into an evidence-backed reportsource, checked Partialexplains patterns and can run statistical or predictive analysis on request, but there is no dedicated multi-step reasoning mode across a sessionsource, checked Partialexamples show cross-source validation across tools but no named multi-step 'why did this move' modesource, checked NoText-to-SQL with multi-turn follow-ups, no documented multi-step root-cause or forecasting modesource, checked NoGenie asks clarifying questions within a conversation but has no dedicated root-cause or forecasting modePartialnative ML forecasting and what-if scenarios are real and strong, but Q&A itself is single-turn, not root-cause reasoningsource, checked YesReasoning models and deep research synthesize multi-step, cited analysis across sourcessource, checked Yesadaptive and extended thinking let Claude reason at a configurable depth before answering complex, multi-step questionssource, checked
YesLive data apps on your own design system, from a promptNoDashboards and workbooks, not applicationsPartialPower Apps, built by a maker rather than generated from a promptNoPartialLiveboards and embedded components, not apps on your own design systemPartialExtensions, built by a developer against the Looker APINoQlik's own pages describe charts and dashboards, not a full interactive data app generated from a promptYesApp Catalyst builds a working app from a text prompt on governed data, refined with natural languagesource, checked Partialinput tables let teams build governed write-back forms on warehouse data, but they're built by hand, not generated by AI from a promptsource, checked YesApps are AI-generated interactive tools built from a prompt on the semantic model, can take input and write backsource, checked YesArtifacts can be interactive apps Zoë builds from a prompt, using a Python sandbox to generate the outputsource, checked Noembeddable surfaces are limited to chat and dashboards, not full interactive applicationssource, checked YesData apps are built per question from the ontology and stored back into itsource, checked Yesthe Apps surface builds interactive views with KPI cards, charts, tables and filters from a promptsource, checked YesGenBI Apps builds interactive dashboards and reports from a prompt on Cloud Agentic projectssource, checked Nocurrent product builds dashboards, chat answers, and automations, not generated interactive appsPartialHex's agent can build custom generative apps from a prompt, but this is in beta; the default path is an analyst building in a notebook then publishingsource, checked Yesgenerates an interactive React data app from a prompt in a sandboxed iframe, free during its current betasource, checked Partialthe AI website builder turns a prompt into an interactive app or dashboard and publishes a shareable linksource, checked YesBruin Data Apps turns a chat description into an operational app wired to live, governed warehouse queriessource, checked NoStreamlit in Snowflake is a Python framework developers hand-code apps in, not AI-generated from a promptsource, checked NoDatabricks Apps are built by developers with Python frameworks like Dash, Gradio and Streamlit, not generated by Genie from a promptsource, checked Partiallive, data-connected apps exist, but as a separate product ("Apps in Amazon Quick"), not inside QuickSight itselfsource, checked PartialSites can build a shareable app from a prompt, but isn't wired to live company databasessource, checked PartialArtifacts can persist state across sessions and be published, but each one lives inside a single conversation, not a managed productsource, checked
YesA live dashboard from a single promptPartialThe AI helps an analyst build a workbook; it does not produce one from a promptPartialCopilot drafts report pages inside a published modelNoMetabot helps build and explain questions rather than generating a dashboardsource, checked YesSpotterViz turns data into dashboardssource, checked YesThe MCP server exposes tools for creating dashboards and Lookssource, checked PartialInsight Advisor builds one chart at a time, only a fixed period-over-period template bundles severalsource, checked PartialAI surfaces trends and answers questions inside dashboards you build, not full generation from a single promptsource, checked PartialAssistant turns a chat into workbook elements like charts and tables but frames it as a starting point to keep building manuallysource, checked YesDashboard Builder plans queries, picks charts, and lays out a full dashboard from a single promptsource, checked Yesdashboards are one Artifact type generated from a prompt, with live queries that re-run against the warehouse on opensource, checked Partialdashboards build from a natural-language prompt as widgets, then the team explores and rearranges themsource, checked PartialDashboards are bundled with data apps as one prompt-built category, not a separately named dashboard generatorsource, checked Yessame Apps feature; ask the Analysis Agent to build a dashboard and it stays always livesource, checked YesGenBI Apps or Classic Dashboards generated from natural-language promptssource, checked YesOne prompt assembles a complete dashboard of charts, KPIs, and layoutsource, checked Partialgenerative dashboards from a prompt are described as a beta capability, not the default workflowsource, checked Partialdata apps can generate a dashboard-style app from a prompt, but native dashboards are still built tile by tilesource, checked Partialpricing page promises shareable dashboards generated from data, but no evidence they refresh live rather than being a point-in-time exportsource, checked Yesfull interactive dashboard with KPIs, charts, filters, and date pickers generated from one promptsource, checked NoSnowsight dashboards are built manually tile by tile from worksheet queries, not generated by AI from a promptsource, checked PartialAI-assisted authoring helps configure data and charts; not a full dashboard generated from a single promptsource, checked Nogenerative BI builds one visual at a time from a prompt, which an author then manually adds to a dashboardsource, checked PartialSites can publish a persistent shareable dashboard, but it's prompt-built, not warehouse-connectedsource, checked Nono dedicated dashboard product; a published Artifact is the closest equivalent and has no scheduled data refresh
YesOne prompt builds the whole chain: detect, analyse, export and route the resultPartialRules and schedules only; the AI does not act on what an alert findsPartialPower Automate, wired by handPartialRules and schedules only; Metabot answers questions, it does not run workflowsYesSpotter can create Jira tickets, update Salesforce and post to Slacksource, checked PartialRules and schedules only; no agent step after the deliveryPartialthe Automate agent triggers pre-built workflows by asking, it doesn't author a new one from a promptsource, checked Partiala low-code automation builder with AI assistance, mapped by hand rather than generated from one promptsource, checked PartialAgents (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 promptsource, checked NoRoutines run a scheduled prompt and reply in plain text to email or Slack, they cannot edit dashboards or export filessource, checked PartialProactive Agents chain multi-step, conditional-logic conversations on a schedule to email or Slack, short of a documented single-prompt detect-to-route pipelinesource, checked Yestext-to-agentic-workflow builds a full agent that queries data, analyzes it, and takes action like ticketing or alertssource, checked YesAgent fleets detect issues like stale data and deliver scheduled briefs without a human triggering each stepsource, checked PartialSlack supports threshold alerts and scheduled prompts with CSV/PDF export, no documented single chained automationsource, checked Nono documented end-to-end automation chaining detection, analysis, export and routingPartialAutomations trigger on a schedule or data change and deliver AI analysis to Slack or email, with no export or multi-destination routing stepsource, checked PartialAgent Tasks run on a schedule, investigate a metric, and deliver results to Slack or email, but this is not a full detect-analyze-export-route automation buildersource, checked Partialan agent conversation can become a recurring scheduled delivery, but no single prompt chains detect, analyze, export and routesource, checked Partialscheduled runs can repost a saved prompt to Slack on a cadence, but there is no builder for a multi-step detect-analyze-export-route pipelinesource, checked Partialshows 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 DAGsource, checked NoTasks run scheduled SQL and stored procedures; no documented AI-planned detect-analyze-export-route buildersource, checked NoAlerts, dashboards and jobs exist as separate features you assemble yourself; no single-prompt detect-analyze-export-route builderPartialpossible via separate Quick Flows/Quick Automate products, not built from a single prompt inside QuickSightsource, checked PartialWorkspace agents run on a schedule or API trigger, but there's no native warehouse pipelinesource, checked PartialClaude Cowork can schedule a task to run unattended on a cadence, but only against the apps and folders connected to that tasksource, checked
YesThreshold and anomaly alerts described in plain English, which analyse the change rather than only announce itYesData-driven alerts on a continuous numeric axis, sent by email, in-site or to Slacksource, checked PartialOnly on dashboard tiles pinned from gauge, KPI and card visuals, and visible to nobody but the person who set itsource, checked YesOn a saved question, delivered by email, Slack or webhook; webhooks are admin-onlysource, checked YesKPI alerts on a threshold or an anomaly; an admin caps how often they may checksource, checked YesOn query-based or Look-linked dashboard tiles, set up tile by tilesource, checked Yesthreshold alerts with real-time delivery via Slack, Teams, email and more through Qlik Automatesource, checked Yescustom threshold alerts delivered by email, text, mobile app, or phone callsource, checked Yesconditional scheduled exports support threshold and anomaly alerts delivered by email, Slack, Teams, or webhooksource, checked YesConditional routines watch for a stated condition in plain language and notify by email or Slack when it's metsource, checked Yesanomaly detection on governed metrics, delivered as scheduled investigations to Slack or emailsource, checked Yesagents push anomaly alerts and threshold notifications through Slack, Teams, or emailsource, checked PartialAlerts are a listed Ana output delivered via Playbooks, but no documented threshold or anomaly rule buildersource, checked Yesthreshold alerts and scheduled anomaly digests are delivered through Slacksource, checked Nono threshold or anomaly alerting feature found in Wren AI's own docsYesData-change triggers and daily anomaly insights are delivered to Slack and emailsource, checked Yessubscriptions and conditional notifications alert on scheduled-run completion or a defined condition, plus agent-driven investigation of unexpected changessource, checked Yesthreshold alerts on a saved chart's first value, delivered to email or Slack, no anomaly detectionsource, checked Nono threshold or anomaly alerting product feature found; anomaly detection appears only as an educational glossary termPartialships blocking pipeline/schema-change alerts plus example agent actions like auto-pausing campaigns, not a configurable plain-English threshold buildersource, checked YesNative Alerts evaluate a scheduled SQL condition and can email, call a webhook, or run a proceduresource, checked YesSQL Alerts run a query on a schedule and notify on email, Slack, Teams, webhook or PagerDuty when a condition is metsource, checked Yesnative threshold alerts on KPI, gauge, table, and pivot visuals, delivered by emailsource, checked PartialMonitoring tasks can watch for changes and notify, not threshold or anomaly alerts on metricssource, checked PartialCowork's scheduled reports flag variances like moves over 10% within a report run, not a standing threshold-alert system on live datasource, checked
YesScheduled reports written by an agent and delivered as PDF, PowerPoint or ExcelYesSubscriptions email an image or PDF snapshot of a view or workbook on a schedulesource, checked YesEmail subscriptions to reports and dashboards; subscribing anyone else needs a paid Pro or PPU licencesource, checked YesDashboard subscriptions by email or Slack, including to people with no Metabase accountsource, checked YesScheduled Liveboard emails, one Liveboard per job and up to 80 visualisationssource, checked YesScheduled dashboard delivery to email, webhook, an S3 bucket, SFTP or Slacksource, checked YesQlik NPrinting schedules and distributes reports as Excel, Word, PowerPoint, PDF and HTMLsource, checked Yesscheduled reports sent to subscribers; specific export formats are not detailed on the sitesource, checked Yesscheduled exports ship as PDF, Excel/CSV, or PowerPoint (beta) to email, Slack, Teams, SharePoint, or Google Drivesource, checked YesScheduled delivery as CSV, PDF, PNG, or Excel via email, Slack, webhook, S3, or SFTP, no PowerPoint exportsource, checked Yesscheduled delivery of PowerPoint, Excel, Word, and interactive-memo artifacts to email or Slacksource, checked Partialdashboards can be subscribed to on a daily, weekly, or custom cadence via email or Slacksource, checked YesPlaybooks deliver scheduled results to Slack and Microsoft Teams as formatted cardssource, checked PartialSlack delivers scheduled digests, rich PDFs and CSV exports; no PowerPoint or Excel export documentedsource, checked NoGenBI reports are built on demand in chat; no scheduled delivery via email, Slack, or file export is documentedPartialScheduled automations deliver AI-written reports to Slack and email only, no PDF, PowerPoint, or Excel export in that pipelinesource, checked Partialscheduled runs deliver to Slack or email with CSV attachments or PNG/PDF screenshots; no PowerPoint or Excel exportsource, checked Yesscheduled deliveries send a chart or dashboard as image, CSV, XLSX or PDF to email, Slack, Teams or Google Chatsource, checked Partialscheduled runs deliver reports to Slack on a recurring cadence, limited on lower tiers and unlimited on Business; no native email or PDF/PPT/Excel delivery schedule documentedsource, checked Yesscheduled reports plus PDF, image, and chart exports are shipped delivery formatssource, checked NoTasks and Alerts can trigger an email notification but there's no built-in PDF, PowerPoint, or Excel report exportsource, checked PartialDashboards can push scheduled PNG/PDF snapshots to a Slack channel; no PowerPoint/Excel export or agent-written reportsource, checked Yesscheduled email reports as PDF, CSV, or Excel, up to 5 visuals per schedulesource, checked PartialScheduled tasks and agents can deliver recurring updates, but sharing to a team is manualsource, checked PartialCowork can schedule a report daily, weekly, or monthly and build the deck, but it runs as one user's task, not a team-wide delivery systemsource, checked
YesOne question spanning every connected sourceYesCross-database joins and blends, defined in the workbookYesOnce sources are combined into one model in Power QueryNoA question can only use tables from the same database as its starting datasource, checked PartialWithin one connection; it queries the warehouse you point it atNoOne connection per model; joins live inside LookMLNoQlik Answers works within a single Qlik Sense app per query, multi-app is only planned for a future releasesource, checked YesMagic ETL and the SQL tile join and blend data across all connected sources before analysissource, checked Notables can't be joined across separate connections, Assistant can only bridge sources already linked in a data model ahead of timesource, checked NoDocs describe each model as built on one connection, no published support for joining across separate connections in one questionNodocumentation describes querying one connected warehouse's semantic layer at a time, with no published cross-source capabilityYesone question can span multiple connected sources, e.g. a warehouse, a CRM, and a PDF contract togethersource, checked YesOne question can join warehouse history with live SaaS data such as Salesforce in a single querysource, checked Nonot documented; only Snowflake and BigQuery are listed as connectors, with no stated multi-source query claimNoeach Wren AI project connects to one data source at a time, confirmed in its own connector docssource, checked YesAutomations and chat can analyze and join data across every connected source in one runsource, checked YesThreads can query multiple connected data sources within a single conversationsource, checked Noeach project queries the one warehouse its dbt project compiles against, no cross-warehouse query in a single questionPartialmarkets a single workspace across every connected source, but documented examples query one connector at a time rather than joining two live sources in one questionsource, checked Yesone question can join unlimited connected sources in a single answersource, checked NoOnly answers questions over data already loaded into Snowflake tablessource, checked NoGenie only queries data already inside Unity Catalog; other sources must be ingested first via a separate toolsource, checked Partialtopics join multiple datasets into one semantic layer, but AWS doesn't describe truly open-ended cross-source questionssource, checked PartialCompany Knowledge cites across connected apps, but it's document search, not a live data joinsource, checked PartialTeam/Enterprise ship named connectors like Slack, Google Workspace, GitHub, Microsoft 365, HubSpot, and Asana, plus any MCP serversource, checked
YesSQL and Python in one editor, over every connected sourcePartialThrough TabPy, an external service you run yourselfYesIn Power Query and Python visuals; needs Python installed locally and a gateway to refreshsource, checked NoNoYesWritten by the Code Interpreter: pandas, numpy and scikit-learn are availablesource, checked PartialAdvanced Analytics Integration calls out to a separately hosted R or Python server via chart expressionsYesreal Python and R for data scientists via Jupyter Workspaces, plus R/Python scripts inside Magic ETLsource, checked Yesa Python element runs real code with full libraries, but only against a Snowflake or Databricks connection, not every connected sourcesource, checked NoWorkbook query modes are point-and-click, SQL, and spreadsheet formulas, no Python execution mode found in product pages or docsYesZoë writes and evaluates Python in a secure sandbox to build interactive Artifactssource, checked Nono product or docs page mentions a Python execution environment; answers come from AI-generated queries onlyPartialAna runs Python internally for charts and analysis; users are told they never need to write code themselvessource, checked Yesthe Analysis Agent can use SQL or Python directly for deeper investigationssource, checked Noquerying is SQL-only across all documented surfaces; no Python execution feature is describedNoSQL editor only, no documented Python execution against connected dataYesnative Python cells alongside SQL in every notebook, with integrated package management and AI-assisted code fixessource, checked Nono in-app Python execution; the SQL runner is SQL-only, Python access is external via a Postgres-wire API or SDKsource, checked Yesgenerates and runs real Python/SQL code and shows it beside every answersource, checked YesSQL, Python, and R are first-class in the transformation engine across connected warehousessource, checked NoSnowpark Python is a separate Snowflake feature; Cortex Analyst itself only generates SQLsource, checked NoGenie spaces generate SQL only; Python lives separately in Databricks notebooks, not inside a Genie spacesource, checked Nocustom SQL editor only; no native Python execution against connected datasource, checked YesRuns real Python in a stateful sandbox, but only on uploaded files, no external API callssource, checked Yesthe code execution tool runs real Python and Bash in a sandboxed container to analyze data and generate filessource, checked
YesSlack, Teams, Claude, ChatGPT and CursorPartialSlack, for Pulse digestsPartialTeams onlyPartialSlack, where Metabot answers by mention; Claude and ChatGPT through the MCP serversource, checked YesClaude, ChatGPT, Gemini and any MCP client; Spotter can post to Slacksource, checked PartialClaude and other MCP clients, through the Looker-managed MCP serversource, checked Slack, Teamsdelivers alerts and automation actions into Slack and Teams, no native presence in Claude, ChatGPT or Cursorsource, checked NoSlack and Microsoft Teams appear only as data-source connectors; no native AI chat presence in Slack, Teams, Claude, ChatGPT, or Cursor is publishedPartialSlack ships a native Analysis Assistant bot and Claude, ChatGPT, and Cursor connect via MCP, but Teams only receives exports, not chatsource, checked Slack, MCP clientsNative Slack agent plus an MCP server reachable from Claude, ChatGPT, Cursor, and Copilot, no native Teams agentsource, checked PartialClaude.ai, Claude Code, and ChatGPT connect via MCP, and Slack and Teams are native surfaces; no Cursor presence documentedsource, checked Partialan MCP server lets Claude, ChatGPT, Cursor, and VS Code call in as clients; Slack and Teams are used for pushing alerts and scheduled digests, not native two-way chatsource, checked Slack, TeamsDedicated Slack and Microsoft Teams integrations are shipped; no documented native presence in Claude, ChatGPT, or Cursorsource, checked PartialSlack is native; Teams and Codex are named on the homepage; Claude and Cursor connect only as MCP clientssource, checked Partialships in Slack and Microsoft Teams (Cloud, Interactive Mode only) and a Claude.ai connector on Enterprise Cloud; no ChatGPT integration foundsource, checked Slack, ChatGPT, CursorNative Slack app plus MCP support for ChatGPT, Cursor, and Claude Code; no Microsoft Teams or native Claude chat surfacesource, checked Partialnative in Slack; Claude, Cursor, ChatGPT, Codex, and Glean can reach Hex via its MCP server, but there is no Microsoft Teams surface for the agentsource, checked Slacknative chat lives in Slack; Claude, ChatGPT and Codex reach Lightdash only as MCP query clientssource, checked Slack onlyships a native Slack agent; no published native presence in Teams, Claude, ChatGPT, or Cursorsource, checked Slack, Teams + 6 moreships Slack, Microsoft Teams, Discord, Google Chat, WhatsApp, Telegram, email, and browser; no native Claude or ChatGPT surface, Cursor is dev-only via MCPsource, checked PartialMCP server connects to Claude, ChatGPT, and Cursor; Slack and Teams require building your own integrationsource, checked None nativeGenie can be added to Slack, Teams or Glean only by building the integration yourself against its APIsource, checked PartialSlack, Microsoft Teams, and Microsoft 365, but no native presence in Claude, ChatGPT, or Cursorsource, checked PartialWorkspace agents can be deployed into a Slack channel, but ChatGPT stays its own destinationsource, checked Web, desktop, mobile, Slackships web, desktop (Mac/Windows/Linux beta), iOS/Android, and a Chrome extension; Slack is a Team/Enterprise-only betasource, checked
YesIn both directions: we expose one, and we read yoursYesTableau publishes an MCP server for Cloud and Server sitesYesMicrosoft's MCP server targets semantic modelling from VS Code rather than asking questionsYesAn official MCP server, scoped to the connecting user's permissionssource, checked YesThe first major BI platform to ship one, now supporting Spotter 3source, checked YesA Looker-managed MCP server over the Conversational Analytics APIsource, checked Server onlyships its own MCP server for AI clients, own pages don't describe consuming other MCP serverssource, checked Nono mention of an MCP server or MCP client support found anywhere on domo.comYesSigma runs an MCP server for outside AI tools and its agents can call external MCP tools, both directionssource, checked One-wayShips an MCP server so external AI tools can query Omni, no published support for Omni consuming other MCP serverssource, checked Yesships its own MCP server for Claude and ChatGPT to query it, plus an MCP client that reads Tableau, Power BI, Looker, dbt, Snowflake, GitHub, and Jirasource, checked Partialships a hosted MCP server exposing chat, list_domains, and echo tools; docs don't describe it consuming other MCP serverssource, checked Yes, both directionsExposes an MCP server (ana, ana_ask, list_connectors tools) and consumes external MCP servers like Notion or GitHubsource, checked YesSundial runs its own MCP server for external clients and reads Notion, Slack, Linear and GitHub as contextsource, checked Partialserves an MCP server for clients like Cursor and Claude Code (OSS CLI or hosted on Enterprise Cloud); no evidence it consumes other MCP serverssource, checked Yes, both directionsShips a remote MCP server for AI clients and can also connect external MCP servers as tool sourcessource, checked BothHex ships its own MCP server for external assistants, and its agent can also act as an MCP client connecting to Notion, Linear, and other MCP servers (beta)source, checked Yes, both directionsships an MCP server for Claude, ChatGPT and Codex, and agents can call external MCP servers like Notion and Linearsource, checked Partialconsumes external MCP servers (Notion, GitHub, Intercom, Zapier) as a client; no published Julius-hosted MCP server for other tools to pull fromsource, checked Yesships an MCP server so AI editors like Cursor and Claude Code can query warehouses and build pipelines through Bruinsource, checked YesSnowflake-managed MCP server is generally available and exposes Cortex Analyst as a callable toolsource, checked PartialManaged 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 servicesource, checked PartialAmazon Quick ships an MCP client to consume external servers, but AWS has not published an MCP server exposing QuickSight datasource, checked PartialConnects to external MCP servers as a client, but does not expose ChatGPT as an MCP serversource, checked YesAnthropic created and open-sourced MCP, and Claude Desktop and Claude.ai are reference clients connecting to local and remote serverssource, checked
YesSQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approvePartialCustom SQL inside a data source, not a general editorPartialDAX and Power Query, not a general SQL editorYesA native SQL editor alongside the visual query builderPartialModelled worksheets rather than a general SQL editorYesSQL Runner, alongside LookML developmentPartialData Load Editor autocompletes Qlik's own script keywords, not schema-aware SQL or Pythonsource, checked Partiala real SQL tile inside Magic ETL plus an AI SQL assistant that writes queries from prompts; schema-aware autocomplete not advertisedsource, checked YesPython and SQL elements share an autocomplete-enabled editor alongside the spreadsheet interface in every workbooksource, checked YesSQL workbook that automatically parses and restructures queries, used alongside modeled data in the same workbooksource, checked Nono schema-aware SQL/code editor is documented; SQL surfaces only as chat-generated queries inside ArtifactsNono SQL or code editor is documented; a run visualizer shows the SQL that already ran, but nothing is user-editableNoInterface is chat-first; no documented interactive SQL or Python editor with schema-aware autocomplete for end usersNono SQL/code editor with autocomplete is documented; interaction is through chat and generated Apps/ArtifactsNoSQL is AI-generated and shown read-only after the fact ("View Full SQL"), not an editable schema-aware workbenchsource, checked YesSQL editor with syntax highlighting, autocompletion, schema browser, and an AI query assistantsource, checked Yesschema browser, typeahead, Jinja and dbt support in SQL, plus AI-assisted code fixes across SQL and Python cellssource, checked YesSQL runner with a warehouse table browser and schema panel; no documented autocomplete or AI-diff editingsource, checked Nothe Notebooks feature that let you view, edit, and rerun generated SQL/Python was sunset in August 2026 in favor of single-prompt chat onlysource, checked Noprimary interface is CLI plus YAML/SQL/Python asset files with a VS Code extension, not a hosted schema-aware editorsource, checked YesSnowsight worksheet editor autocompletes table, column, and alias names as you typesource, checked YesSQL editor has schema-aware autocomplete for columns, aliases and joins; Python/Scala/R live in notebookssource, checked Partialcustom SQL editor with syntax highlighting, basic autocomplete, and a schema explorer panelsource, checked NoNo persistent schema-aware SQL editor; only ephemeral Python on files with no external callssource, checked Nocode execution is a sandboxed, ephemeral container per conversation, not a persistent schema-aware SQL/code editor over connected databasessource, checked
YesRulesets, knowledge, dropped files and apps such as Notion, read through MCPPartialA published data source, plus whatever the workbook author knewPartialThe semantic model, published from DesktopPartialModel metadata and, on Pro, a system prompt for MetabotPartialWorksheets and models, maintained by a data teamYesLookML: strong governance, and a modelling project before the first questionYesbusiness logic defines a logical model and vocabulary that persist for all users across sessionssource, checked PartialFileSets feed documents into agents as context; no published rules or metric-definition memory across sessionssource, checked NoAssistant selects context fresh each time from data models and metadata, no persistent ruleset, memory, or knowledge base carries across sessionssource, checked Semantic layerWritten as code with two-way dbt sync, or built just-in-time from the UI as you analyze, then promoted into the shared modelsource, checked Yesa git-managed semantic layer of metric and field definitions that Zoë validates every query against, with usage-based suggestions to promote new definitionssource, checked YesAdaptive Context Engine ingests docs, Notion, Confluence, Slack, and Jira alongside live warehouse syncs, with drift and conflict tracking and context versioningsource, checked Yes — "the Ontology"A git-stored semantic layer of definitions, playbooks, and permissions that agents propose patches to and teams reviewsource, checked Yesa Context Engine holds the semantic layer, playbooks, AI context and warehouse metadata, fed by Notion/Slack/Linear/GitHubsource, checked MDL semantic layerversion-controlled business models, metrics and relationships in files, plus a separate knowledge base of instructionssource, checked YesGlobal context, group context, reusable 'Skills' playbooks, and a Models semantic layer persist across sessionssource, checked YesContext Studio holds semantic models, endorsed tables, and published work as persistent team-level context the agent draws on across notebooks, Slack, and CLIsource, checked dbt-defined semantic layermetrics, dimensions and tables defined once in version-controlled YAML that every chart, agent and API call reads fromsource, checked Partialcustom agents can be trained on schemas, data dictionaries, and dbt info, and the connector layer learns a schema's business rules over repeated queries, but no single persistent team-wide ruleset is shownsource, checked Yesa version-controlled glossary defines shared entity and column definitions; the agent can also pull context from Notion or Confluence over MCPsource, checked Semantic model YAMLBusiness tables, dimensions, facts, and metrics are hand-defined in a semantic model filesource, checked Curated tables + example SQL + instructionsAnalysts add up to 30 tables/views, example SQL queries and functions, and written instructions per spacesource, checked PartialQ Topics hold friendly names, descriptions, and custom instructions as a semantic layer, curated per topic by an authorsource, checked PartialCompany Knowledge cites across connected apps company-wide, but memory itself is per-usersource, checked PartialProjects can be shared org-wide on Team/Enterprise with per-project knowledge, but each is a separate opt-in workspace, not one ruleset for every sessionsource, checked
12 databases + 22 appsDatabases and warehouses queried live when a question is asked; apps read live through their MCP servers~100Listed connectors. Tableau publishes no single totalHundredsListed in Power Query. Microsoft publishes no single totalsource, checked 18Databases with official drivers. Databases only, not SaaS appssource, checked 34Databases and warehouses. No SaaS appssource, checked 48SQL dialects. Databases and warehouses onlysource, checked Not publishedown pages say only 100s of data sources and hundreds more via Talend Cloud, no exact count givensource, checked 1,000+pre-built connectors across cloud apps, on-prem systems, files, and federated warehousessource, checked ~10 warehouseswarehouse-native by design: Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, AlloyDB, Azure SQL, SQL Server, and Starburst, no direct operational-system connectorssource, checked 14 warehouses/databasesWarehouse-native: Snowflake, BigQuery, Databricks, Redshift, Postgres, MySQL, ClickHouse and more, no broad SaaS connector cataloguesource, checked ~11connects to data warehouses only (Snowflake, BigQuery, Redshift, Databricks, Postgres, MySQL, SQL Server, Azure Synapse, Druid, Trino, MotherDuck), no SaaS app connector catalogue publishedsource, checked Not publisheddocs list about two dozen named databases and file sources plus SaaS access via Fivetran or Airbyte, with no total count givensource, checked 6318 SQL connectors and 45 API connectors, published on TextQL's own connectors pagesource, checked 2 (Snowflake, BigQuery)only two warehouse connectors documented, plus dbt/Astro metadata and four context connectorssource, checked 18-20BigQuery, Snowflake, Databricks, Redshift, Postgres, MySQL, SQL Server, Oracle, ClickHouse, Trino, Athena, Spark and more; one data source per projectsource, checked 750+Catalogue lists 717 sources across 10 categories; only 12 are direct database connections, the rest sync via Fivetran into a managed warehousesource, checked 30+named as Snowflake, BigQuery, dbt, Databricks, and 30+ more, concentrated in warehouses, databases, and orchestration/catalog toolssource, checked 9 warehousesBigQuery, Snowflake, Redshift, Databricks, Postgres, Trino, ClickHouse, Athena and DuckDB, no operational-system connectorssource, checked ~11 namedPostgres, MySQL, SQL Server, Snowflake, BigQuery, Databricks, Supabase, Google Ads, Meta Ads, plus Google Drive/OneDrive/SharePoint; no total catalogue number published, custom connectors on requestsource, checked 100+100+ sources and destinations via the open-source ingestr enginesource, checked 0, warehouse-onlyNo connector catalogue of its own; only queries data already loaded via separate Snowflake ingestion toolssource, checked 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+ connectorssource, checked 30+counted from AWS's own published connector list: relational/warehouse sources, file formats, and a handful of SaaS sourcessource, checked Not publishedApps directory lists many third-party apps, not a single vetted connector catalog with a totalsource, checked Not publishedAnthropic names specific connectors (Google Workspace, Slack, GitHub, Microsoft 365, HubSpot, Asana, Amplitude) but publishes no directory totalsource, checked
97.8%Accuracy on the BI BenchNoneNo accuracy figure publishedNoneNo accuracy figure publishedNoneNo accuracy figure publishedNoneThe published 99.6% is a reduction in time-to-insight, not accuracyNoneNo accuracy figure publishedNoneno accuracy figure published for Qlik Answers' AI-generated responsesNoneno accuracy figure published for Domo AI's answersNoneno accuracy figure for AI answers published on Sigma's own siteNoneNo accuracy figure published for AI-generated answersNoneno measured accuracy or benchmark percentage for Zoë's answers is published; case studies quote customer outcomes, not a product accuracy figure95%+claims 95%+ answer accuracy in production enterprise analytics, methodology not detailed on the pagesource, checked 35.7% (relative)Measures the accuracy gain from reusing the ontology versus starting the same task from scratch, not an absolute accuracy ratesource, checked NoneEvals page describes methodology for testing the agent but publishes no accuracy or correctness percentagesource, checked Noneno accuracy or correctness benchmark is published; correctness is argued architecturally (MDL, memory, validation), not measured94.8%From Basedash's own 'BI Bench', an internal benchmark it runs on one database it chose, ranking itself first of 11 tools; not an independently audited figuresource, checked Noneno accuracy percentage or benchmark is published for Hex Magic or Threads answersNoneno published accuracy benchmark found on Lightdash's own pagesNoneno accuracy or benchmark percentage published on julius.aiNoneno accuracy percentage published; Bruin argues trust comes from shown SQL, lineage, and stable metric definitions instead of a numbersource, checked ~90%Snowflake's own internal benchmark of 150 questions, not independently auditedsource, checked NoneNo company-wide accuracy figure is published; Genie ships a customer-run benchmark tool to test a space's own accuracy privatelyNoneAWS has not published an accuracy figure for Amazon Q's answersNoneOpenAI publishes model benchmarks, not a business-data-analysis accuracy figureNoneAnthropic publishes model benchmarks for reasoning and coding, not a business-data-analysis accuracy figure
—you want answers from your database in minutes, without publishing a data source and building a workbook first.Tableau answers inside Pulse and Ask Q&A, and only after a data source is published and a workbook is built on it. Supaboard reads your schema directly — a first analysis takes about 5 minutes, and the SQL is shown beside every answer.you want anyone to ask questions without a semantic model built in Desktop first, or Premium seats and Fabric capacity for the AI.Copilot is not on the $14 Pro seat — it wants Premium Per User at $24 or a Fabric capacity, on top of a semantic model someone builds in Desktop first. Supaboard puts no model between a question and its answer. 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.one question needs data from more than one database, or you want a dashboard generated from a prompt.Metabase meters its own AI service past the first million tokens, or asks you to bring a provider key. Supaboard's credits are in the seat across 12 databases and warehouses and 22 apps, and the SQL behind every answer is shown, so a wrong number can be traced instead of just distrusted.you want answers before anyone models worksheets, and questions that span more than one connection.ThoughtSpot's search wants worksheets modelled underneath it, and its tiers carry per-query allowances with Pro on a consumption model at $0.10 per credit. Supaboard answers from a connected source in about 5 minutes and is priced per seat. 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.you want to start asking before a LookML model is written, or to ask across more than one connection.Conversational Analytics only sees what LookML describes, and data tokens beyond the monthly allowance are billed from 1 October 2026. Supaboard needs no model written and reviewed like code before the first question. 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.one question needs to span more than one source, and you want the dashboard, alert and report from the same prompt.Qlik's own FAQ admits Qlik Answers works one Qlik Sense app at a time per query, with multi-app querying only 'planned for a future release' — Supaboard already spans every connected source in one question. And where Qlik pairs a chart-at-a-time assistant with a separate no-code automation builder someone has to wire by hand, Supaboard's single agent builds the dashboard, sets the alert, and writes the scheduled report from one prompt.you want one agent for answers, dashboards, alerts and reports, at a price published up front.Domo splits AI, apps, dashboards, and automation across separately-branded products (App Catalyst, Agent Catalyst, Magic ETL, Workflows) that each need their own setup, and doesn't publish a price until after a demo. Supaboard is one agent, tuned on the customer's own metric definitions, that answers the question, builds the dashboard, sets the alert, and writes the report in the same conversation, at a price published today: a free plan, then $19 to $249 per seat a month.you want questions answered across separate connections, with the dashboard or app finished from the same prompt.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 by querying each source and combining the results, finishes the dashboard, app, or report from the same prompt that answered the question, and publishes its price instead of routing you to sales.your questions reach operational databases and apps that never land in a warehouse.Omni needs a warehouse before it can answer anything; Supaboard connects directly to the operational databases that often never reach a warehouse at all, and reads 22 apps such as HubSpot and Stripe live through MCP. And where Omni splits detect, analyze, export, and route across chat, Routines, and the schedules API, Supaboard's single agent does all four from one prompt, then shows its SQL so you can check the work.one question needs to combine several sources, and you want a published accuracy figure and seat price.Zenlytic connects to about a dozen warehouses and stops at each source's boundary; Supaboard answers one question by combining every connected source it needs, and advertises an accuracy figure, 97.8% on the BI Bench, where Zenlytic's own site publishes none. Supaboard's seat prices are also published, so you know the price before you sign, not after a sales call. 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.you want the dashboard, data app, alert and scheduled report built by the agent that answered, at a published price.WisdomAI's context engine is genuinely strong, but it only feeds a chat box and a dashboard that a team still has to assemble by hand from there. Supaboard's agent takes the same kind of governed business context and carries it all the way through: the dashboard, the data app, the alert, and the scheduled export come from one prompt to one agent, with SQL and Python both editable in the same place. And you can see every price without booking a demo to find out.you want a per-seat price, with sources queried live and nothing synced first.TextQL's Team pricing is metered by compute (ACUs), so the bill moves with usage and is hard to forecast; Supaboard's per-seat price ($19 to $249 a month) is published. 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. Supaboard also queries 12 databases and warehouses live and reads 22 apps through MCP, with nothing synced first, and ships in five chat surfaces (Slack, Teams, Claude, ChatGPT, Cursor) against TextQL's documented two (Slack, Teams) — covering more of the business without a sales call to get started.your data sits beyond Snowflake and BigQuery, and one question has to combine sources.Sundial connects to two warehouses (Snowflake and BigQuery) and pairs its analyst with a separate observability product to keep it honest; Supaboard connects 12 databases and warehouses and 22 apps, combines them in one question, and folds the same confidence-scoring and evals into the system that also builds the dashboards, apps, and automations. Supaboard is newer than the legacy BI incumbents too, but between the two AI-native platforms, its bet is breadth-plus-trust in one product rather than trust sold as an add-on layer.you want alerts, scheduled reports and questions that span more than one source.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 every connected source, 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.you want generated data apps, file-based reports and Python alongside SQL.For teams mainly buying dashboards and Slack reports, Basedash may be the cheaper fit. But for a company that wants generated apps, file-based reports, five chat surfaces, and Python alongside SQL, Supaboard covers ground Basedash's current product doesn't.the people asking are not analysts, and you want dashboards, apps and automations from a prompt.Hex gives a data team an excellent shared notebook; Supaboard gives the rest of the company an agent that already knows the business and can act on it. Where Hex's AI generation of apps, dashboards, and workflows is largely in beta layered onto a notebook-first product, Supaboard's dashboards, data apps, scheduled reports, and automations are the product, generated from a prompt and delivered where the team already works — Slack, Teams, Claude, ChatGPT, or Cursor.your questions reach operational systems that are not in a dbt project.Supaboard connects to the operational systems that never make it into a dbt project, so a company doesn't need a modeling effort before its agents can answer a real business question. The same AI that answers questions also builds the dashboard, the data app, and the automation that routes the result, as one system rather than a stack of separately priced add-ons. And AI agents and data apps come with the seat price, and embedding with the Individual seat and up, rather than as three more line items after buying the base product. 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.a whole team needs the same business rules, dashboards, alerts and reports over live connected data.Julius is a fast, pleasant way for one person to turn a file into a chart. Supaboard is built for what comes after that: a whole company's live, connected data, held together by agents that carry the same business rules, dashboards, alerts, and reports across every team member and every session.you want a self-serve alert and workflow builder, a published accuracy figure and a published price.Supaboard's agent is tuned on your own business rules and metric definitions from day one, with the SQL shown beside every answer, and an advertised accuracy figure of 97.8% on the BI Bench — Bruin publishes neither a comparable accuracy number nor a self-serve alert or workflow builder, relying instead on marketing examples. And Supaboard's per-seat pricing is a published number where Bruin Cloud's usage-based pricing beyond its free tier has no public price list. 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.your data is not all in Snowflake, or you want a dashboard, alert or report rather than a SQL answer.Cortex Analyst only ever sees data already loaded into Snowflake and produces one thing, a SQL answer; Supaboard connects directly to databases that never reach a warehouse, reads 22 apps live through MCP, and turns the same context into dashboards, alerts, and scheduled reports from one agent. For a team that isn't Snowflake-only, or that wants the output to be a finished artifact rather than a query result, that's the gap that matters.your data is not all in a Databricks lakehouse, or you want finished dashboards and workflows from the answer.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 the operational databases that never reach a lakehouse, reads 22 apps live through MCP, and turns the same context into dashboards, alerts, reports, and workflows, priced per seat. 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.you want one system that generates the dashboard and reasons about why a number moved.QuickSight is a capable, cheap-to-view BI layer for teams already fully inside AWS, but its AI is a natural-language question-answering feature bolted onto a decade-old product, split across several differently-priced tiers and, increasingly, several different products entirely. Supaboard is one system where the agent generates the dashboard, reasons about why a number moved, and can act on it, priced per seat. 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.the same question comes back every week and needs a shared metric definition, a live source, and a dashboard or alert that stays.ChatGPT is a genuinely strong tool for the analysis one person needs to run once. Supaboard exists for the question your company needs answered the same way every time, with a shared metric definition, a live connection to the actual source system, and SQL you can check rather than trust, plus a dashboard or alert that's still there next week without anyone re-asking for it.the question repeats across your team and needs one governed definition, a live connection, and alerts that fire without a prompt.Claude is excellent at the one-off version of this question: upload a file, get a careful answer, move on. Supaboard exists for the version that repeats, the same metric asked by different people, that needs one governed SQL definition, a live warehouse connection, and a dashboard or alert that fires without anyone re-prompting. The two aren't mutually exclusive: because Supaboard speaks MCP, the protocol Anthropic built, a team can keep living in Claude for everything else and still reach Supaboard's governed agent from inside it.

Also weighed and not given a column: Sisense, Apache Superset, Alteryx, Oracle Analytics, SAS Viya. Every one of the 24tools in the comparison above passed the same bar: seventeen rows sourced to that vendor’s own pages, not a review site or our memory of it. These five did not clear that bar cleanly enough to print. If you are comparing one of those, the alternatives guides on the blog go deeper than a table row can.

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