# Supaboard vs WisdomAI > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to WisdomAI's own pages. HTML version: https://supaboard.ai/compare/wisdom-ai ## At a glance | Row | Supaboard | WisdomAI | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Not published** — no plan or seat price anywhere on the site; the only entry point is a demo request (https://www.wisdom.ai/demo-wisdom-ai, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **Yes** — built and marketed as an agentic analytics platform, not BI software with AI added on (https://www.wisdom.ai/, checked 2026-09-10) | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Yes** — Analytics Agents reason over connected data and take actions like creating tickets or triggering APIs (https://www.wisdom.ai/product/agents, checked 2026-09-10) | | Deep reasoning | **Yes** — Deep Dive answers why a number moved and what to do next, not only what it is | **Partial** — agents run multi-step workflows with conditions and loops, but root-cause or forecasting analysis isn't described (https://www.wisdom.ai/product/agents, checked 2026-09-10) | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **No** — embeddable surfaces are limited to chat and dashboards, not full interactive applications (https://www.wisdom.ai/product/embed, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Partial** — dashboards build from a natural-language prompt as widgets, then the team explores and rearranges them (https://www.wisdom.ai/product/ai-dashboards, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **Yes** — text-to-agentic-workflow builds a full agent that queries data, analyzes it, and takes action like ticketing or alerts (https://www.wisdom.ai/product/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** — agents push anomaly alerts and threshold notifications through Slack, Teams, or email (https://www.wisdom.ai/product/agents, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Partial** — dashboards can be subscribed to on a daily, weekly, or custom cadence via email or Slack (https://www.wisdom.ai/product/ai-dashboards, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **Yes** — one question can span multiple connected sources, e.g. a warehouse, a CRM, and a PDF contract together (https://www.wisdom.ai/product/chat, checked 2026-09-10) | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **No** — no product or docs page mentions a Python execution environment; answers come from AI-generated queries only | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Partial** — an 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 chat (https://docs.wisdom.ai/integrations/mcp-server/MCP-Server, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **Partial** — ships a hosted MCP server exposing chat, list_domains, and echo tools; docs don't describe it consuming other MCP servers (https://docs.wisdom.ai/integrations/mcp-server/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 | **No** — no SQL or code editor is documented; a run visualizer shows the SQL that already ran, but nothing is user-editable | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **Yes** — Adaptive Context Engine ingests docs, Notion, Confluence, Slack, and Jira alongside live warehouse syncs, with drift and conflict tracking and context versioning (https://www.wisdom.ai/product/context, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **Not published** — docs list about two dozen named databases and file sources plus SaaS access via Fivetran or Airbyte, with no total count given (https://docs.wisdom.ai/getting-started/connect-data-sources/supported-data-sources, checked 2026-09-10) | | Answer accuracy | **97.8%** — On LegendEHR's tuned agent in production, with a confidence score on every answer | **95%+** — claims 95%+ answer accuracy in production enterprise analytics, methodology not detailed on the page (https://www.wisdom.ai/, checked 2026-09-10) | Last checked 2026-09-10. WisdomAI pricing page: https://www.wisdom.ai/demo-wisdom-ai ## Why choose Supaboard over WisdomAI 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 the price, $99/seat/month, without booking a demo to find out. ## Supaboard vs WisdomAI, in our own words WisdomAI sells itself as "agentic analytics" for the enterprise: an AI data analyst wired into your warehouse, your apps, and your documents through what it calls an Adaptive Context Engine, with the specific pitch that most AI-analytics tools give wrong answers and WisdomAI's governed context layer is what fixes that. There's no self-serve price anywhere on the site. Every path leads to a demo request. The question worth asking before you take that call: is this a platform you evaluate feature by feature, or a sales process you have to go through before you know what it costs? ### Where the cost actually lands WisdomAI publishes no plan, no seat price, no usage tier. The only entry point is a demo request form. That's consistent with how the product is built, too: it expects a warehouse (or its own BigQuery-managed environment), Fivetran- or Airbyte-style ETL for SaaS sources, and enterprise deployment options like single-tenant VPC and bring-your-own-LLM. None of that is priced upfront. The cost lands in a sales cycle and an implementation project, not a checkout page. ### What is actually different Give WisdomAI credit where it's earned: the Adaptive Context Engine is a genuinely serious piece of engineering. It ingests docs, PDFs, Notion pages, Confluence wikis, Slack threads, and Jira tickets alongside live warehouse connections, tracks context drift and definition conflicts over time, and versions business knowledge the way you'd version code. That's a real, verified strength, not marketing filler. But look at what the context engine feeds into. WisdomAI's own product pages describe exactly three surfaces: Conversational BI (chat), AI-Powered Dashboards, and Analytics Agents. Dashboards are built from natural-language prompts as widgets, but the product still frames it as "WisdomAI builds it, your team explores it" — an iterative, edit-afterward workflow, not a finished data app. There's no data-apps product at all; the embed page only ships chat and dashboards via iFrame, SDK, or GraphQL. There's no SQL or Python editor anywhere in the docs — WisdomAI generates queries behind the scenes and shows you what ran, but you never write or edit the code yourself. And on MCP, WisdomAI ships a server (three tools: chat, list_domains, echo) that Claude, ChatGPT, Cursor, and VS Code can call into — but nothing in its docs describes WisdomAI consuming other MCP servers. It's a one-way door. Distribution tells the same story. WisdomAI can subscribe a dashboard to email or Slack on a schedule and push anomaly alerts and threshold notifications through Slack, Teams, or email — real capabilities, confirmed on their own product pages. What isn't there is a native, queryable chat presence inside Slack or Teams itself (as opposed to receiving a scheduled push), and there's no confirmation anywhere that scheduled deliveries come as PDF, PowerPoint, or Excel exports rather than just a link or a digest. Supaboard's bet is different: one agent, tuned on your business rules and metric definitions, does all of it from a single prompt — the same agent that answers your question also builds the live dashboard on your design system, generates a full data app, sets the threshold or anomaly alert in plain English, writes the scheduled PDF/PPT/Excel report, and builds the detect-analyze-export-route workflow end to end. SQL and Python sit in the same editor, over every connected source, with AI proposing edits as a diff you approve — not a black box that only shows you the SQL after the fact. And MCP runs both directions: Supaboard exposes its own server and can read from others, including Notion, the same way WisdomAI's context engine does. Connector breadth matters here too — WisdomAI's docs name roughly two dozen databases and file sources plus SaaS access through third-party ETL, against Supaboard's 700+, with 124 connectable without a sales conversation. ### What to test if you trial both Give each platform one prompt that has to end in an artifact, not just an answer: "Build a dashboard tracking weekly churn by segment, alert me by Slack if it jumps more than 10% week over week, and send a PowerPoint summary to my team every Monday." See whether one tool does all three steps from that single instruction, or whether you're stitching together a dashboard, a separate alert rule, and a manual export. Then hand each one a real transformation that needs actual code — not a chart, a Python function over the query result — and see which one lets you write and run it instead of describing it and hoping. ## FAQ ### Is WisdomAI cheaper than Supaboard? There's no way to know without a sales call. WisdomAI publishes no pricing anywhere on wisdom.ai — every path leads to a demo request, and typical enterprise agentic-analytics deployments carry warehouse, ETL, and implementation costs on top of whatever the license quote comes back at. Supaboard is $99/seat/month billed monthly, or $83/seat/month billed annually, with usage credits included in the seat and no separate AI metering. ### Can Supaboard connect to the same data WisdomAI already touches? In most cases, yes. WisdomAI's own docs list around two dozen named databases and file sources (Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, and similar) plus SaaS access through Fivetran or Airbyte. Supaboard connects to 700+ sources, with 124 connectable directly without a setup call, so the overlap with anything WisdomAI reaches is typically covered. ### What does Supaboard actually produce, versus just answering questions? WisdomAI's product surfaces are chat, dashboards, and agents that take actions like creating tickets or triggering APIs. Supaboard's agent produces a live dashboard, a full interactive data app on your own design system, a scheduled PDF/PowerPoint/Excel report, and an end-to-end automation (detect, analyze, export, route) — all from the same prompt that answered your original question, with SQL and Python shown for every step. ### Do we still need a data analyst if we switch to Supaboard? For routine questions and standard reporting, no — Supaboard's agents are tuned on your business rules and metric definitions and show their SQL, so a non-analyst can trust and check the answer. For net-new modeling or judgment calls specific to your business, an analyst is still valuable, the same way WisdomAI's own Analytics Agents are built to run alongside, not replace, an analytics team. ### Can Supaboard run in our own VPC like WisdomAI's enterprise deployment? Yes. On the Enterprise plan, Supaboard runs as single-tenant containers in your own environment, using your own model provider keys, with no data egress. WisdomAI advertises the same category of deployment (single-tenant VPC, bring-your-own-LLM) on its enterprise track, and Supaboard offers a directly comparable option. ## 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 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`.