# Supaboard vs Omni > Seventeen capabilities compared, from price and connectors to AI analysts, alerts, MCP support and answer accuracy, each figure sourced to Omni's own pages. HTML version: https://supaboard.ai/compare/omni ## At a glance | Row | Supaboard | Omni | | --- | --- | --- | | Price | **$99/seat/mo** — Or $83 billed annually. Usage credits are in the seat | **Quote-based** — No published price anywhere on omni.co, every path leads to a demo request (https://omni.co/demo, checked 2026-09-10) | | AI native | **Yes** — The agent is the product, not a panel added to a dashboard tool | **Partial** — Built around a workbook and semantic layer first, with an AI agent layered on top as one of several query modes (https://omni.co/, checked 2026-09-10) | | AI data analysts | **Yes** — Agents tuned on your business rules and metric definitions, with the SQL shown beside every answer | **Partial** — An agentic coordinator plans, queries, and evaluates results, but it's not tuned on a customer's specific business rules (https://omni.co/ai, 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** — Coordinator plans multi-step queries and evaluates results, but no published root-cause or forecasting mode (https://omni.co/ai, checked 2026-09-10) | | Data apps, AI made | **Yes** — Live data apps on your own design system, from a prompt | **Yes** — Apps are AI-generated interactive tools built from a prompt on the semantic model, can take input and write back (https://omni.co/apps, checked 2026-09-10) | | Dashboards, AI made | **Yes** — A live dashboard from a single prompt | **Yes** — Dashboard Builder plans queries, picks charts, and lays out a full dashboard from a single prompt (https://omni.co/ai, checked 2026-09-10) | | AI workflows | **Yes** — One prompt builds the whole chain: detect, analyse, export and route the result | **No** — Routines run a scheduled prompt and reply in plain text to email or Slack, they cannot edit dashboards or export files (https://docs.omni.co/ai/routines, 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 routines watch for a stated condition in plain language and notify by email or Slack when it's met (https://docs.omni.co/ai/routines, checked 2026-09-10) | | Automated reporting | **Yes** — Scheduled reports written by an agent and delivered as PDF, PowerPoint or Excel | **Yes** — Scheduled delivery as CSV, PDF, PNG, or Excel via email, Slack, webhook, S3, or SFTP, no PowerPoint export (https://docs.omni.co/api/schedules/create-schedule, checked 2026-09-10) | | Cross-source data query | **Yes** — One question spanning every connected source | **No** — Docs describe each model as built on one connection, no published support for joining across separate connections in one question | | Python scripts | **Yes** — SQL and Python in one editor, over every connected source | **No** — Workbook query modes are point-and-click, SQL, and spreadsheet formulas, no Python execution mode found in product pages or docs | | Slack, Teams, Claude, ChatGPT | **Yes** — Slack, Teams, Claude, ChatGPT and Cursor | **Slack, MCP clients** — Native Slack agent plus an MCP server reachable from Claude, ChatGPT, Cursor, and Copilot, no native Teams agent (https://docs.omni.co/ai/mcp, checked 2026-09-10) | | MCP support | **Yes** — In both directions: we expose one, and we read yours | **One-way** — Ships an MCP server so external AI tools can query Omni, no published support for Omni consuming other MCP servers (https://docs.omni.co/ai/mcp, checked 2026-09-10) | | Query bench | **Yes** — SQL and Python with schema-aware autocomplete, and AI edits proposed as a diff you approve | **Yes** — SQL workbook that automatically parses and restructures queries, used alongside modeled data in the same workbook (https://omni.co/product, checked 2026-09-10) | | Context and memory management | **Yes** — Rulesets, knowledge, dropped files and MCP sources such as Notion | **Semantic layer** — Written as code with two-way dbt sync, or built just-in-time from the UI as you analyze, then promoted into the shared model (https://omni.co/data-modeling, checked 2026-09-10) | | Connectors | **700+** — Connectable, 124 of them without talking to us | **14 warehouses/databases** — Warehouse-native: Snowflake, BigQuery, Databricks, Redshift, Postgres, MySQL, ClickHouse and more, no broad SaaS connector catalogue (https://docs.omni.co/connect-data/setup/, 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 published for AI-generated answers | Last checked 2026-09-10. Omni pricing page: https://omni.co/demo ## Why choose Supaboard over Omni Omni needs a warehouse before it can answer anything; Supaboard connects to 700+ sources, including the operational systems, CRM, support, ad platforms, that often never reach a warehouse at all. 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. ## Supaboard vs Omni, in our own words Omni pitches itself as the modeling layer BI never had: a semantic layer you write as code or build just-in-time from the UI, with two-way dbt sync, sitting under a workbook that mixes point-and-click exploration, SQL, and spreadsheet formulas. It's the closest thing on this list to a real Looker successor, and it treats AI as a client of that model rather than the model itself. There is no listed price anywhere on omni.co. Every pricing path dead-ends at a demo request form. The question worth asking isn't whether Omni's semantic layer is good, it clearly is, it's whether a governance-first modeling tool with AI layered on top is what most teams need, versus an agent built from day one to be the whole interface. ### Where the cost actually lands Omni won't quote a number until sales has talked to you. That's normal for enterprise BI, but it means every dollar figure in a proposal is negotiated, not published, so comparing "Omni" to a competitor's list price is really comparing a number to a conversation. Supaboard publishes both tiers up front: $99/seat/month monthly, $83/seat/month billed annually, with usage credits already inside the seat, no separate AI meter to negotiate around later. ### What is actually different Omni's agent, per its own docs, is a coordinator that "plans actions, selects tools, executes queries, evaluates results, and decides what to do next," which is real multi-step reasoning, but it stops at answering the question. Turning that answer into a scheduled, monitored, delivered process takes a separate feature, Routines, and Routines have real limits: they reply in plain language to email or Slack only, and by Omni's own documentation "cannot edit dashboards, they can only inspect and report on a dashboard referenced in a prompt." Scheduled dashboard exports are a third, separate mechanism (the schedules API supports csv, pdf, png, xlsx and json, delivered via email, Slack, webhook, S3, or SFTP), so a buyer ends up composing three different subsystems, chat, routines, schedules, to get from a question to a dashboard to a recurring, routed report. Supaboard runs that as one agent: the same agent that answers your question also builds the dashboard, sets the threshold or anomaly alert, and writes the scheduled PDF/PowerPoint/Excel report, because detect-analyze-export-route is one prompt, not three products. Omni does have things Supaboard doesn't try to match. Its data modeling story is genuinely strong: write the model as code with two-way dbt sync, or skip that and model just-in-time from inside the UI as you analyze, then promote what's valuable into the shared model. That's a real, distinct idea, and it's why analytics engineers like Omni. But it requires a warehouse first. Every one of Omni's 14 documented connections (Snowflake, BigQuery, Databricks, Redshift, Postgres, MySQL, ClickHouse, and others) is a data warehouse or database; there's no catalogue of SaaS or operational-system connectors. Supaboard's 700+ connectors include the operational systems, Salesforce, Zendesk, ad platforms, that often never make it into a warehouse. Omni's MCP server is real and works in one direction: Claude Desktop, ChatGPT, Cursor and Copilot can query Omni's model, but nothing in Omni's docs describes it consuming other MCP servers. Supaboard's MCP works both ways. Omni has no native Slack integration for Python; on data science tooling specifically, Omni's own product and docs describe SQL and formulas as the code paths in a workbook, Python doesn't appear as a query mode anywhere we could find. Supaboard runs SQL and Python in the same editor over every connected source. And on governance, Omni's model is the semantic layer itself, one shared, versioned definition everyone queries against. Supaboard's is per-agent rules, knowledge, and memory, a more conversational model that trades some of that single-source-of-truth rigor for agents that can be tuned per team without touching a shared model. ### What to test if you trial both Ask each product the same operational question that spans two genuinely different systems, say, ad spend from a marketing platform against pipeline data that has never touched your warehouse, and watch what each one needs before it can answer. Then ask each to turn that answer into a recurring Monday report that lands in Slack as a formatted export. That sequence surfaces the real difference: whether you're waiting on a data engineer to land a new source in the warehouse first, and whether "automate this" means one prompt or three separate configurations. ## FAQ ### How does Omni's pricing compare to Supaboard's? Omni doesn't publish pricing; every quote comes after a demo call with sales. Supaboard publishes both tiers: $99/seat/month billed monthly, or $83/seat/month billed annually, with usage credits already included in the seat, no separate AI metering to negotiate. ### Can Supaboard read the same data Omni is already connected to? Yes. Supaboard connects to the same warehouses Omni does, Snowflake, BigQuery, Databricks, Redshift, Postgres, and more, alongside 700+ other sources, so you don't have to migrate or re-point anything Omni already touches. ### What does Supaboard actually produce, versus an answer in chat? A live dashboard, a full interactive data app built on your own design system, a scheduled PDF/PowerPoint/Excel report, or a threshold/anomaly alert, all from the same prompt and the same agent, with the SQL shown beside every answer. ### Do we still need a data analyst if we use Supaboard instead of Omni? For day-to-day questions, no, the agent is tuned on your business rules and metric definitions and shows its SQL so you can check its work. Omni's semantic layer still benefits from someone maintaining the model, which is a real ongoing role either way. ### Can Supaboard run in our own VPC instead of Supaboard's cloud? Yes, on the Enterprise plan: single-tenant containers, your own model provider keys, and no data egress. Omni offers a single-instance deployment option with git and dbt for enterprise customers as well; ask both vendors for the specifics of what stays in your network. ## 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 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`.