Supaboard vs Omni

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 every tier up front: a free plan, then $19 to $249 per editor a month ($16 to $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.

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 connects directly to the operational databases that often never make it into a warehouse, and reads 22 apps, HubSpot, Stripe and Jira among them, live through their MCP servers. 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, signups in a production Postgres database against revenue in a billing database 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.