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Competitor comparison

Mode vs Omni

Both tools are built for data teams that write SQL, but they disagree about whether that SQL should stay inside individual reports or move into a shared model.

Quick decision snapshot

Pick Mode if your analysts are the main authors and you want each report's query to stay visible and editable in one place. Pick Omni if the same metrics keep getting rewritten across reports and you are ready to model them once so stakeholders can explore without a new query each time. If neither the analyst queue nor a modeling project suits your team, Basedash sits between the two.

Mode treats the query as the unit of work

In Mode, a report starts as a SQL query written against the warehouse, then picks up charts, parameters, and commentary before it is shared or scheduled. Python and R notebooks sit next to the query results for analysis SQL handles poorly, and Helix keeps exploration of a result set fast. The logic behind any number is right there in the report, which analysts like because nothing is hidden behind a modeling layer.

The cost of that transparency shows up later. When revenue or active users is defined in a dozen reports, each copy can drift, and a stakeholder who wants a new cut usually needs an analyst to write another query. Mode also expects modeling to happen upstream, typically in dbt, rather than inside the tool. ThoughtSpot has owned it since 2023, which matters to buyers who weigh roadmap independence.

Omni treats the model as the unit of work

Omni comes from the team behind Looker, and it shows: a semantic model defines metrics, joins, and dimensions once, and dashboards, workbooks, and AI chat all read from it. Unlike a rigid LookML-style commit cycle, Omni lets ad hoc SQL and spreadsheet-like exploration happen in the same workspace as the governed definitions. For a Mode team tired of copy-pasted metric logic, that is the main attraction.

The tradeoff is upfront work. A person, usually an analytics engineer, has to design the model and keep it current before business users get reliable answers, and as a newer platform its integration ecosystem is still filling in. Omni also acquired Explo in October 2025, so teams with customer-facing analytics on the roadmap get an embedded story that Mode does not focus on.

How Mode and Omni handle the same metric

CriterionModeOmni
Where a metric is definedInside each report's SQL, or upstream in dbtOnce, in Omni's semantic model
Who authors new analysisSQL-fluent analystsAnalysts model; stakeholders explore within the model
Ad hoc workSQL editor plus Python and R notebooksSQL and spreadsheet-like workbooks on modeled data
AI assistanceAssistant that writes and explains SQL and summarizes resultsAI chat that answers from the model's definitions
Getting startedQuick: connect the warehouse and write a querySlower: value grows as the model fills out
Customer-facing embedsReports can be embedded, but it is not a focusStrengthened by the Explo acquisition
OwnershipPart of ThoughtSpot since 2023Independent company

Mode fits teams where

Analysts answer most questions themselves and stakeholders read the finished report.

Metric definitions already live in dbt, so the BI tool does not need its own model.

Work regularly moves from SQL into Python or R within the same analysis.

Omni fits teams where

The same KPI has been rewritten in many reports and the numbers no longer match.

A data team has capacity to own a semantic model as ongoing work.

Business users should explore and chat with data without a fresh query for every cut.

Customer-facing embedded dashboards are on the roadmap.

Shared definitions without a modeling project first

Basedash keeps what Mode analysts value, SQL you can read, and adds what Omni promises, shared metric definitions, without making a modeling project the first step. A stakeholder types the question the way they would ask a colleague, Basedash drafts the SQL from your shared definitions, and any analyst can open that query and correct it. Answers also reach Slack, and automations take over the recurring reports a Mode team would schedule by hand.

It is a weaker fit if your analysts want a Python and R notebook as their daily workspace, or if you already have modelers who want the fine-grained control Omni gives them. On BI Bench, Basedash ranked first with 94.8% accuracy; neither Mode nor Omni was part of that test.

Also compare each against Basedash

FAQ

How do teams move from Mode to Omni?

The usual path starts with the metrics that appear in the most Mode reports, such as revenue, active customers, or pipeline. Those definitions get modeled once in Omni, and the dashboards that depended on them are rebuilt against the model, with numbers checked against the old Mode reports before anyone switches. One-off analyses and notebook work often stay in Mode or get rewritten only when needed, since they rarely benefit from a shared model. Expect the modeling step, not the dashboard rebuilds, to take the most time.

Can Mode and Omni run side by side?

Yes, either during a transition or permanently. A common split puts the dashboards and exploration that business teams use every week in Omni, on shared definitions, and leaves analysts' ad hoc SQL and Python work in Mode. The risk is two places defining the same metric. Teams that keep both should give every KPI a single owning tool and keep definitions upstream in dbt where possible, so Mode queries and the Omni model read from the same tables.

Does Mode's ownership by ThoughtSpot matter in this choice?

It is one input among several. Since ThoughtSpot bought it in 2023, Mode's roadmap has been decided inside a larger company that also sells its own search-led analytics product. Omni is independent and has been expanding, including its 2025 purchase of Explo. Neither fact tells you which product fits better today, but teams signing multi-year contracts should ask both vendors about roadmap and pricing commitments.

When is Basedash a better fit than Mode or Omni?

Basedash fits when the real problem is that business teams wait on analysts, and you do not want to solve it by building a semantic model first. Stakeholders type their question in ordinary language, get back a chart whose SQL an analyst can audit, and can send results to dashboards, Slack, or scheduled automations. It suits teams less well if their analysts mainly want a Python notebook, or if modelers are ready to invest in Omni's model. A 14-day free trial lets you test it on real questions.

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