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Competitor comparison

Domo vs Omni

Domo asks you to bring your data into its cloud and model it there, while Omni, built by the team behind Looker, leaves data in your warehouse and centers the work on a flexible semantic model.

Quick decision snapshot

Omni is the stronger choice for a data team with a warehouse that wants shared definitions and ad hoc SQL in one tool, plus a growing embedded story since it bought Explo. Domo is the stronger choice for a company without a warehouse or modeling staff that wants ingestion, hosting, alerts, and mobile dashboards handled by one vendor. A Domo customer moving to Omni is taking on a warehouse and ingestion project as well as a BI migration.

Why Domo customers look at Omni

Omni often enters a Domo evaluation when a data team has grown up around the BI tool. The company now has a warehouse, maybe dbt, and analysts who would rather define metrics once next to the data than maintain Magic ETL dataflows and Beast Mode formulas inside a separate vendor cloud. Omni's pitch fits that moment: a semantic model where governed definitions and quick ad hoc queries live side by side.

Ownership adds urgency for some buyers. Progress Software closed its acquisition of Domo in September 2026 and expects to realize cost synergies by the end of its 2027 fiscal year, which leaves customers watching for changes in packaging and support. Omni, meanwhile, has been expanding through its own purchase of Explo.

Where Domo still has the advantage

Domo covers ground Omni does not attempt. It pulls data from more than 1,000 sources, stores it, and lets business analysts transform it visually, so there is no warehouse to run and no modeling language to learn. Its distribution layer is mature too: mobile apps, threshold alerts, scheduled delivery, and App Studio for custom data apps.

For an organization whose bottleneck is getting data in at all, rather than modeling it well, that bundle saves real effort. The tradeoff is that Domo becomes a second copy of your data with its own logic.

Where Omni pulls ahead

Omni treats the semantic model as the center of analytics, with less ceremony than LookML. Analysts can write SQL, promote useful logic into shared definitions, and let business users explore against them, all on the warehouse your team already runs. AI chat and analysis draw on that semantic context, and the Explo acquisition in 2025 gave Omni a dedicated product for customer-facing embedded analytics.

Its cost is upfront design. Business users get the most from Omni after the data team has built the model, so value can take longer to reach non-technical teams. Phone access and threshold alerts get less attention than in Domo.

Domo and Omni compared

CriterionDomoOmni
Data locationDomo's cloud, loaded through 1,000+ connectorsYour warehouse: Snowflake, BigQuery, Redshift, Databricks
Modeling approachMagic ETL dataflows and Beast Mode formulas edited in the UIA semantic model where shared definitions and ad hoc SQL coexist
Who modelsBusiness analysts and BI adminsThe data team, before broad self-serve
Embedded analyticsDomo Everywhere plus App Studio for custom appsNative embedding, with Explo added in 2025
AIAgent Builder and an MCP server for outside assistantsAI chat and analysis grounded in the semantic model
Mobile and alertsMature apps and threshold alertsWeb-focused, with lighter alerting
Pricing posturePlatform fees plus consumption creditsPer-user pricing through a sales process

Domo is the better match for

Companies with no warehouse that need data ingested and hosted.

Non-technical analysts who prefer visual pipelines to modeling.

Field and executive teams who rely on mobile alerts.

Omni is the better match for

Data teams with a warehouse that want definitions modeled once.

Analysts who want SQL and governed exploration in the same tool.

SaaS companies planning customer-facing analytics on the same model.

Shared definitions without a long modeling phase first

Basedash aims at the delay Omni can introduce. Basedash Models hold shared measures and segments, but the AI also handles questions nobody has modeled yet, showing the SQL so a data person can check it, so business users are not waiting on the model to be finished. Internal dashboards and embedded analytics use the same definitions.

Compared with Omni, it also brings what Domo customers would otherwise have to rebuild: a managed warehouse and 750+ connectors. Teams that want deep, data-team-owned semantic modeling as the core of their practice may still find Omni the better fit.

Also compare each against Basedash

FAQ

What does a Domo-to-Omni migration involve?

It involves a data stack, not just a BI tool. Omni queries a warehouse, so Domo customers who relied on Domo for storage need to pick one, such as Snowflake or BigQuery, and add an ingestion tool to replace Domo's connectors. Dataflow and Beast Mode logic then moves into warehouse transformations and Omni's semantic model, and dashboards are rebuilt on top. If Cloud Amplifier already points Domo at an outside warehouse, part of that work is done. Budget data-team time for the modeling phase.

Does Omni's purchase of Explo make it a better embedding choice than Domo?

For SaaS companies shipping analytics to customers, it strengthens Omni's case. Omni acquired Explo in 2025, adding a product built for white-labeled, multi-tenant dashboards inside other applications, on top of Omni's own embedding. Domo offers Domo Everywhere for embedded Cards and App Studio for custom data apps, which work well when embedding is a modest extension of internal reporting. Because Domo embeds rely on data ingested into Domo's cloud, teams that want tenant data to stay in their own warehouse usually lean toward Omni.

Is Omni harder for business users than Domo?

At the start, usually. Domo lets business analysts create DataSets, Beast Mode fields, and Cards themselves, so they can build without waiting on anyone. Omni's best experience comes after the data team has modeled the key entities and metrics; until then, non-technical users have less to explore. Over time Omni tends to produce more consistent numbers, because definitions are shared rather than duplicated across many user-built DataSets. The right choice depends on whether speed or consistency is your bigger problem.

What if a team wants Omni's shared definitions sooner?

Basedash is aimed at teams that like Omni's single-definition approach but cannot wait for a full modeling phase before business users get answers. Anyone can ask questions in plain English and review the generated SQL, Basedash Models capture definitions as they are agreed, and the same metrics power internal dashboards and embeds. Domo customers also get hosted storage and 750+ connectors in the same contract rather than sourcing them separately. BI Bench ranked Basedash first overall.

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