Domo vs Looker: what each is built for, and when neither fits

Domo vs Looker compared: what each is built for, what it needs before the first answer, and a file-first option when neither fits.

By CoviragePublished 24 September 20268 min read

The short answerDomo bundles connectors, preparation, dashboards and apps billed by credits; Looker queries an existing warehouse through a LookML model a data team writes. Domo suits organisations that want connectors, data preparation, dashboards and automation from a single vendor; Looker suits organisations that want one governed set of metric definitions shared across teams and tools. When the need is one desk's question answered from the exports it already has, a file-first tool may fit better than either.

Domo and Looker are both good at what they are built for. Domo bundles connectors, preparation, dashboards and apps billed by credits; Looker queries an existing warehouse through a LookML model a data team writes. This guide sets them side by side, fairly, and ends with the case where a team needs neither.

At a glance

	Domo	Looker	Covirage
Built for	Organisations that want connectors, data preparation, dashboards and automation from a single vendor; Companies that want to give dashboards to many people without buying a licence per user	Organisations that want one governed set of metric definitions shared across teams and tools; Companies with a SQL data warehouse, particularly BigQuery users already on Google Cloud	The person waiting for the answer, in a big or medium company
How it gets data	Domo ingests data through more than 1,000 cloud connectors, file uploads, email attachments, SFTP, APIs and on-premises connectors (Domo Workbench), and stores it in its own cloud. It can also run federated queries on live data in warehouses such as Snowflake, BigQuery and Redshift without copying it. Data is transformed with Magic ETL, SQL DataFlows or DataSet Views.	Live query: Looker generates SQL from the LookML project and submits it to the customer's database connection (BigQuery, Snowflake, Redshift, PostgreSQL, SQL Server, Oracle and many other dialects). SQL Runner also allows direct SQL against those connections.	Files and sheets now; scheduled delivery by API, S3, Azure, SFTP or email on Business; connections built with your team on Enterprise
Needs before the first answer	Connector set-up for each source, or Workbench for on-premises data; Data pipelines in Magic ETL or SQL DataFlows to clean and join data; Data prepared with metadata and context ("AI readiness") for AI Chat to give accurate answers	A SQL database or warehouse holding the data; LookML developers to model tables, joins and measures before business users explore; Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting	The exports your systems already produce. No warehouse, no modelling language, no data team
AI	AI Chat lets users ask questions in natural language and returns answers, visualisations or recommendations from their data. The AI SQL assistant turns plain-language requests into SQL queries and Beast Mode formulas. Agent Catalyst is used to build, test and deploy custom AI agents, and Domo offers hosted models from providers such as OpenAI and Anthropic or connections to a customer's own models. DomoGPT is described as a suite of private models within the Domo cloud for chatting, querying and summarising.	Conversational Analytics uses Gemini for Google Cloud to interpret a natural-language question, map it to fields in the LookML model, and compose a Looker query through the model's Explores rather than writing free-form SQL. It then analyses the query results to answer the question and can create visualisations. Administrators must enable the Gemini in Looker setting.	The AI model chooses a measure and writes the explanation; our tools do the arithmetic and check every total
Published pricing	Domo does not publish list prices. It uses a credit-based model: credits are consumed by actions such as storing data, updating tables, running workflows and using AI, with no per-user charges. A 30-day free trial requires no credit card. A third-party guide from August 2026 reports paid contracts starting at about $30,000 a year.	Platform editions (Standard, Enterprise and Embed) and user licences (Developer, Standard and Viewer) are sold on annual commitments priced through Google Cloud sales; no list prices are published. Conversational Analytics is metered in data tokens, with monthly allowances included per platform tier (for example 60M input and 1.2M output tokens on Standard); overage is listed at $3.00 per 1M input and $20.00 per 1M output tokens, though Google states billing is not yet enforced during a promotional period. Data Studio (formerly Looker Studio) is free, with a paid Pro tier offering a 30-day trial.	Three tiers on one sheet, shared in a conversation

Domo in brief

Domo is a cloud platform that combines data integration, business intelligence dashboards, low-code apps and AI agents in one product. Customers connect sources through pre-built connectors, prepare data with the visual Magic ETL tool or SQL, and build dashboards, alerts and workflows on top. In September 2026 Progress Software completed its purchase of substantially all of Domo's AI and data platform business.

Josh James founded Domo in 2010, and the company was headquartered in American Fork, Utah.
Domo listed on the Nasdaq under the ticker DOMO on 29 June 2018.
Progress Software completed its $400 million acquisition of substantially all of Domo's AI and data platform business on 22 September 2026.
Progress said the acquisition brought a customer base of over 2,400 businesses.

Looker in brief

Looker is Google Cloud's enterprise business intelligence platform. Data teams describe their database in LookML, Looker's modelling language, and Looker then generates SQL from that model and runs it against the connected database to power Explores, dashboards, scheduled deliveries and embedded analytics. It is separate from Google's free report builder, formerly Looker Studio and now called Data Studio.

Looker was founded in 2012 in Santa Cruz, California by Lloyd Tabb and Ben Porterfield.
Google announced its $2.6 billion acquisition of Looker on 6 June 2019 and completed it in February 2020.
Looker's documentation describes it as a tool that generates SQL queries from a LookML project and submits them against a database connection.
Looker platform editions are sold on annual commitments through Google Cloud sales, with no published list price.

Where Domo fits best

Organisations that want connectors, data preparation, dashboards and automation from a single vendor.
Companies that want to give dashboards to many people without buying a licence per user.
Teams pulling data from many cloud applications without a separate integration tool.

Where Looker fits best

Organisations that want one governed set of metric definitions shared across teams and tools.
Companies with a SQL data warehouse, particularly BigQuery users already on Google Cloud.
Product teams embedding governed analytics into customer-facing applications.

Where they differ

Data

Domo. Domo ingests data through more than 1,000 cloud connectors, file uploads, email attachments, SFTP, APIs and on-premises connectors (Domo Workbench), and stores it in its own cloud. It can also run federated queries on live data in warehouses such as Snowflake, BigQuery and Redshift without copying it. Data is transformed with Magic ETL, SQL DataFlows or DataSet Views.

Looker. Live query: Looker generates SQL from the LookML project and submits it to the customer's database connection (BigQuery, Snowflake, Redshift, PostgreSQL, SQL Server, Oracle and many other dialects). SQL Runner also allows direct SQL against those connections.

AI

Domo. AI Chat lets users ask questions in natural language and returns answers, visualisations or recommendations from their data. The AI SQL assistant turns plain-language requests into SQL queries and Beast Mode formulas. Agent Catalyst is used to build, test and deploy custom AI agents, and Domo offers hosted models from providers such as OpenAI and Anthropic or connections to a customer's own models. DomoGPT is described as a suite of private models within the Domo cloud for chatting, querying and summarising.

Looker. Conversational Analytics uses Gemini for Google Cloud to interpret a natural-language question, map it to fields in the LookML model, and compose a Looker query through the model's Explores rather than writing free-form SQL. It then analyses the query results to answer the question and can create visualisations. Administrators must enable the Gemini in Looker setting.

What each needs before the first answer

Domo:

Connector set-up for each source, or Workbench for on-premises data.
Data pipelines in Magic ETL or SQL DataFlows to clean and join data.
Data prepared with metadata and context ("AI readiness") for AI Chat to give accurate answers.

Looker:

A SQL database or warehouse holding the data.
LookML developers to model tables, joins and measures before business users explore.
Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting.

When neither fits

Both assume a team that will build and maintain a model of the data before questions get answered. Some teams have a narrower need: one desk, one recurring question such as "what is driving my costs" or "where is my revenue coming from", and exports that already exist. For them, Covirage reads those files, answers the question with a bridge that sums to the change, and is set up for them within a week. The AI model chooses the measure and explains it; our tools do the arithmetic.

See the full pages on Domo alternatives and Looker alternatives, or how to evaluate an analytics vendor.

Sources

Facts about other products were checked on 24 September 2026 from the pages below. Product names are trademarks of their owners; Covirage is not affiliated with them.

Questions people ask

What is the main difference between Domo and Looker?

Domo bundles connectors, preparation, dashboards and apps billed by credits; Looker queries an existing warehouse through a LookML model a data team writes.

Which is easier to start with, Domo or Looker?

It depends on what is in place. For Domo: Connector set-up for each source, or Workbench for on-premises data; Data pipelines in Magic ETL or SQL DataFlows to clean and join data; Data prepared with metadata and context ("AI readiness") for AI Chat to give accurate answers. For Looker: A SQL database or warehouse holding the data; LookML developers to model tables, joins and measures before business users explore; Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting. Check both against your team and your data before comparing licence prices.

Is there an alternative to both?

For a desk that needs to explain why its numbers moved, from exports it already produces, Covirage answers from the file and is set up for you within a week. It does not replace a platform for the whole organisation.