Domo vs Looker
Domo and Looker serve distinct segments of the BI market. Domo delivers an all-in-one platform with built-in ETL, 1,000+ connectors, and proprietary data storage that appeals to organizations wanting a single vendor for the entire data pipeline. Looker prioritizes governed, semantic-layer-driven analytics on top of existing cloud warehouses, making it the stronger choice for teams that already invest in modern data stacks and need version-controlled, reusable metrics. Budget-conscious mid-market teams that value data integration breadth will lean toward Domo, while data-mature organizations on Google Cloud that prize governance and embedded analytics will find more value in Looker.
Domo4.2Looker4.4
Business Intelligence
Quick Comparison
| Feature | Domo | Looker |
|---|---|---|
| Best For | All-in-one BI with built-in ETL, connectors, and mobile-first dashboards | Governed analytics with LookML semantic modeling and embedded BI for SaaS products |
| Pricing Model | Hybrid model combining per-user license fees with consumption credits. Minimum viable deployment starts at $30,000/year ($2,500/month). Small teams (10-25 users): $1,200 to $3,000/user/year ($100 to $250/month). Mid-market (50-100 users): $1,000 to $2,000/user/year. Enterprise (200+ users): $750 to $1,500/user/year. Very large (500+ users): Custom pricing. Consumption-based credits included in enterprise tiers. | Standard $99/mo, Premium $299/mo, Enterprise custom |
| Data Integration | 1,000+ pre-built cloud connectors, on-premises connectors, Magic ETL, SQL DataFlows | Direct query against cloud warehouses; no native ETL or data storage |
| Semantic Layer | Beast Modes for calculated fields; no dedicated modeling language | LookML modeling language with Git version control and reusable metrics |
| Deployment | Cloud-native SaaS with proprietary data storage and Adrenaline engine | Google Cloud Platform integration with SSO via Google Cloud IAM |
Domo
- Best For:
- All-in-one BI with built-in ETL, connectors, and mobile-first dashboards
- Pricing Model:
- Hybrid model combining per-user license fees with consumption credits. Minimum viable deployment starts at $30,000/year ($2,500/month). Small teams (10-25 users): $1,200 to $3,000/user/year ($100 to $250/month). Mid-market (50-100 users): $1,000 to $2,000/user/year. Enterprise (200+ users): $750 to $1,500/user/year. Very large (500+ users): Custom pricing. Consumption-based credits included in enterprise tiers.
- Data Integration:
- 1,000+ pre-built cloud connectors, on-premises connectors, Magic ETL, SQL DataFlows
- Semantic Layer:
- Beast Modes for calculated fields; no dedicated modeling language
- Deployment:
- Cloud-native SaaS with proprietary data storage and Adrenaline engine
Looker
- Best For:
- Governed analytics with LookML semantic modeling and embedded BI for SaaS products
- Pricing Model:
- Standard $99/mo, Premium $299/mo, Enterprise custom
- Data Integration:
- Direct query against cloud warehouses; no native ETL or data storage
- Semantic Layer:
- LookML modeling language with Git version control and reusable metrics
- Deployment:
- Google Cloud Platform integration with SSO via Google Cloud IAM
Community & Adoption Signals
| Metric | Domo | Looker |
|---|---|---|
| TrustRadius rating | 8.5/10 (253 reviews) | 8.4/10 (457 reviews) |
| PyPI weekly downloads | — | 4.5M |
| Search interest | 0 | 12 |
| Product Hunt votes | 15 | 73 |
As of 2026-05-04 — updated weekly.
Interface Preview
Domo
Looker
Feature Comparison
| Feature | Domo | Looker |
|---|---|---|
| Data Integration & ETL | ||
| Pre-built Data Connectors | 1,000+ cloud connectors plus on-premises connectors via Workbench | Connects to cloud warehouses (BigQuery, Snowflake, Redshift) via direct query |
| Built-in ETL Tools | Magic ETL (visual, no-code) and SQL DataFlows for custom transformations | No native ETL; relies on external tools like dbt or Dataform |
| Data Storage | Proprietary storage with Adrenaline engine for in-memory queries | No data storage; queries run directly against your warehouse for always-fresh results |
| Semantic Modeling & Governance | ||
| Semantic Layer | Beast Modes for ad hoc calculated fields; no centralized modeling language | LookML defines reusable metrics, joins, permissions, and derived tables in a governed layer |
| Version Control | No native Git integration for data models | Git-integrated version control for all LookML models |
| Row-Level Security | Personalized data permissions with trusted attributes and custom user roles | Row-level and column-level security with audit features for enterprise governance |
| Visualization & Dashboards | ||
| Chart Types | 150+ chart types with 7,000+ custom maps | Standard chart library with Looker Studio for interactive drag-and-drop reports |
| Self-Service Exploration | Drag-and-drop Analyzer with Beast Modes for business user exploration | Explores let users drill into governed data; Looker Studio offers ad hoc analysis |
| Mobile Experience | Dedicated mobile app with full dashboard and alert functionality | Mobile-responsive dashboards through web browser; no dedicated mobile app |
| Embedded Analytics & APIs | ||
| Embedded Analytics | Embedded analytics with branding toolkit, bi-directional filters, and mobile-optimized UI | Robust embedding and white-labeling options with full API coverage for SaaS products |
| API & SDK Access | APIs, SDKs, and webhooks for custom integrations and app development | API-first platform with REST APIs, SDKs, and Looker Extensions for custom workflows |
| AI & Advanced Analytics | ||
| AI Capabilities | Domo.AI with AI agents, conversational data exploration, and AutoML via SageMaker Autopilot | Gemini-powered Conversational Analytics for natural language queries and Vertex AI integration |
| Data Science Workspaces | Jupyter Workspaces integration with Python and R for in-platform model development | Machine Learning Accelerator via Looker Marketplace; relies on BigQuery ML or Vertex AI |
| Natural Language Queries | AI-powered conversational exploration through Domo.AI | Conversational Analytics powered by Gemini for chat-with-your-data workflows |
Data Integration & ETL
Pre-built Data Connectors
Domo1,000+ cloud connectors plus on-premises connectors via Workbench
LookerConnects to cloud warehouses (BigQuery, Snowflake, Redshift) via direct query
Built-in ETL Tools
DomoMagic ETL (visual, no-code) and SQL DataFlows for custom transformations
LookerNo native ETL; relies on external tools like dbt or Dataform
Data Storage
DomoProprietary storage with Adrenaline engine for in-memory queries
LookerNo data storage; queries run directly against your warehouse for always-fresh results
Semantic Modeling & Governance
Semantic Layer
DomoBeast Modes for ad hoc calculated fields; no centralized modeling language
LookerLookML defines reusable metrics, joins, permissions, and derived tables in a governed layer
Version Control
DomoNo native Git integration for data models
LookerGit-integrated version control for all LookML models
Row-Level Security
DomoPersonalized data permissions with trusted attributes and custom user roles
LookerRow-level and column-level security with audit features for enterprise governance
Visualization & Dashboards
Chart Types
Domo150+ chart types with 7,000+ custom maps
LookerStandard chart library with Looker Studio for interactive drag-and-drop reports
Self-Service Exploration
DomoDrag-and-drop Analyzer with Beast Modes for business user exploration
LookerExplores let users drill into governed data; Looker Studio offers ad hoc analysis
Mobile Experience
DomoDedicated mobile app with full dashboard and alert functionality
LookerMobile-responsive dashboards through web browser; no dedicated mobile app
Embedded Analytics & APIs
Embedded Analytics
DomoEmbedded analytics with branding toolkit, bi-directional filters, and mobile-optimized UI
LookerRobust embedding and white-labeling options with full API coverage for SaaS products
API & SDK Access
DomoAPIs, SDKs, and webhooks for custom integrations and app development
LookerAPI-first platform with REST APIs, SDKs, and Looker Extensions for custom workflows
AI & Advanced Analytics
AI Capabilities
DomoDomo.AI with AI agents, conversational data exploration, and AutoML via SageMaker Autopilot
LookerGemini-powered Conversational Analytics for natural language queries and Vertex AI integration
Data Science Workspaces
DomoJupyter Workspaces integration with Python and R for in-platform model development
LookerMachine Learning Accelerator via Looker Marketplace; relies on BigQuery ML or Vertex AI
Natural Language Queries
DomoAI-powered conversational exploration through Domo.AI
LookerConversational Analytics powered by Gemini for chat-with-your-data workflows
Our Verdict
Domo and Looker serve distinct segments of the BI market. Domo delivers an all-in-one platform with built-in ETL, 1,000+ connectors, and proprietary data storage that appeals to organizations wanting a single vendor for the entire data pipeline. Looker prioritizes governed, semantic-layer-driven analytics on top of existing cloud warehouses, making it the stronger choice for teams that already invest in modern data stacks and need version-controlled, reusable metrics. Budget-conscious mid-market teams that value data integration breadth will lean toward Domo, while data-mature organizations on Google Cloud that prize governance and embedded analytics will find more value in Looker.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Frequently Asked Questions
What is the main difference between Domo and Looker?
Domo is an all-in-one BI platform with built-in ETL, 1,000+ data connectors, and proprietary data storage. Looker is a semantic-layer-driven analytics platform that queries your existing cloud warehouse directly using LookML, without storing data itself. Domo handles the entire data pipeline in one tool, while Looker focuses on governed analytics on top of an existing modern data stack.
How does pricing compare between Domo and Looker?
Domo uses a hybrid consumption-based model combining per-user license fees with credit-based usage charges. The minimum viable deployment starts at $30,000/year, with per-user costs ranging from $750 to $3,000/user/year depending on team size. Looker offers Standard ($99/mo), Premium ($299/mo), and Enterprise (custom) plans with annual commitments. Both platforms require contacting sales for accurate enterprise-level quotes.
Which platform is better for embedded analytics?
Both platforms offer strong embedded analytics, but they approach it differently. Looker provides an API-first platform with robust white-labeling and embedding options designed for SaaS companies building data products. Domo offers embedded analytics with a branding toolkit, bi-directional filters, and mobile-optimized UI. Looker is generally favored for deeply integrated product analytics, while Domo works well for customer-facing dashboards within its all-in-one ecosystem.
Do I need a data warehouse to use Domo or Looker?
Domo does not require a separate data warehouse because it includes proprietary data storage and an in-memory Adrenaline engine. You can connect to 1,000+ sources and store data directly in Domo. Looker requires an existing cloud data warehouse (such as BigQuery, Snowflake, or Redshift) because it queries data in place without storing it. This means Looker always returns fresh results but depends on your warehouse for performance and storage.
Which platform has better AI capabilities in 2026?
Both platforms have invested heavily in AI. Domo offers Domo.AI with customizable AI agents, AutoML via Amazon SageMaker Autopilot, Jupyter Workspaces for Python and R model development, and conversational data exploration. Looker integrates with Google Gemini for Conversational Analytics, Vertex AI for custom AI workflows, and BigQuery ML for in-warehouse machine learning. The choice depends on your cloud ecosystem: Domo provides a self-contained AI toolkit, while Looker leverages the broader Google Cloud AI stack.