Domo vs Looker

Domo and Looker both help teams use data. But they tend to serve different people best.  

With Looker, power sits primarily with analysts who write LookML. With Domo, power sits with everyone. Domo’s low-code tools make it easy to connect data, build dashboards, automate tasks, and build custom apps without coding skills.  

Domo vs Looker at a glance

Domo is an AI and data products platform built to help anyone, not just data experts, use data to run the business. You can bring in data from almost any system, shape it, explore it, and turn it into dashboards, apps, and automated actions. Everything can be seen in one place, so teams can move from a question to an answer to an action fast.

Looker is a BI tool centered on code-based data modeling that gives companies a way to define and govern metrics. It works for teams that want a semantic layer and have the technical skills to maintain it. (A semantic layer is a centralized way to define and govern data metrics.) With Looker, most of the setup happens in LookML, and data teams play a central role in creating and updating models used across the business.

Both platforms aim to help people make better decisions. The key difference is how easy it is to get there. Domo gives business users more freedom and speed. Looker gives data teams deep control through code-heavy modeling.

Domo vs Looker: Side-by-side comparison

Not sure which tool best meets your data and AI needs? Let’s take a closer look at how they compare.

Data integration: No-code connectors vs complex configuration

Domo brings data in from almost anywhere with simple, no-code connectors. Looker connects well to major warehouses but may need extra tools to bring in other types of data.  

Domo

Domo offers hundreds of pre-built, no-code connectors that let teams pull data in without help from IT. Business users can connect cloud apps, on-prem systems, files, and SaaS tools in minutes. More advanced integrations are supported, too, but most everyday needs stay simple. This gives teams fast access to clean, unified data.

Looker

Looker connects to many SQL databases, with especially deep support for BigQuery, Snowflake, Redshift, and other cloud data warehouses. For SaaS or file-based sources, teams often pipeline data into a warehouse first before Looker can use it. This can add steps for companies with a wide mix of data sources.

Ease of use: Low-code tools vs developer-heavy modeling

Domo is built for both data teams and business users, with tools that are easy to learn. Looker is powerful but relies heavily on LookML and SQL.

Domo

Domo gives users simple, low-code tools for modeling, dashboarding, data prep, and app building. Much of the work can be done without writing code, which helps teams move quickly. Business users can explore data on their own, while data teams still have full control. This mix of skills support makes rollout smoother across the company.

Looker

Looker is centered around LookML, a modeling language that requires technical skills. Analysts gain strong control over definitions and governance, but non-technical users typically depend on those pre-built models. Many updates and changes require developer support, which can slow speed and create bottlenecks. As a result, Looker often works best in teams with dedicated data engineers.

Embedded analytics and custom apps: No-code tools vs developer setup

Domo lets teams embed dashboards and build custom apps with little to no code. Looker supports embedding too but usually needs developers.

Domo

Domo Everywhere makes embedding simple: You can drop live, interactive analytics into portals, apps, and internal tools without technical work. There’s also a low-code app builder so teams can create custom tools and workflows using their data. These apps can include forms, automations, and writebacks to other systems. This helps teams build real solutions, not just reports.

Looker

Looker has strong embedding capabilities, but setup often requires engineers and authentication work, especially for secure, personalized embeds. Custom apps can be built on Looker’s APIs, but they usually involved LookML modeling and developer skills. This limits who can build and maintain them. For many companies, embedded projects need IT support from start to finish.

Mobile experience: Full mobile BI vs basic report viewing

Domo’s mobile app lets people explore, build, and act on data anywhere. Looker’s mobile app is mainly for viewing reports.

Domo

The Domo mobile app supports full data exploration, interactive dashboards, alerts, and collaboration. Users can drill into views, share insights, and take action from their phone. The experience mirrors the desktop, so teams stay connected on the move.  

Looker

Looker’s mobile app is designed mainly for viewing dashboards and saved reports. It supports light filtering and basic drilling, but deeper exploration and dashboard editing are limited. Users often need to return to the desktop for full analysis or content creation. For teams that rely heavily on mobile work, this can slow decision-making.

Automation and workflow: Built-in automations vs IT-managed pipelines

Domo includes automation tools anyone can use. Looker supports workflows but often needs IT involvement.

Domo

Domo includes built-in pipelines, alerts, and workflow actions that can write back to systems or trigger processes. These tools help teams reduce manual reporting and act on data right away. Most automations can be set up without touching code. This makes it easier to turn insight into action.

Looker

Looker offers workflow tools, but most need technical support to set up and maintain. Out-of-the-box actions can handle simple tasks, but more advanced workflows depend on custom endpoints, authentication, or LookML modeling. This often makes automation a technical project rather than a self-service one. As a result, non-technical teams typically rely on data or engineering teams to build and manage these workflows.

Security and governance: Flexible-sharing controls vs warehouse-tied access

Domo uses multi-layer security designed for flexible access and sharing. Looker’s security relies heavily on the connected data warehouse.

Domo

Domo includes row-level permissions, multi-factor authentication, and broad compliance certifications. Security is built into every layer of the platform, not tied to one system. This lets teams share data with the right people while keeping sensitive information protected. It is built for secure scaling across many teams.

Looker

Looker uses a governance model that builds on the security of the connected data warehouse. It also includes its own access filters and permissions, but many sharing and access changes still require updates to LookML models or warehouse roles. This approach protects data but can limit flexibility, especially for teams that want to share data quickly across many users. For fast-moving organizations, these dependencies can add friction.

Visualizations and dashboards: Drag-and-drop freedom vs model-dependent charts

Domo offers a large library of charts and drag-and-drop dashboards anyone can build. Looker’s dashboards are strong but depend on modeled data and often need coding for custom work.

Domo

In Domo, users can build dashboards with simple drag-and-drop tools and explore data without waiting on technical teams. Data updates in real time, keeping insights fresh. This makes dashboards easy to build and easy to trust.

Looker

Looker’s visuals are clean and reliable but tied closely to LookML models. Custom views or more advanced charts typically require developer support, either through LookML or Looker’s visualization API. This gives analysts strong control but can slow down business teams that want to iterate quickly. In many organizations, even small dashboard changes end up routed through the data or analytics team.

AI and predictive analytics: Built-in AI tools vs external ML setup

Domo includes built-in AI and ML tools designed for everyday users. Looker relies more on outside tools and advanced skills.

Domo

Domo brings AI, ML, and predictive insights directly into the platform. Users can run forecasts, detect patterns, and build AI-powered apps without deep technical knowledge. AI tools work alongside dashboards and workflows, helping teams act fast. This gives companies a practical path into AI.

Looker

Looker integrates with BigQuery ML, Vertex AI, and Gemini, but most ML workflows still rely on analysts or data scientists to prepare data, train models, or maintain pipelines. Tools like the ML Accelerator support simpler use cases, but more advanced AI work typically happens outside Looker and is then surfaced back through the platform. This makes AI powerful but less accessible to everyday business users. Many organizations see slower adoption because of the technical steps involved.

Making the strategic choice: Why Domo is the stronger BI and AI platform for most organizations

Domo and Looker both help teams analyze data, share insights, and build a governed analytics foundation. They each offer modern cloud architectures and strong visualization capabilities. But the way they deliver those capabilities—and who can actually use them—differs in meaningful ways.

Domo provides broader accessibility, faster time to value, and a more unified platform. With built-in connectors, low-code tools, full mobile BI, and embedded AI, Domo helps more people across the business use data without relying on developers or IT queues.

Who should choose Domo vs Looker?

Why fast-moving teams choose Domo

You want to:

• Bring data together quickly without stitching together external tools
• Give business users real autonomy with low-code dashboards, apps, and automations
• Embed live analytics anywhere with minimal technical setup
• Scale governance and sharing across many teams without warehouse constraints
• Adopt AI in a practical, accessible way—without requiring data scientists
• Move faster than IT bandwidth or modeling bottlenecks allow
• Support mobile-heavy or field teams with full BI on the go

Where Looker might seem like a fit (but comes with trade-offs)

Looker can be a good choice if you:

• Have a technical team that prefers centralized, code-first modeling
• Want deep control over metric definitions through LookML
• Run primarily on BigQuery, Snowflake, or Redshift and plan to keep data fully in-warehouse
• Are comfortable with embedding and workflows that require engineering support
• Expect analysts or data scientists to manage most ML and automation work

Domo delivers a more flexible, accessible, and complete platform, one that equips every team to move faster and make decisions with confidence. When you need a modern BI and AI platform that truly scales across the business, Domo is the choice that keeps your teams ahead.