Skip to content
Start free
Log inStart free
Competitor comparison

Looker vs Mode

Both are built for data teams, but Looker invests in a model that others explore, while Mode, now part of ThoughtSpot, invests in the analyst writing the query.

Quick decision snapshot

If your analysts spend their week on new, one-off questions, Mode's SQL-first reports fit how they already work. If they spend it answering the same questions for different stakeholders, Looker's modeled Explores let those stakeholders answer them alone, at the cost of building and maintaining LookML.

Looker: do the SQL once so others never have to

Set beside Mode, Looker looks like an investment in leverage. Instead of writing a fresh query for every request, analytics engineers encode joins and measures in LookML, and Looker generates the SQL whenever a business user picks fields in an Explore. The marketing lead filtering signups by channel and the CFO checking revenue by region both get queries written from the same definitions, without an analyst in the loop.

That leverage only arrives after the model exists. Building LookML that covers real business questions takes months, and every new metric or table becomes a model change that someone has to write and review. Looker is a poor fit for investigative work that will never be repeated, because modeling a question you will ask once is wasted effort.

Mode: the analyst's own environment

Mode was designed for people who think in SQL. Analysts write queries, build charts on the results, collaborate in notebooks, and publish reports that stakeholders can read and refresh. When the question is new, messy, or exploratory, few setups are faster than an analyst with direct SQL access and a place to share the output.

The cost is that business users mostly consume. They can view and filter a published report, but creating or meaningfully changing one sends them back to the data team. Without a semantic layer, each report holds its own logic, so two analysts can define the same metric slightly differently. Some users also find the interface dated next to newer tools, and since ThoughtSpot acquired Mode, buyers should ask how its roadmap fits ThoughtSpot's.

How the two workflows differ

CriterionLookerMode
Who writes the SQLLooker generates it from the LookML modelAnalysts write it by hand for each report
Shared metric definitionsCentral measures every Explore inheritsNone enforced; logic lives in each query
A stakeholder's follow-up questionAnswered in an Explore if the fields are modeledUsually becomes a new request to an analyst
Exploratory, one-time analysisAwkward unless the data is already modeledThe core use case, in SQL and notebooks
Upfront workMonths of LookML before broad useConnect a database and start querying
AI assistanceGemini in Looker for conversational questions over modeled fieldsAI help layered onto the analyst's SQL workflow
OwnershipPart of Google CloudPart of ThoughtSpot

Looker is usually better for

Data teams tired of rewriting the same query for each department.

Companies that want business users exploring modeled fields without SQL.

Orgs that need governed numbers in embedded or customer-facing views.

Mode is usually better for

Analyst teams whose work is mostly new investigations rather than repeat reporting.

Organizations where stakeholders are happy to read reports analysts publish.

SQL-fluent teams that want results today without a modeling project.

When the real problem is the request queue

Teams comparing Looker and Mode are usually trying to get repeat questions off analysts' plates. Basedash attacks that directly: a product manager or ops lead types the request, Basedash generates the query from shared measures in Models, and any analyst can open that query to check it. Analysts keep control of the definitions without writing every query or modeling every field in advance.

Basedash is less suited to a pure SQL notebook practice; analysts who want Mode's editor-first workflow for deep investigations may prefer it. And teams that want a full modeling language with Git review will find more of that in Looker.

Also compare each against Basedash

FAQ

Does Mode have a semantic layer like LookML?

No. Mode is organized around analyst-written SQL, so each report carries its own query logic and there is no central model that enforces one definition of a metric. Looker is the opposite: LookML defines tables, joins, and measures once, and every Explore and dashboard queries through it. If metric consistency across many dashboards is your main concern, Looker addresses it structurally. With Mode, consistency comes from team conventions and code review of queries rather than from the tool.

Can a data team use Looker and Mode together?

Some do, splitting the work by type: Looker for standardized dashboards and self-service Explores on well-modeled data, Mode for exploratory SQL and one-off deep dives that would not justify modeling. The arrangement works if everyone agrees that official KPIs come from Looker and that Mode analyses are investigations. Without that rule, analysts recreate core metrics in Mode queries, and you end up with the same drift Looker was bought to prevent, plus two contracts to maintain.

What does ThoughtSpot's ownership of Mode mean for buyers?

Mode is now part of ThoughtSpot, a company whose main product is search-driven analytics on a modeled layer. For a buyer, that is mostly a diligence item: ask how Mode's roadmap, pricing, and support are planned alongside ThoughtSpot's platform, and whether features you depend on will be maintained. Looker has its own version of this question, since it sits inside Google Cloud and its direction is closely tied to BigQuery and Gemini. Neither ownership is a reason to rule a tool out on its own.

When does Basedash make more sense than Looker or Mode?

Basedash makes sense when the goal is letting non-analysts answer recurring questions themselves without first building a LookML model. A question typed in everyday language comes back as a chart built on the team's shared measures and segments, with SQL an analyst can audit, and it can be saved as a dashboard, a Slack answer, or an automation. Analysts stay in control of definitions. Stay with Mode if your team's value is mostly in exploratory SQL, and pick Looker if analytics engineers should own every metric as code.

We can help you migrate your data and dashboards from any other tool.

Start freeBook a demo