Comparing Hex vs. Looker

Thinking of switching from Looker to Hex? Modern data teams make the switch to Hex to power faster and deeper insights with AI.

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Trusted by leading data companies

"Our vision for Notion’s data team is that anyone, regardless of technical proficiency, is comfortable using data to answer their own questions – and Hex enables that."Abhishek Modi, Software Engineer
"We like to embed Hex dashboards where teams work."Ian Macomber, Head of Data and Analytics Engineering
"We’re regarded as top performers because the work we are doing is deemed more credible and more widely surfaced than before Hex."Kate Urquiola, Director, Go-to-Market Analytics
"Hex has helped us empower business analysts to explore and dig into data on their own"Meghana Reddy, Head of Data at Stubhub

Why companies switch from Looker to Hex

Quick questions. Deep analysis. All in plain language.

Looker requires non-technical users to grasp dimensions, measures, and Explores—just to answer simple questions.

With Hex, any question can be explored in plain language. Business users self-serve through Threads, while data teams can explore code-based analysis with the Notebook Agent.

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Comparison breakdown

Features

Hex

Looker

AI self-serve

Business users self-serve in plain language

Hex

Threads lets users ask questions in plain language, iterate with follow-ups, and explore answers without knowing SQL or the model.

Looker

Conversational Analytics supports simple, one-shot natural-language questions, but lacks multi-step reasoning.

AI code-based analysis

Generate and refine SQL, Python, and logic with AI

Hex

Notebook Agent assists with writing and refining SQL, Python, and charts; reasons through multi-step logic and debugging.

Looker

No AI support for code generation, debugging, or building analyses.

Seamless collaboration

Continuity of context between business users and data teams

Hex

The seamless handoff from a Thread to a Notebook keeps full context and continuity for inspectability and extension.

Looker

Inspecting underlying logic or extending analysis is limited to Looker’s Explore interface.

No-code exploration

Spreadsheet, drag-and-drop based analysis

Hex

Supports no-code exploration with tables, pivots, and charts for drag-and-drop analysis.

Looker

Supports GUI-based exploration of modeled data through Explores, filters, pivots, and visualizations.

From static dashboards to advanced data apps

With data apps in Hex, you can turn code-based explorations, like forecasting, what-if models, and root-cause analysis, into interactive experiences for end users.

While Looker dashboards are fine for monitoring KPIs, they don’t support complex computation or advanced interactivity.

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Comparison breakdown

Features

Hex

Looker

Interactive apps

Dynamic exploration beyond dashboards

Hex

Data apps include inputs and controls (dropdowns, sliders, date pickers, text boxes) enabling truly interactive analysis.

Looker

Dashboards are static with basic filter controls; no input-driven logic or custom interactions.

Advanced analytics

Computation and modeling built in

Hex

Data apps run live Python and SQL, enabling forecasting, what-if scenarios, simulations, and other advanced analyses directly in the app.

Looker

Dashboards display static query results. No embedded computation, modeling, or parameter-driven logic.

Reporting dashboards

Standard KPI and metric tracking

Hex

Build dashboards using no-code charts or full Python visualization libraries.

Looker

Create dashboards with customizable, Explore-powered visualizations based on LookML models.

Flexible governance, unconstrained exploration

LookML provides governance but locks you into a proprietary modeling layer. Every new question triggers edits, reviews, and redeploys.

Hex is flexible: build models in Hex or sync them from dbt, Snowflake, or Cube. When questions go beyond the model, explore freely with agentic notebooks, without switching tools.

Compare features

Comparison breakdown

Features

Hex

Looker

Ad hoc exploration

Explore unmodeled data freely, all in one place

Hex

Explore frontier questions and new data with SQL, Python, and no-code, all in one environment.

Looker

Exploration is limited to what’s already modeled; you can’t freely query new tables or run code-based analysis.

Interoperable modeling

Use or sync models from multiple sources

Hex

Build models in Hex or sync semantic definitions from dbt, Snowflake, or Cube.

Looker

Logic must be defined in LookML; no support for importing or syncing external semantic layers.

AI-assisted model creation

Jumpstart models using existing project context

Hex

Modeling Agent can reference existing notebooks, queries, and assets to bootstrap new semantic models with natural language.

Looker

Gemini can generate LookML, but cannot reference existing analyses as context for new models.

Unify the full cycle of data work with Hex.

Leave disconnected workflows behind.

Compounding accuracy

Deep analysis

Trusted context

Conversational self-serve

Hear from data teams that chose Hex

Learn how the best companies in the world increase team efficiency using Hex.

"OM1 uses Hex to work with constantly evolving, massive datasets and to provide a user-friendly, dynamic data application."

"Hex has helped us empower business analysts to explore and dig into data on their own"

"Calendly's analytics team is 2x more efficient in Hex."

"We're amazed at how useful Hex has been for the needs of a data science team while also being easy to use for our product engineering teams."

View all Customer stories

FAQ

What’s the main difference between Looker and Hex?

Not every question a business asks fits neatly inside a predefined model. New datasets, joins, and edge-case logic constantly create questions that fall outside the model. In Looker, anything beyond that model forces data teams to leave the tool, explore elsewhere, and rebuild the logic back in. This slows iteration and creates friction for business users who need quick answers to new questions.

Hex is built for this reality. Business users can self-serve in plain language on top of modeled data, and data teams can explore complex, unmodeled questions with SQL, Python, and AI in one connected workspace. Each new answer adds to shared context, allowing data teams to power agents to handle more of the organization’s questions, faster.

What can I do with Hex that I can’t do in Looker?

With Hex, you can:

Explore unmodeled data using SQL, Python, and no-code together in reactive notebooks.
Ask and iterate in plain language with Threads, without needing to understand modeling concepts in LookML or Explores.
Turn advanced analysis into an interactive data apps powered by live notebook logic.
Work in one connected workspace, so exploration, iteration, and sharing stay in sync.

In Looker, anything outside the predefined LookML model requires leaving the tool to explore elsewhere and then rebuilding that logic back in LookML. This slows iteration and limits the questions non-technical users can answer.

Isn't Hex mainly for data teams? What about non-technical users?

Hex gives business users approachable ways to engage with data, like interactive apps and natural-language queries, while still offering the depth analysts need for technical work. Both groups share the same workspace, which means stakeholders get answers faster and data teams don’t have to rework analysis across multiple tools.

How is Hex different from traditional BI tools?

BI dashboards are built to show what happened—fixed KPIs and standard cuts of the data. Hex is built to answer how it happened, why it happened, and what if it changes. With data-team-first workflows in a notebook that supports SQL, Python, and no-code, teams can move quickly from simple KPI lookups to deep, iterative analysis, without bouncing between tools.

Why combine BI and technical data workflows in one tool?

Business questions come in all shapes and forms. Sometimes it’s a simple KPI trend, other times it’s “why is this happening?” or “what can we expect next?”, which requires deeper analysis. Splitting BI and technical analysis across different tools creates friction, duplicate effort, and inconsistent answers. Hex unifies it all in one place so the workflow stays connected.

Can you create dashboards in Hex?

Yes. Hex supports traditional dashboards, but also goes further, letting teams build interactive data apps, parameterized views, and even workflows with predictive models. That flexibility means you can cover executive reporting and deeper analysis in the same place, instead of maintaining separate tools.

Are there no-code ways for users to explore data in Hex?

Yes. Hex lets users self-serve insights with natural language Q&A, interactive charts, pivot tables, spreadsheet-style views, and simple formula-based calculations—all without writing SQL or Python.

Should I build semantic models in Hex?

Hex takes an interoperable approach to semantic models, giving you the flexibility to build directly in Hex or sync from other sources. Building in Hex lets you define and edit models right where you already work with data—with autocomplete, inline validation, and AI-assisted code generation in the modeling workbench.

Alternatively, you can sync existing semantic models from dbt, Snowflake, or Cube. Either way, your semantic models live alongside notebooks, self-serve, and agents, ensuring governed, reusable logic powers every workflow.

How can we start evaluating Hex?

Anyone can sign up for a free 14-day trial of Hex's Team plan. If you'll require more time to evaluate Hex or are curious about our Enterprise plan, please reach out and we'll get you set up!

Can't find your answer here? Get in touch.