Embeddable vs Looker Embedded

SaaS teams assessing embedded analytics solutions often compare two fundamentally different models.

Looker Embedded delivers enterprise grade BI built on LookML, emphasizing internal analytics, governed modeling and standardized reporting across complex organizations.

Embeddable takes a developer first approach tailored to customer-facing dashboards, offering native embedding, complete UI control and performance tuned for multi-tenant SaaS.

This guide breaks down both tools across architecture, developer experience, customization, performance, security, multi-tenancy, pricing and migration complexity to help product and engineering leaders decide with confidence.

TLDR

Looker Embedded:

• Works best for internal business intelligence / analytics.
• Data modeling with LookML.
• Embedding relies on iframes or Looker’s Embed SDK.
• UX always looks and behaves like Looker.
• Pricing and infrastructure tied to Google Cloud and Looker’s enterprise tier.
  
  

Embeddable:

• Designed specifically for customer facing SaaS analytics.
• No iframes, native embedding that renders inside your DOM.
• Fully customizable UI and UX - looks like part of your app
• Multi-layer caching and optimized performance for multi tenant workloads.
• Predictable flat pricing with unlimited users.
  
  

Quick Overview: Embeddable vs Looker Embedded at a Glance

Embedding method:

• Embeddable - Native DOM rendering with web components, React and Vue (no iframes). Two way interaction with your app logic and UI state.
• Looker Embedded - iframe based embedding or the Looker Embed SDK. Always loads Looker’s UI container. Limited interaction beyond SDK surface.

Customization:

• Embeddable - Fully customizable components and layouts. Matches your design system. Extendable with your own charting libraries.
• Looker Embedded- Limited UI control. Dashboards retain Looker’s visual identity. Only minor theming available.

Performance:

• Embeddable - Fast-loading. Multi layer caching including L1 in memory and L2 pre aggregations. Optimized for real time customer facing analytics.
• Looker Embedded- Performance tied to LookML model complexity and warehouse speed. Embedded dashboards load inside iframes, adding UI overhead. Explore based workflows can be slow for multi-tenant SaaS traffic.

Multi tenancy:

• Embeddable - Server-issued security tokens enable tenant-aware row-, column-, and database-level security. Supports shared schemas, isolated DB per tenant, or multiple database connections. No duplication of data models per tenant.
• Looker Embedded - Complex to manage at scale. Multi tenancy typically requires: Separate Looker user accounts per tenant; Role based access rules; Sometimes separate Explore permissions or filtered LookML views

Setup time:

• Embeddable - Fast implementation with default setup. Option for deeper customisation & further time spent on setup. Very low maintenance.
• Looker Embedded - Heavy initial setup. Requires LookML modeling, content hierarchy, roles and permission sets. Requires Google Cloud billing.

Integration depth:

• Embeddable - Components render directly inside your DOM. Full control over events, variables and app logic.
• Looker Embedded - Looker stays sandboxed inside an iframe. Passing in and out variables on-change is not possible. May require users to login twice depending on setup.

Pricing:

• Embeddable - Flat predictable pricing with unlimited users and dashboards.
• Looker Embedded - Enterprise pricing based on usage, consumption, hosting and Google Cloud footprint. Can be expensive for SaaS products with many tenants.

Deployment:

• Embeddable - Cloud or self hosted.
  Looker Embedded - Google Cloud hosted Looker only.

End user experience:

• Embeddable - Feels fully native to your product. Enables end-users to build their own custom views of dashboards.
• Looker Embedded - Feels like Looker inside your product regardless of styling.

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What is Looker Embedded

Overview and core philosophy

Looker is an enterprise BI platform built around LookML, a data modeling layer that centralizes metrics and business logic. It was built for data teams and excels when large internal teams need consistent definitions and governed reporting.

Looker Embedded extends Looker’s dashboarding capabilities to customer-facing applications using iframes or the Embed SDK. It uses the same architecture for embedded analytics as Looker’s main internal BI tool.

This makes it powerful for standardized analytics but less flexible for SaaS products that need custom UI, fast loading and tenant aware experiences.

Strengths of Looker Embedded

• Enterprise grade governed modeling through LookML.
• Strong role based permission system.
• Rich Explore interface for internal data exploration.
• Deep integration with Google Cloud and BigQuery.
  
  

Limitations for embedded SaaS use cases

• Embedding is iframe based and constrained.
• Looker dashboards always retain Looker’s UX and layout.
• End users see a third-party BI tool, not your own product experience.
• Multi tenancy is complex to manage at scale.
• Requires significant modeling investment in LookML.
• Can introduce query latency depending on warehouse load - slow loading.

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What is Embeddable

Overview and philosophy

Embeddable is a developer-first toolkit designed specifically for customer facing analytics in SaaS products. It enables teams to easily embed highly customizable dashboards directly in the DOM, with code driven control, native rendering and performance architecture built for multi-tenant SaaS.

Embeddable focuses on speed, flexibility and user experience; while offering full UI ownership and efficient caching strategies for an excellent developer experience.

Key strengths of Embeddable

• Native rendering via web components with no iframe limitations.
• Fully customizable UI and UX down to the pixel.
• Broad and extendable charting library.
• High performance via multi tier caching.
• Powerful end user self serve dashboard builder.
• Modern developer workflow with versioning and CI/CD.
• Tenant aware RLS with simple, secure token based access.
  
  

Ideal use cases

• SaaS platforms offering analytics to customers.
• Multi tenant environments with large concurrent usage.
• Replacing Looker embeds where UI control or performance is lacking.
• Replacing legacy BI tools for customer-facing dashboards / embedded analytics.
• Migrating away from expensive custom built dashboards.

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Feature Comparison Deep Dive

1. Developer experience

Looker Embedded

• Requires LookML modeling of datasets and metrics.
• Requires Looker projects, Git integration and content permissions.
• Embedding relies on iframes and Looker Embed SDK.
• Explore and dashboard interactions remain Looker controlled.
• Custom visuals require extension frameworks.
  
  

Embeddable

• Simple setup.
• Native SDKs for React, Vue and JavaScript.
• Optional code-level control over components and interactions.
• Easy-to-use theming with unlimited power
• Charts and dashboards can be easily assembled by non-technical teams.
• Clear versioning, deployment environments and CI/CD control.
  
  

What it implies
Looker is powerful but complex and was originally built for internal data teams.
Embeddable matches how modern SaaS engineering teams ship product features.

2. Performance and scalability

Looker Embedded

• Query performance depends on the warehouse (BigQuery, Snowflake, etc).
• Rendering dashboards inside an iframe adds UI overhead.
• LookML joins can get complex and slow with large models.
• Explore usage across many tenants can stress underlying compute.
  
  

Embeddable

• L1 in memory cache for hot queries.
• L2 pre aggregations for large datasets.
• Caching API for orchestrating refresh and invalidation rules.
• DOM native rendering for minimal overhead.
  
  

What it implies
Looker is built for internal analytics workloads, not high concurrency SaaS.
Embeddable is intentionally optimized for multi-tenant customer facing analytics at scale.

3. UI customization and design fidelity

Looker Embedded

• The Looker dashboard layout and toolbar are always present.
• Only light theming available.
• Layouts, filters and interactions remain Looker controlled.
• Cannot adopt your design system deeply.
  
  

Embeddable

• Full control over UI using CSS, JS and component props.
• Supports your own charting libraries and custom visualizations.
• Dashboards feel natively part of your SaaS product.
• End users build dashboards without breaking brand consistency.
  
  

What it implies
Looker makes your product feel like Looker.
Embeddable makes analytics feel like your product.

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4. Security and multi tenancy

Looker Embedded

• Uses Looker user roles, groups and access filters.
• Multi tenant setups commonly require:
  
  • Dedicated Looker users per tenant
  • Row level access filters in LookML
  • Complex permission sets
• Hard to scale to thousands of customers.
  
  

Embeddable

• Server generated tokens define tenant scope.
• Row level, column level and object level policies.
• No dataset duplication required.
• SOC2 Type 2, GDPR and enterprise security standards.
  
  

What it implies
Looker can support multi-tenancy but becomes difficult to manage at scale.
Embeddable was designed for SaaS multi-tenancy from the ground up.

5. Pricing and ROI

Looker Embedded

• Enterprise pricing based on consumption, users and compute.
• Google Cloud billing applies.
• Embedding can be costly when serving thousands of tenant dashboards.
  
  

Embeddable

• Flat pricing.
• Unlimited users, viewers and dashboards.
• Predictable cost structure for SaaS companies.
  
  

What it implies
Looker makes financial sense for internal enterprise BI.
Embeddable makes financial sense for customer facing SaaS analytics.

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When to Choose Looker Embedded

Looker is best for:

• Internal analytics within large enterprises.
• Organizations that are already heavily invested in LookML.
• Teams needing strong governance and centralized metric definitions.
  
  

Where Looker falls short:

• Dashboards do not feel native in a SaaS application.
• Iframe user experience is limiting.
• Multi tenant setups are complex to implement and maintain.
• Expensive for large external user bases.
• Slow loading speeds.

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When to Choose Embeddable

Embeddable is best for:

• SaaS products with customer facing dashboards or reports.
• Teams that want full control over UX and interactions.
• Multi tenant environments needing scalable RLS.
• Developer driven organizations wanting modern CI CD workflows.
  
  

Added value of Embeddable:

• No iframe overhead.
• Native rendering that feels built into your product.
• Predictable pricing as usage grows.
• End user self-serve capabilities - users can build custom dashboards in your application.
• Reduced engineering overhead compared to Looker.

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Case Studies and Proof Points

• Pledge, for supply chain sustainability analytics.
• Fashion Cloud, with new revenue opportunities driven by embedded analytics.
• Resident Advisor, with large scale live event data analytics.
• HONK, with improved product management experimentation and reduced engineering load.
• mpathic AI, with seamless branding and user experience integration.

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FAQ

Is Looker good for embedded analytics‍

It is powerful for internal use and enterprise governance. However iframe based UX, limited customization and complexity make it less suited for SaaS products.

Does Looker support multi tenancy

‍Yes but it requires complex setups involving LookML filters, roles and dedicated users. It is not built natively for SaaS scale multi-tenancy.

Does Embeddable support multi tenant RLS

‍Yes through tenant-aware security tokens and simple, secure access policies.

How customizable is Looker

‍Customization is limited. Dashboards always retain Looker’s layout and components.

How customizable is Embeddable

‍Extremely customizable with unlimited extensibility at code level, ability to import custom charts and apply your design system in full.

How performant is Embeddable

‍Very performant due to multi layer caching and lightweight rendering.

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