DataBrain vs. Looker Embedded :
A comparison guide for Embedded Analytics in 2025

For many SaaS teams, embedded analytics has shifted from a “nice to have” to a core part of the product experience. Looker, Google’s analytics platform, brings a strong semantic layer (LookML), strict governance controls, and tight BigQuery integration. It fits organizations that need centralized metric definitions, complex analysis, and alignment with the Google Cloud stack. For a broader view of Looker Embededded alternatives in the embedded analytics market, see our comprehensive guide:

DataBrain approaches the problem from the opposite direction: starting with SaaS embedding requirements and working backward. It provides programmatic multi tenancy via Guest Tokens, automatic SQL-level RLS, a web components and React based embedding model, and managed infrastructure that scales with load. For most SaaS teams this translates into shorter implementation cycles, predictable flat pricing, and lower ongoing effort for security and governance in multi-tenant setups.

The rest of this article goes through a practical, engineering oriented comparison of both tools: multi tenancy design, RLS mechanics, embedding architecture, data modeling and governance flows, operational workload, and total cost of ownership, so you can map each platform to your current stack and growth plans.

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DataBrain vs. Looker Embedded: TL;DR Comparison

That same strength on the enterprise BI side can become a constraint in embedded, multi-tenant SaaS environments. Teams often spend weeks translating product requirements into LookML models, designing multi-tenant patterns, and wiring up RLS rules. Looker typically assumes access to experienced data engineers and BI architects and comes with layered licensing that mixes platform and per-user components. Multi tenancy is usually handled on shared instances through user attributes and access filters, which is powerful but sensitive to design mistakes as tenant counts grow. Embedding relies on the Embed SDK plus signed URLs, so you need backend services to sign embeds, store secrets, and synchronize user attributes. Looker shines at governed metrics and complex relational logic, but its DNA is still enterprise BI first and embedded second.

DataBrain Pros and Cons

DataBrain is purpose-built for SaaS teams that want embedded analytics to behave like a native part of the product, not a bolted-on BI tool. It focuses on programmatic multi-tenancy, simple embedding, and predictable pricing so you can move from prototype to production without wrestling with workspaces, capacity planning, or per-user licensing.

Pros:

• Purpose-built for SaaS embedded analytics from the ground up
• Programmatic multi-tenancy with zero per-tenant RLS configuration via Guest Tokens
• Automatic RLS enforcement at SQL query layer; policies configured once, tenant filtering automatic
• Native web components and React integration; true white-labeling with DOM-level customization
• Fast implementation (teams report working embedded dashboards in days using guest token flow)
• AI features designed for non-technical end users
• Predictable flat-rate pricing ($999-$1,995/month); no capacity meters or per-user fees
• Minimal DevOps/DBA overhead; managed cloud option available
• Unlimited end-user viewers at any price tier
• Tenant isolation and RLS are token-driven and mostly automatic; simpler role management than Looker's group/model hierarchy
• SOC 2 compliant and supports HIPAA use cases with BAA agreements
  

Cons:

• Relatively newer platform; smaller enterprise case study library
• Limited data discovery capabilities compared to Looker's search-driven analytics
• Semantic layer is newer and less mature in ecosystem scale vs Looker's LookML
• Natural-language interface is focused on Chat Mode rather than full search-driven exploration
• Smaller ecosystem compared to Looker's extensive partner integrations

Looker Embedded Pros and Cons

Looker is positioned as an enterprise-grade analytics platform with a long track record in governed BI and embedded use cases. It shines when you need a rich semantic layer, strict governance, and a mature API surface across complex data environments.

Pros:

• Powerful semantic layer (LookML): Central governance of metrics, dimensions, and business logic
• Enterprise-grade data governance: Single source of truth for metrics; ensures consistency across analytics
• Deep BigQuery integration: Native performance optimization for cloud data warehouse workloads
• Search-driven analytics: Intuitive exploration with recommendations and drill-down capabilities
• Rich visualization library: Advanced charts, interactive reports, and custom visualization support
• Generative AI integration: Conversational analytics powered by Google's Gemini LLMs
• Advanced RLS capabilities: User attributes, access filters for fine-grained row-level segmentation
• Embed SDK and signed URLs: Flexible embedding with extensive customization options
• Trusted authentication: Native SAML, OIDC, SSO, and trusted auth support
• Google Cloud ecosystem: Seamless integration with GCP services, IAM, and BigQuery

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Cons:

• Complex multi-tenancy design: User attributes, access filters, and group management grow complex as tenant count increases
• Enterprise pricing model: Per-user and platform components require sales negotiation; typically high five to six-figure yearly range for embedded deployments
• Multi-tenant implementation complexity: In practice, many teams report multiple weeks of planning, modeling, and RLS configuration
• RLS rule management at scale: User attributes, access filters, and groups require careful design and ongoing maintenance
• Looker originated as enterprise BI: Originated as an enterprise BI platform and later added embedded support; DataBrain is architected from day one for SaaS embedded scenarios
• Signed URL complexity: Requires custom backend infrastructure to sign URLs and manage embed secrets
• Iframe-based embed: Embedded experiences built on iframe model with theming and custom branding, but often retain Looker UI chrome and offer less DOM-level control than component-based approaches
• Group and user management overhead: Manual provisioning of groups and user attributes required for multi-tenant setups
• Requires skilled teams: Data engineers, BI architects, DevOps expertise needed for multi-tenant deployment

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What should you choose?

The decision turns on how much complexity you are willing to own.

If you are building a SaaS product and want embedded analytics to ship quickly, feel native, and stay predictable in cost as you add tenants and end users, DataBrain offers a faster, flatter path: programmatic multi tenancy via Guest Tokens, SDK driven embedding, automatic SQL level RLS, and flat rate pricing that does not punish adoption.

If you operate in a complex enterprise environment, need a highly expressive semantic layer, and have a BI or data engineering team ready to invest in modeling, governance, and multi environment lifecycle management, Looker can provide a powerful, governed analytics backbone, with the trade off of longer timelines, higher licensing costs, and greater operational overhead for multi tenant embedded scenarios.

The aim of this comparison is to make those trade offs explicit so you can choose the platform whose architecture, implementation profile, and cost model actually match how your SaaS product is built and how your team works.

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Deep Engineering Comparison

Multi-Tenancy Architecture: Programmatic Vs Managed with User Attributes

Multi-tenancy architecture drives implementation complexity, operational burden, and scalability.

Looker: Shared Instance + User Attributes & Access Filters (RLS Pattern)

Looker's multi-tenancy approach centers on a shared instance with row-level security via user attributes and access filters. This is the pattern documented in Looker's official RLS and multi-tenant guidance.

How It Works:

1. Multi-Tenant Database: Data warehouse contains `tenant_id`, `customer_id`, or similar column identifying tenant ownership

2. User Attributes: Looker stores attributes on user records (e.g., `tenant = "ABC"`, `region = "North America"`)

3. Access Filters: LookML defines filters on Explores that automatically apply based on user's attributes

4. Groups for Organization: Users grouped for easier attribute assignment and permission management

From Looker Documentation:

"Access filters let you define row-level security rules at the Explore level. These filters automatically apply based on the user's attributes, limiting data visibility per user or group."

RLS Implementation

// In LookML model:
explore: orders {
  access_filter: {
    field: orders.tenant_id
    user_attribute: tenant
  }
}

// User attributes configured per user or group:
// User "alice@customer_abc.com" has user_attribute "tenant" = "ABC"
// Group "Customer_ABC" has user_attribute "tenant" = "ABC"

// Backend signing generates embed URL with embedded user attributes:
{
  "target_url": "https://looker.example.com/embed/dashboards/17",
  "external_user_id": "customer_abc_user_1",
  "user_attributes": {"tenant": "ABC"}
}

Strengths:

• Cost-efficient: Single instance, single set of models shared across all tenants
• Content reuse: All tenants see same Liveboards; RLS filters their data
• Reasonable for moderate scale: 50-200 customers manageable with careful planning
• Search-driven UI: All tenants get search-driven exploration with recommendations

Challenges:

• User attribute management complexity: Must maintain user-to-group-to-tenant mappings
• Access filter configuration: Each Explore requires separate access filter setup
• RLS testing burden: Complex rules require extensive testing across many user/group combinations
• Group and user provisioning: Manual or API-driven provisioning required for each tenant
• Hierarchical group limitations: Searce documentation notes that hierarchical groups in multi-tenant scenarios increase complexity and auditing overhead

Scale Considerations:

• 500 customers: ~1,000+ groups typical; user attribute management becomes complex
• Performance: Teams commonly report careful design needed to avoid performance issues at high tenant/user counts
• Audit complexity: Tracking who can see what requires analyzing user attributes + access filters + group memberships

DataBrain: Programmatic Tenant Isolation

DataBrain eliminates per-tenant workspace management through programmatic multi-tenancy, with tenant context flowing through secure JWT tokens.

How it works:

1. Guest Token Generation: Backend generates signed JWT with tenant/user context
2. Automatic SQL Injection: DataBrain injects filters into queries based on token payload
3. Hierarchical Context: Token supports org → team → user filtering
4. Zero Configuration: No per-tenant RLS setup in admin UI

Typical 3-day implementation:

• Day 1: Connect data source, define metrics in DataBrain UI
• Day 2: Implement Guest Token generation in backend (few API calls)
• Day 3: Embed SDK component in React app, generate tokens, test isolation

Implementation example:

// Backend: Generate tenant-specific token
const guestToken = await dataBrain.createGuestToken({
  clientId: 'customer_123',
  userId: 'user_456',
  filters: {
    organizationId: 'org_789',
    teamId: 'team_101'
  },
  expiresIn: '1h'
});

// Frontend: Embed dashboard
<dbn-dashboard
  token={guestToken}
  dashboard-id="sales-overview"
/>

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‍What happens at query layer:

-- DataBrain automatically transforms query

-- Original:
-- SELECT * FROM sales;

-- Transformed to:
WITH tenant_filter AS (
  SELECT * FROM sales
  WHERE tenant_id = 'customer_123'
    AND organization_id = 'org_789'
    AND team_id = 'team_101'
)
SELECT * FROM tenant_filter;

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Automatic SQL Transformation at Runtime (Pseudo-code):

-- Original dashboard query (written once, for all tenants):
SELECT region, SUM(revenue) as total_revenue
FROM sales_data
GROUP BY region

-- DataBrain automatically injects tenant filters at execution:
WITH tenant_context AS (
  SELECT * FROM sales_data 
  WHERE client_id = 'customer_xyz_123'
    AND organization_id = 'org_789'
    AND department_id = 'dept_101'
)
SELECT region, SUM(revenue) as total_revenue
FROM tenant_context
GROUP BY region

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Frontend Embedding ( React Example ) :

import { DataBrainDashboard } from '@databrain/react';

export function AnalyticsPage() {
  return (
    <DataBrainDashboard
      dashboardId="sales-overview"
      token={guestToken}
      theme={{
        primaryColor: "#0D6EFD",
        fontFamily: "Inter, system-ui",
        borderRadius: "8px"
      }}
      filters={{
        dateRange: 'last_30_days',
        region: currentUser.region
      }}
      onFilterChange={(newFilters) => {
        // Hook into app state if needed
      }}
    />
  );
}

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Strengths:

• Zero per-tenant configuration; onboarding = token generation
• Same architecture for 10 or 10,000 tenants
• Tenant context enforced at query layer (cannot be misconfigured)
• Security logic centralized in backend
• Flat pricing regardless of tenant count

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DataBrain is designed to support large multi-tenant SaaS deployments with consistent performance and automated onboarding via guest tokens.

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Embedding Complexity: Signed URLs vs Web Components

Embedding architecture determines development effort, customization, and production readiness timeline.

Looker: Embed SDK + Signed URLs

Looker's embedding is iframe-based, with two primary approaches:

1. Signed Embedding: Secure, private URLs signed with an embed secret
2. Embed SDK: Higher-level integration for richer interactions

Implementation Steps:

1. Initialize SDK (Browser) 

import { LookerEmbedSDK } from '@looker/embed-sdk'

LookerEmbedSDK.init('your.looker.host')

2. Build Signed URL (Backend , Custom Implementation Required)
   • Backend Must Compute HMAC-SHA256 signature:

// Backend signing endpoint (pseudocode example)
const createSignedUrl = (targetUrl, user, host, secret) => {
  const params = {
    external_user_id: user.id,
    user_attributes: { tenant: user.tenant },
    force_logout_login: false
  }
  // Must HMAC-sign with embed secret
  // Exact signature computation shown in Looker SDK docs
}

3. Embed Dashboard

LookerEmbedSDK.createDashboardWithId('11')
  .appendTo('#embed_container')
  .withFilters([
    { name: 'Region', value: 'North America' }
  ])
  .build()
  .connect()

Embedding Considerations:

• Backend signing required: Custom infrastructure to sign URLs with embed secret
• Embed secret management: Must securely manage and rotate embed secrets
• URL encoding: Filter values must be properly encoded in signed URLs
• Session management: Each signed URL creates new session
• Iframe-based architecture: Theming and custom branding available, but less DOM-level control than component-based approaches

Implementation Experience:

For complex multi-tenant SaaS deployments with RLS and custom UX, teams often report multi-week implementations. Simple internal or single-tenant embeds can be faster. Google has published case studies of rapid implementations, but these typically focus on narrower scenarios (single tenant, standard Liveboards).

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DataBrain: SDK-Native Embedding

1. Install SDK: npm install @databrain/react
2. Generate Guest Token ( Simple Backend Call ) 

// One API call to DataBrain backend
const response = await fetch('https://api.usedatabrain.com/api/v2/guest-token/create', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_TOKEN',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    clientId: 'customer_123',
    dataAppName: 'myapp'
  })
});

const { token } = await response.json();

3. Embed In Frontend ( Web Component )

import '@databrainhq/plugin/web';

export function AnalyticsPage() {
  const [token, setToken] = useState('');

  useEffect(() => {
    getGuestToken('customer_xyz').then(setToken);
  }, []);

  return (
    <dbn-dashboard
      token={token}
      dashboard-id="sales-overview"
    />
  );
}

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Architectural Advantages:

• Native integration: Web component model allows seamless React/Vue integration
• No custom signing required: Token generation is simple API call
• Web component integration: Seamless React/Vue integration
• Automatic token refresh: SDK handles expiration
• CORS simplified: Domain whitelisting configured in DataBrain UI
• DOM-level customization: Full control over styling and layout

Implementation Experience:

For SaaS teams with working app and data warehouse, typical implementations using the guest token flow and pre-built components complete in several days, based on case studies and vendor positioning.

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Embedding Comparison

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Data Modeling & Governance: Semantic Layer

Both platforms include semantic layer capabilities, but with different maturity and focus.

Looker: LookML Semantic Layer

Approach:

• LookML is Looker's modeling language
• Defines dimensions, measures, filters, and access controls
• Central source of truth for metrics, calculations, and business logic
• All analytics built on LookML; enforces consistency

Advantages:

1. Metric governance: Metrics defined once; used everywhere consistently
2. Reusability: Dimensions and measures used across multiple Explores
3. Git-backed: Version control and audit trail for all changes
4. Mature ecosystem: Extensive third-party training, tools, and community resources

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DataBrain: Semantic Layer for Embedded Analytics

Approach:

• Business-friendly fields, synonyms, and semantic types
• Reusable metrics and governed calculations
• Focus on metrics for embedded and AI use cases

DataBrain Semantic Layer Features:

• Semantic types and field grouping
• Reusable metric definitions
• Business-friendly naming and synonyms
• Integration with AI Data Studio and Chat Mode

Positioning:

Looker's semantic layer is more mature and deeply integrated into enterprise BI workflows. DataBrain's semantic layer focuses on reusable metrics and business-friendly fields for embedded and AI use cases, but has a smaller ecosystem and less third-party training material.

Implementation Complexity: DataBrain vs Looker Embedded

Getting embedded analytics into production is not only about features. It is about how long it takes, how many engineers you need, and how much custom plumbing you must maintain.

Looker is built as an enterprise BI and semantic layer platform. That gives you strong governance and modeling, but it usually comes with multi week projects for modeling, RLS design, SSO embedding, and environment setup. Google Cloud case studies show full Looker BI rollouts in the 2–8 week range, even when individual visualization work is only a small part of a larger project, and consulting partners position “up and running” timelines in the multi week window.  

DataBrain is optimized for SaaS embedding. Multi tenancy is handled programmatically through guest tokens, the SDK is designed for direct React or Vue integration, and you can follow a five minute tutorial to get a working embed. Public comparison pages and docs position typical implementation from first dashboard to production in 2–5 days, with multiple assets reinforcing a “launch dashboards in days” narrative.  

For a SaaS team, that difference in implementation speed is often the practical gap between shipping analytics this sprint versus adding another multi week project to the backlog.

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Pricing & Cost of Ownership

DataBrain: Transparent Flat-Rate Pricing

Key Characteristics:

• No per-user fees
• No per-viewer fees
• Unlimited end-user access at any tier
• No usage charges (queries unlimited)
• Free trial available

Example 3-Year Cost (500 SaaS customers):

Year 1-3: $1,995/month × 12 = $23,940/year per tier
3-Year Total: $71,820

Cost per customer: $239/year (or $20/customer/month)

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Looker Embed Pricing Model

Google does not publish specific dollar amounts for Looker (Google Cloud core) or Looker Embed on its official pricing page. The embed platform is listed as “Call sales” and user licensing is described only at the level of roles, not prices. All concrete numbers are quote based and must be obtained from Google Cloud sales or resellers. The structure below is based on Google documentation and independent third party analyses, not official list prices. Actual pricing can vary significantly by contract.

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Pricing structure

• Platform licensing
  • Annual subscription for a Looker (Google Cloud core) instance, purchased as Standard, Enterprise or Embed edition.
  • The embed platform SKU on the official pricing page is shown without any public price, with a note to work with sales to design a solution.
  • A platform subscription typically includes a small bundle of users by default, for example a limited number of Standard and Developer users tied to the instance.
• Per user licensing
  • Additional access is licensed per user, with different roles such as Viewer, Standard and Developer.
  • Each role has its own entitlements for content creation, administration and development, and carries a different price point.
  • Per user prices are not listed publicly and are negotiated as part of the overall contract.
• Contract term and editions
  • Subscriptions are usually sold on one, two or three year terms, aligned with other Google Cloud enterprise contracts.
  • Embedded analytics use cases typically require an Embed or Enterprise edition, which carries higher pricing than internal only Standard deployments.
    

Pricing characteristics

• Non transparent, sales only pricing
  • There is no self serve pricing for Looker Embed.
  • The official pricing page shows the embed platform with no dollar values and a “Call sales” message.
  • There is no public price list for Viewer, Standard or Developer roles, and no calculator for embedded use cases. Any concrete numbers come from a sales proposal, not from published pricing.
• Enterprise, quote based cost profile
  • Total cost is driven by several factors working together:
    • Chosen edition (Standard, Enterprise, Embed)
    • Number of instances and environments
    • Number of users by role (Viewers vs Standard users vs Developers)
    • Query and API volume and concurrency requirements
    • Underlying warehouse spend (for example BigQuery) and professional services
  • Independent analyses of recent contracts commonly place starting Looker deployments in the mid five figure per year range, with Enterprise and Embed scenarios frequently reaching six figures annually for moderate to large user bases, before warehouse and services costs.
• Per user model that scales with audience size
  • Several third party breakdowns highlight a per viewer pricing pattern, with viewer licences priced at roughly a few hundred dollars per viewer per year in example quotes.
  • As viewer counts grow into the hundreds or thousands for embedded use cases, this per user model can become the dominant driver of cost.
  • Because pricing is negotiated, two companies with similar user counts and usage patterns can end up paying different amounts depending on timing, discounts and broader Google Cloud commitments.
    

What this means for SaaS teams

For SaaS teams, Looker Embedded behaves like a classic enterprise BI contract:

• You must talk to sales to get any real pricing, even for a proof of concept.
• The contract usually starts in the tens of thousands of dollars per year, and can grow into six figures per year or more as you add external viewers and environments.
• The per user, per role licensing model and lack of public prices make total cost of ownership harder to predict compared to flat rate, published tier pricing.

The upside is access to a mature semantic layer, governance model and Google Cloud integration. The trade off is a metered, seat based cost structure that grows with adoption rather than staying flat, combined with non transparent, sales driven pricing that requires careful negotiation and long term planning.

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Row-Level Security ( RLS ) Implementation: SQL-Level vs Access Filters

RLS implementation determines operational burden and how scaling impacts system design.

Looker: User Attributes + Access Filters

How It Works:

• RLS configured via LookML access_filter parameter on Explores
• Access filters evaluate user's user_attribute values at query time
• User attributes stored in Looker, assigned per user or per group

Implementation Steps (From Lookers Documentation) :

1. Create User Attribute in Looker Admin
   • Name: `tenant`
   • Type: String Filter (advanced)
2. Configure access_filter In LookML

explore: orders {
  access_filter: {
    field: orders.tenant_id
    user_attribute: tenant
  }
}

3. Assign user attribute values to groups or users
   • Group "Customer_ABC": tenant = "ABC"
   • Group "Customer_XYZ": tenant = "XYZ"
4. Test and maintain
   • Test filter application per user/group combination
   • Monitor and adjust as tenant structure grows

Scale Challenges:

• User attribute management: 500 customers × multiple groups = substantial group and attribute management overhead
• Access filter configuration: Each Explore requires separate filter definition and testing
• RLS complexity: Multi-level filters (tenant + region + department) multiply configuration surface area
• Audit complexity: Understanding who can see what requires analyzing user attributes + access filters + group memberships

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Databrain: SQL-Level RLS (Automatic)

How It Works:

RLS is automatic, SQL-level filtering enforced at query execution. You define multi-tenant dataset rules once; every query is filtered based on token context. No role definitions needed, no DAX expressions, no manual configuration per tenant.

1. Define Multi-Tenant Dataset (Once in Databrain Studio) 
   • Specify Tenant identity columns ( e.g.,client_id, organization_id)
   • DataBrain automatically applies these as WHERE clauses to all queries
2. Generate Guest Token ( Backend handles all tenant context)

const token = await createGuestToken({
  clientId: 'customer_xyz_123',
  dataAppName: 'myapp'
  // Token includes tenant context; DataBrain auto-filters based on it
});

3. Databrain Auto-Applies Filters (No admin work needed) 

-- DataBrain automatically injects WHERE clauses based on token context
-- Admin/developer doesn't write RLS rules; filters are automatic

-- For ANY query against ANY table, filters are applied:
WHERE client_id = 'customer_xyz_123'

4. No Role management
   • No role Creation
   • No DAX Expressions
   • No User to Role mapping
   • All governance handled via token context

RLS Comparison

Governance & Compliance :

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Conclusion: Which Is Right For Your SaaS Product?

When choosing between DataBrain and Looker Embedded for your embedded analytics strategy, the right fit comes down to your team's technical bandwidth, customization needs, and how quickly you want to bring analytics to market.

Choose DataBrain if you need:

• You're building a SaaS product with multiple customers (multi-tenancy is core requirement)
• You need automatic tenant isolation without per-tenant RLS configuration
• Time-to-market is critical (days vs weeks)
• You want predictable, flat-rate costs (not per-user scaling)
• Your team has limited data engineering resources
• You need governance-first multi-tenancy (compliance, data isolation, audit trails)
• Customer-facing, embedded analytics is a core product feature
• You want to minimize operational overhead (managed infrastructure, no capacity planning)
• You need unlimited end-user access without per-seat licensing
  

Choose Looker Embedded if you prefer:

• You're an enterprise with shared analytics infrastructure across organization
• You need a central semantic layer to govern metrics and ensure consistency
• Your organization values search-driven data exploration (vs dashboards)
• You have strong data engineering and BI teams for modeling and governance
• You can allocate multiple weeks for implementation and RLS planning
• You're deeply invested in Google Cloud (BigQuery, GCP ecosystem)
• You need generative AI for conversational analytics (Gemini-powered)
• You're comfortable with custom pricing negotiation and per-user licensing
• You have regulatory requirement needing enterprise-grade audit and governance

For most SaaS companies building embedded analytics into their products, DataBrain offers a faster, more scalable path to production with far less engineering effort. Its focus on modern SaaS patterns—multi-tenancy, embedded AI, and flexible SDKs—makes it the smarter choice for delivering value to end users without the complexity of managing infrastructure.

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Ready to see the difference? Book a personalized demo today to discover how DataBrain can help you launch powerful embedded analytics—faster and smarter.

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Related Comparisons & Resources

Explore more alternatives to Looker Embedded and how DataBrain compares with other platforms:

• The 10 Best Looker Embedded Analytics Alternatives and Competitors for 2025" for a broad overview of the embedded analytics market.

Compare DataBrain directly against leading solutions:

• DataBrain vs GoodData: Comparison with GoodData's enterprise analytics capabilities.
• DataBrain vs Sisense: Evaluation of Sisense’s embedded BI platform.
• DataBrain vs Power BI Embedded: See how DataBrain stacks up to Microsoft's Power BI Embedded.
• DataBrain vs ThoughtSpot: Detailed comparison with ThoughtSpot’s AI-powered analytics.