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

AI-heavy analytics is now table stakes in SaaS, but not every platform is built for the realities of multi-tenant products, strict security requirements, and limited engineering bandwidth. ThoughtSpot sits in the enterprise BI camp: a powerful, AI-driven analytics platform with natural language querying, search-centric exploration, and governance-first features for organizations that care deeply about controlled, compliant data access. For a broader view of Thoughtspot Embededed alternatives in the embedded analytics market, see our comprehensive guide

DataBrain takes a different path, built specifically for SaaS teams that want analytics to behave like a native part of the product. Multi-tenancy is handled programmatically via Guest Tokens, RLS is pushed down to automatic SQL-level filters, and embedding is reduced to web components and React integrations instead of heavyweight BI constructs. Infrastructure scaling is automated, and pricing is flat and predictable ($999–$1,995/month), which keeps both implementation timelines and governance overhead manageable for multi-tenant environments.

The rest of this comparison walks through multi-tenancy architecture, row-level security patterns, embedding complexity, governance and compliance, operational overhead, and total cost of ownership, so you can map ThoughtSpot and DataBrain against your current stack and where your product needs to go next.

DataBrain vs. Thoughspot: TL;DR Comparison

That strength comes with weight. Embedded deployments with multi-tenant RLS typically require weeks of upfront modeling and planning. Teams have to choose and maintain a multi-tenancy pattern (Org-based isolation vs shared database with RLS), wire up Liveboard embedding with authentication and runtime filters, and work within the constraints of iframe-based, app-level embeds rather than a native component SDK. Pricing is opaque and negotiated, and usually lands in per-user contracts that expand with adoption.

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
• Built-in governance: automatic tenant isolation, audit logs, no manual role mapping
• SOC 2 compliant and supports HIPAA use cases with BAA agreements
  

Cons:

• Relatively newer platform; smaller enterprise case study library compared to Thoughtspot
• Community ecosystem smaller than Thoughtspot's
• Limited data discovery compared to advanced search and natural language capabilities
• No conversational natural language search interface (queries use SDK-based filters and parameters)
• Fewer third-party integrations than established enterprise platforms

Thoughspot Embedded Pros and Cons

Thoughspot 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:

• AI-driven natural language analytics: SpotIQ engine and Spotter AI enable conversational queries without SQL knowledge
• Search-first architecture: Intuitive data exploration with auto-suggestions, drill-down, and contextual recommendations
• Advanced visualization and discovery: Rich visualization library, pivot tables, and complex analytical workflows
• Enterprise-grade governance: Fine-grained RLS rules, column-level security, hierarchical groups, comprehensive audit trails
• Multiple embedding options: Liveboard embed, search embed, NLQ embed, full app embed via Visual Embed SDK
• Runtime filter flexibility: Apply filters at query time without redeploying content
• Trusted authentication: Native support for SAML, OIDC, and trusted auth with auto-login
• Multi-tenancy support: Orgs feature for complete tenant isolation or data-level RLS for shared models
• Programmatic APIs: REST APIs available for managing Orgs, groups, users, and content at scale
• Trusted by thousands of enterprises worldwide
  

Cons:

• High implementation complexity: Most real-world embedded deployments take multiple weeks (commonly 8–14 weeks) per third-party analyses and customer reports
• Substantial licensing costs: Industry analyses suggest typical contracts for embedded and enterprise use start in the low five figures and often land in the $25,000–$60,000+/year range, with larger deals exceeding $100,000/year
• Requires skilled teams: Data engineers, BI specialists, DevOps for architecture planning
• Multi-tenancy choice required upfront: Three different models require careful design decisions before implementation
• Operational overhead: ElastiCube maintenance, schema management, custom configurations
• Originally built for enterprise internal BI: Embedded use cases supported but often carry complexity of internal-first approach
• Longer time-to-market: Weeks-to-months needed for embedding and customization after initial setup

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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, 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, ThoughtSpot can provide a powerful, governed analytics backbone, with the trade-off of longer timelines, higher licensing costs, and greater operational overhead for 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 Orchestrated

Multi-tenancy architecture drives operational cost, implementation timeline, and scalability. This is where the two platforms diverge most significantly.

Thoughtspot: Two Multi-Tenant Patterns (Choose Upfront)

Thoughtspot offers two distinct multi-tenancy approaches, each requiring careful architectural decisions before implementation. Both are supported via REST APIs for programmatic deployment, but each requires explicit modeling of tenant structure.

Pattern 1: Org-Based Multi-Tenancy (Logical Isolation)

How It Works:

• Thoughtspot instance logically partitioned into multiple Orgs
• Each Org is a complete tenant environment with its own data connections, metadata, groups, and content
• Orgs are completely isolated; tenants cannot see or access other Org content
• Each Org has separate admin controls, authentication rules, and data access policies
• Suitable when each customer has completely separate data and workflow.

Strengths:

• Maximum isolation: Each Org logically isolated at application layer
• Simplified governance: No need for complex cross-Org RLS rules
• Per-tenant customization: Each Org can have its own authentication, SSO, trusted auth
• Clear audit boundaries: Audit logs scoped per Org

Challenges:

• Tenant provisioning workflow: Creating and configuring new Orgs requires admin workflow (can be automated via REST APIs, but still requires explicit Org and data connection setup)
• Content management: Liveboards and searches must be created per Org or centrally managed
• Scaling overhead: Adding 500 customers = 500 Orgs to provision and configure
• RLS rules still required: Even with Org isolation, RLS within each Org filters data by user or department
• Infrastructure cost: Each Org consumes licensing capacity; costs scale with number of Orgs

Pattern 2: Data-Level Multi-Tenancy (Shared Model + RLS)

How It Works:

• Single Thoughtspot instance with shared data connections and Models
• Multi-tenant database with `tenant_id` or `customer_id` column in all tables
• Row-level security (RLS) enforces tenant data isolation at query time
• All customers share same Liveboards and searches; RLS filters results per user/tenant
• Uses groups and RLS rules to map user group membership to allowed rows

From Thoughtspot documentation:

• "Multi-tenancy within an Org lets you add row level security to create tenant environments within a single Org. This approach includes a shared semantic layer and data model, with tenant data filtered by RLS rules based on groups."
• "We recommend not to use hierarchical groups in a multi tenanted situation. When groups are hierarchical, the rules for how privileges and row level security are derived become complex, reducing performance and introducing complexity in auditing."

Strengths:

• Cost-efficient: Single database, single set of Models, shared Liveboards for all tenants
• Reasonable for moderate scale (50-200 customers with careful planning)
• No duplicate content: All customers see same Liveboards; RLS filters their data

Challenges:

• RLS rule complexity grows: Each new tenant may require new group creation and/or RLS rule updates
• Performance risk at scale: With 500+ groups and complex RLS expressions, query performance can degrade
• Manual user-to-group mapping: Users must be manually assigned to groups; no automatic tenant context
• Hierarchical group complexity: Thoughtspot documentation explicitly warns against hierarchical groups due to performance and auditing complexity
• Testing burden: Complex RLS rules require extensive testing; edge cases possible
• Audit complexity: Tracking who can see what data requires analysis of RLS rules and group membership

Thoughtspot Multi-Tenancy Comparision

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

‍

‍

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

‍

Embedding Complexity: Visual Embed SDK vs Web Components

Embedding architecture determines development effort, customization depth, and integration with your React/Vue application.

Thoughtspot: Visual Embed SDK + Iframe-Based Architecture

Embedding Steps:

‍

1. Initialize SDK and Authentication

import {
  init,
  AuthType,
  LiveboardEmbed
} from '@thoughtspot/visual-embed-sdk';

// Initialize with authentication
init({
  thoughtSpotHost: 'https://your-thoughtspot-instance.cloud.thoughtspot.com',
  authType: AuthType.OIDC, // or SAML, Trusted Auth
});

‍

2. Embed Liveboard with Runtime Filters

// Visual Embed SDK example (simplified for illustration)
const liveboardEmbed = new LiveboardEmbed(
  document.getElementById('ts-embed'),
  {
    liveboardId: 'LIVEBOARD_GUID',
    // Runtime filters applied at query time
    runtimeFilters: [
      {
        columnName: 'Region',
        operator: 'EQ',
        values: ['North America']
      }
    ],
    // Control UI elements
    hiddenActions: ['DOWNLOAD', 'SHARE'],
    visibleActions: ['REFRESH', 'EXPLORE']
  }
);

liveboardEmbed.render();

‍

1. Handle Authentication and Events (Custom Logic)

// Event handling for custom behavior
liveboardEmbed.on(EmbedEvent.EmbedHeight, (data) => {
  // Adjust container height
});

liveboardEmbed.on(EmbedEvent.CustomAction, (payload) => {
  // Route custom actions back to your app
});

‍

Architectural Trade-offs:

• Strengths: Flexible (extensive customization options); supports multiple embedding patterns (Liveboard, search, NLQ, full app)
• Constrains: Built around Liveboard and app iframe embedding, limiting DOM-level control compared to native component SDKs. Embedded experiences typically retain Thoughtspot UI chrome and branding elements even when many elements are hidden, due to iframe-based architecture.
• Integration: Requires custom authentication setup (OIDC, SAML, or Trusted Auth); event handling for interactive features
• Implementation: Complex initialization and configuration compared to simpler token-based approaches

‍

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"
    />
  );
}

‍

Architectural Advantages:

• Native integration: Web component model allows seamless React/Vue integration
• DOM-level customization: Full control over styling and layout
• Simple token model: Authentication and multi-tenancy handled by token, not SDK initialization
• Minimal boilerplate: No complex event handling or custom auth flows needed

Embedding Comparison

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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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ThoughtSpot embedded pricing - what is publicly known

Multiple independent reviews and vendor comparisons describe ThoughtSpot’s pricing in three layers:

• Analytics plans (internal BI)
  • Essentials: starting around 25 USD per user per month, typically for 5 to 50 users and up to 25M rows.  
  • Pro: usage based pricing starting around 0.10 USD per query, aimed at 25 to 1000 users and larger data volumes.  
  • Enterprise: custom quotes for unlimited users and data.  
• Embedded analytics plans
  • Developer edition: free for small teams to build prototypes.
  • Pro and Enterprise embedded: flexible, case by case pricing, negotiated with sales.  
• Typical contract sizesSeveral sources report that:
  • Entry pricing for meaningful deployments often starts in the low five figures per year.  
  • Average annual contracts for larger deployments hover around 137k USD, with some enterprise implementations in the 300k to 500k USD per year range.  
    

Because pricing is a mix of per user, per query, and custom enterprise terms, total cost is highly sensitive to how many end users you expose analytics to and how often they query.

‍

Directional TCO scenario - embedded ThoughtSpot in a SaaS product

To illustrate how this can add up for a multi tenant SaaS product, here is a hypothetical 3 year scenario based on the public ranges above and common enterprise BI cost patterns. This is a model, not a quote.

Assumptions:

• 500 SaaS customers sharing a data level RLS pattern.
• ThoughtSpot analytics plus embedded SKU.
• Enterprise contract in the low six figure range, aligned with public contract size estimates.
• 2 FTEs worth of internal time for administration, modeling, and DevOps.

Year 1 (illustrative):

 - Core license and infrastructure: 60k - 100k USD

 - Per user / consumption based usage: 20k - 60k USD

 - Implementation services and consulting: 80k - 150k USD

 - Admin / DBA and DevOps time (2 FTEs): ~200k USD

 - Year 1 total (modeled): ~360k - 510k USD

Years 2-3 (illustrative):

 - License renewal and usage growth: 80k - 140k USD per year

 - Admin / DBA maintenance: ~200k USD per year

 - Year 2-3 total (modeled): ~280k - 340k USD per year

Hypothetical 3 year total (modeled): ~920k - 1.2M USD

Illustrative cost per customer per year: ~600 - 800 USD

Important caveats:

• These numbers are directional only, built from public commentary and generic assumptions about FTE costs, not from ThoughtSpot pricing sheets.
• Actual ThoughtSpot commercial terms vary a lot by negotiation, feature set, region, and how heavily customers use the system.
• For current and precise pricing, you must speak directly with ThoughtSpot.

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Governance & Compliance :

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Conclusion: Which Is Right For Your SaaS Product?

When choosing between DataBrain and Thoughtspot 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 Thoughspot Embedded if you prefer:

• You're an enterprise or large organization with sophisticated data discovery needs
• Your users benefit from AI-powered natural language queries (vs pre-built dashboards)
• You need advanced analytical workflows (pivot tables, ad hoc exploration, drill-down)
• You have strong data engineering and BI teams for architecture design and governance
• You can allocate multiple weeks for implementation and multi-tenancy planning
• You need enterprise-grade audit and compliance (fine-grained governance)
• You're comfortable with per-user licensing and custom pricing negotiations
• Your organization values search-first analytics over pre-built dashboard navigation
• You have regulatory requirements (financial, healthcare) requiring detailed RLS and compliance

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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.

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

Related Comparisons & Resources

Explore alternatives:

• Top 7 ThoughtSpot Embedded Alternatives in 2025: A comprehensive overview of other platforms in the embedded analytics space.

Compare DataBrain head-to-head with other solutions:

• DataBrain vs GoodData: See how DataBrain compares to GoodData’s enterprise analytics platform.
• DataBrain vs Sisense: In-depth comparison with Sisense’s enterprise analytics capabilities.
• DataBrain vs Power BI Embedded: Evaluation of DataBrain versus Microsoft’s embedded analytics solution.
• DataBrain vs Looker: Detailed comparison between DataBrain and Google’s Looker Embedded Analytics.