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

Determining the right embedded analytics platform for your SaaS product means balancing performance, security, multi-tenancy architecture, and operational complexity. Power BI Embedded is a mature, enterprise-focused analytics solution built on Microsoft Azure, with strong data modeling, deep integration across the Microsoft ecosystem, and powerful business logic through DAX. It is designed for larger organizations that want comprehensive BI capabilities, heavy customization, and tight alignment with Azure, Office 365, and Teams. For a broader view of Power BI Embedded alternatives in the embedded analytics market, see our comprehensive guide:

DataBrain is built specifically for modern SaaS teams that need to ship embedded analytics quickly without weakening security, isolation, or compliance. It provides fully managed, programmatic multi-tenancy through Guest Tokens, automatic SQL-level Row-Level Security, a streamlined React/Vue SDK for embedding, and automated infrastructure scaling. In practice, this allows most teams to reach production in days instead of weeks, with predictable flat-rate pricing ($999–$1,995 per month, no per-viewer charges) and far lower ongoing engineering overhead to manage multi-tenant analytics at scale.

This comparison takes an engineering-focused look at both platforms, covering multi-tenancy design, RLS implementation, governance controls, tenant isolation models, embedding complexity, operational burden, and total cost of ownership, so you can decide which approach aligns better with your product strategy, technical stack, and governance requirements.

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DataBrain vs. PowerBI: TL;DR Comparison

However, Power BI Embedded's enterprise heritage comes with significant trade-offs. According to customer reports and industry analyses, implementation for multi-tenant scenarios typically takes 8-14+ weeks, requires substantial data engineering and DevOps expertise for RLS configuration and governance, and involves capacity-based hourly billing. Entry-level capacity (smallest A-SKU) is billed hourly; if run continuously 24×7, estimates suggest mid-hundreds of dollars per month—but actual cost depends on active usage, and capacities can be paused to reduce costs. Multi-tenancy requires careful architecture decisions between separate workspaces, shared datasets with complex RLS rules (using DAX), or multiple app ownerships—each introducing operational complexity and governance challenges.

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 manual configuration via Guest Tokens
• Automatic RLS at SQL query layer; no DAX role engineering required
• Native React/Vue SDK components; true white-labeling with DOM integration
• Setup in days for most SaaS teams (not weeks or months)
• AI features designed for non-technical end users
• Predictable flat-rate pricing ($999-$1,995/month as of November 2025); no per-unit capacity charges or per-viewer fees
• Minimal DevOps/DBA overhead; managed cloud option available
• Unlimited end-user viewers at any price tier
• Built-in governance: automatic tenant isolation, no manual role mapping
• SOC 2 and HIPAA compliance features available, with BAA for HIPAA workloads
  

Cons:

• Relatively newer platform; smaller enterprise case study library
• Community ecosystem smaller than Power BI's
• Limited to simpler data modeling vs Power BI's advanced DAX capabilities
• Dashboard authoring is centralized in DataBrain's studio; embedding happens via SDK into your own app (not drag-and-drop admin UI within your product)
• Fewer third-party integrations than Microsoft ecosystem

Power BI Pros and Cons

Power BI 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:

• Enterprise-scale platform with 15+ years of BI evolution .
• Advanced data modeling through DAX language; sophisticated calculations and logic
• Premium Capacity scales to handle very large models and high-volume workloads
• Deep Microsoft ecosystem integration: Azure, Office 365, Teams, Dynamics 365
• Visual report designer: Powerful drag-and-drop interface for building reports
• Rich visualization library: 30+ native visuals plus extensibility via custom visuals
• Advanced RLS via DAX roles with dynamic user context (USERNAME, USERPRINCIPALNAME)
• Object-Level Security (OLS) Hide tables/columns from specific roles
• Flexible capacity management: Capacity can be paused when idle to reduce or eliminate billing
• Trusted by thousands of organizations worldwide

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Cons:

• High implementation complexity: Multi-tenancy requires architectural decisions upfront (separate workspaces vs shared datasets + RLS); 8-14+ weeks typical per customer reports
• Hourly capacity billing: Entry-level A-SKU capacity billed hourly on Azure. If run 24×7, translates to mid-hundreds of dollars/month (≈$735.91/month estimated for A1 at 730 hours); actual cost depends on active usage and region. Capacity can be scaled down or paused when idle to reduce costs
• Requires skilled teams: Data engineers, BI specialists, DevOps for architecture planning and RLS maintenance
• Multi-tenancy design burden: No turnkey multi-tenant mode. Requires non-trivial architecture and ongoing maintenance; automation possible via scripting and REST/XMLA APIs but no out-of-the-box SaaS-multi-tenant mode
• RLS governance overhead: DAX-based RLS requires data modelers; at high tenant counts, large numbers of RLS roles and complex DAX filters can introduce significant operational overhead and may impact performance if models and roles are not carefully designed
• Multi-tenancy requires careful planning: Three common patterns (separate workspaces, shared dataset + RLS, hybrid apps) each require manual configuration and testing
• Embedding complexity: Custom authentication, token generation, CORS/iframe policies must be hand-built
• DevOps/DBA skills required: XMLA endpoint management, gateway configuration, capacity monitoring, refresh pipeline management
• Branding constraints: Branding and theming support exists, but the experience is constrained by Power BI's iframe model. You do not get full DOM-level control or a completely invisible Microsoft footprint the way you do with a native SDK
• Governance overhead: Even though the visual report builder is powerful, turning those reports into a governed, multi-tenant embedded experience still requires careful workspace, RLS, and deployment pipeline design.

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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, 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 workspace lifecycle management, Power BI can provide a powerful, governed analytics backbone, with the trade-off of longer timelines and higher operational overhead.

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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Multi-Tenancy Implementation: Programmatic Automation Vs Manual Orchestration

Power BI Embedded: Design Your Own Multi-Tenant Architecture

Power BI Embedded does not offer a single, easy, out-of-the-box solution for multi-tenancy. Instead, Microsoft requires you to design your own multi-tenant setup using elements such as workspaces, datasets, Row-Level Security (RLS), and features like service principal profiles. Below are the main approaches typically recommended by Microsoft and the Power BI community:

Pattern 1: Separate Workspaces Per Tenant (Full Isolation)

How this works:

• Each tenant is assigned their own dedicated Power BI workspace within a Premium Capacity.
• Each tenant has their own datasets, reports, and dashboards.
• This setup achieves complete isolation at the workspace level.
• Each workspace allows for separate refresh schedules, security configurations, and admin settings per tenant

Strengths:

• This pattern provides maximum isolation and security, addressing regulatory compliance and data separation needs.
• It allows for performance tuning specific to each tenant.
• Access control is managed per tenant, with no complex RLS rules to build.
• Generates clear audit trails for each tenant.

Challenges:

• Managing multiple workspaces within one capacity means resources are shared, leading to management overhead.
• Resource use may be inefficient, as many workspaces might be underutilized.
• Even with isolated workspaces, governance and RLS complexity do not fully disappear.
• Sharing templates or dashboards across tenants is difficult.
• Provisioning new tenants is done manually and can take hours per tenant.

Pattern 2: Shared Dataset with RLS Roles (Cost-Optimized, More Complex)

How this works:

• A single Premium Capacity is shared by all tenants.
• There's one shared data model (dataset) used by multiple Power BI apps (one for each tenant).
• Row-Level Security (RLS) is enforced through DAX roles to ensure tenants only see their own data.
• Each tenant only sees rows filtered according to their tenant context via DAX.
• Users are assigned to specific roles, let those roles enforce filter logic based on username or principal name.

Example RLS logic:

• To filter data by tenant, a typical DAX rule would look up the user's email and match it with a tenant identifier. For more granular access, you might also filter by region, matching the user's region via a separate mapping table.

‍Strengths:

• Cost-efficient: a single capacity is shared among all tenants.
• Works reasonably well for a small to medium number of tenants (about 5-50 customers).

Challenges:

• RLS governance becomes difficult to scale:
• You need at least one role per tenant, so 500 tenants means at least 500 roles to define, test, and audit.
• DAX filter complexity increases, with complex models sometimes requiring 20 to 50 filters per role.
• High numbers of roles and complex DAX code can lead to significant operational overhead and even performance issues if models and roles are not managed carefully.
• Tenant-to-role mapping is often done manually in Excel or CSV files, which is error-prone.
• Adding a new tenant means creating and testing a new role and associated DAX rules, which takes hours each time.
• Multi-tenant provisioning is not automatic: you have to design all roles, DAX expressions, and user mappings yourself. While scripting and API automation is possible, there's no built-in self-serve or fully programmatic multi-tenant provisioning.
• Testing complex RLS rules is a burden and prone to edge cases.
• Auditing who accessed what data becomes tough across hundreds of RLS roles.
• Since all tenants share the same capacity, there’s risk that heavy usage from one tenant can affect performance for everyone — a "noisy neighbor" problem.

Pattern 3: Shared Workspace with Multiple Apps (Hybrid, Still Manual)

How this works:

• There is one workspace and one main dataset.
• Multiple Power BI apps are created, each set up with their own RLS settings reflecting different tenant contexts and ownership.
• Each tenant gets a unique embed token that points them to their own app.
• RLS is set up similarly to Pattern 2, but distributed over separate apps.

Challenges:

• Each app still needs manual RLS configuration.
• Managing app ownership and their lifecycles adds overhead.
• The complexity around RLS remains the same.
• Most governance tasks are still manual.

DataBrain: Programmatic Tenant Isolation

DataBrain offers one optimized, programmatic multi-tenancy model eliminating architectural choices and manual governance. You define secure multi-tenant datasets once; every request is filtered automatically using token context.

How it works:

1. Define Multi-Tenant Dataset
   • Create Dataset with Multi-tenant filtering rules
   • Specify which columns represent tenant identity ( e.g., 'Client_id', 'organization_id')
   • Databrain Applies these Filters Automatically to all queries for all tenants

2. Guest Token Generation (Backend, Your Application)

// Backend generates signed JWT token with tenant context
// Example pattern from DataBrain multi-tenancy docs
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_xyz_123',
    dataAppName: 'myanalytics'
  })
});

const { token } = await response.json();
// Token includes tenant context; DataBrain uses it to enforce RLS

3. Automatic SQL-Level Filtering (Databrain Query Engine)

-- 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'
)
SELECT region, SUM(revenue) as total_revenue
FROM tenant_context
GROUP BY region

-- Filters applied at query layer; consistent enforcement for every request

4. Frontend Embedding ( Your React/Vue App ) 

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

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

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

  return (
    <DataBrainDashboard
      dashboardId="sales-overview"
      token={token}
      theme={{
        colors: { primary: '#0D6EFD' },
        fontFamily: 'Inter, system-ui'
      }}
    />
  );
}

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Key Advantages

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Row-Level Security ( RLS ) Implementation: SQL-Level vs DAX-Level

RLS is critical for multi-tenant compliance and governance. The implementation approach determines operational burden, performance, and auditability.

Power BI Embedded: DAX-Based RLS

How it works:

RLS in Power BI is role-based and DAX-expression-based. Administrators define roles in Power BI Desktop/Service, then write DAX filter expressions that evaluate at query time.

Implementation Steps:

1. Create Security Table ( Mapping users to attributes) 

UserEmail | Tenant_ID | Region | Department
alice@customer123.com | CUST123 | North | Sales
bob@customer123.com | CUST123 | North | Engineering
charlie@customer456.com | CUST456 | South | Sales

2. Define DAX Filter Rules

// Role: "SalesManager_North"
[Region] = RELATED(UserMapping[Region]) &&
[Tenant_ID] = RELATED(UserMapping[Tenant_ID]) &&
USERPRINCIPALNAME() = UserMapping[Email]

3. Assign Users to Roles (Manual, Azure AD Integration Required)
   • Users mapped to Power BI roles via Azure AD groups or manual assignment
   • Automation possible via scripting and REST/XMLA APIs, but no built-in self-service provisioning
   • Each tenant change requires re-testing and potential role updates

4. Test (Manual, Error-Prone)
   • Each role change requires re-testing
   • Testing hundreds of roles is resource-intensi
   • Edge cases and filter-bypass vulnerabilities possible.

Challenges when scaling Power BI Embedded RLS:

• Role explosion: Managing 1,500+ roles if you have 500 customers with 3 roles each.
• DAX complexity: Advanced business logic (such as multi-level hierarchies or exceptions) requires deep expertise in DAX.
• Maintenance burden: Any schema change, like adding a new attribute, means updating DAX rules across all affected roles.
• Governance overhead: Auditing which users have access to which data at scale requires building custom tools.
• User management synchronization: Changes in Azure AD groups do not automatically update Power BI role assignments.
• Performance risk: With many tenants and complex rules, overall performance can degrade if RLS roles and DAX filters are not carefully engineered.

Example: Multi-Level RLS Complexity (3 tenant levels)

// Organization > Region > Department level tenancy
// Each role needs all three filters to work correctly
[Organization_ID] =
  LOOKUPVALUE(
    'UserMapping'[Org_ID],
    'UserMapping'[Email],
    USERPRINCIPALNAME()
  )
&&
[Region] =
  LOOKUPVALUE(
    'UserMapping'[Region],
    'UserMapping'[Email],
    USERPRINCIPALNAME()
  )
&&
[Department] =
  LOOKUPVALUE(
    'UserMapping'[Department],
    'UserMapping'[Email],
    USERPRINCIPALNAME()
  )

// Result: Complex to maintain, hard to test, prone to edge cases

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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

Advantages at Scale

Adding 500 tenants is as simple as generating 500 tokens—no need to create roles or modify DAX logic. RLS requires zero maintenance: there are no role updates and no DAX to debug. Automatic isolation is enforced because every tenant's data is filtered at the SQL layer, making it impossible to bypass these restrictions. Auditing is straightforward since every token generation is logged, allowing you to track data access automatically. Performance is reliable because SQL WHERE clauses are simple and fast. DataBrain's approach is also governance-native, making it easier to satisfy compliance frameworks such as SOC2 and HIPAA.

RLS Comparisions

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Embedding Experience: Built-in vs. Custom Development

The embedding experience determines development effort, customization depth, and time-to-production.

Power BI Embedded: Complex Custom Integration

Embedding Steps

1. Service principal Setup (Custom, Azure Ad Configuration)

// Manual Azure AD configuration for service principal
// Required for programmatic token generation

const axios = require('axios');

// Step 1: Get Azure AD token
const response = await axios.post(
  `https://login.microsoftonline.com/${tenantId}/oauth2/v2.0/token`,
  {
    grant_type: 'client_credentials',
    client_id: YOUR_SERVICE_PRINCIPAL_ID,
    client_secret: YOUR_SERVICE_PRINCIPAL_SECRET,
    scope: 'https://analysis.windows.net/powerbi/api/.default'
  }
);

const accessToken = response.data.access_token;

2. Embed Token Generation (Custom)

// Step 2: Generate Power BI embed token
const embedTokenResponse = await axios.post(
  `https://api.powerbi.com/v1.0/myorg/groups/${workspaceId}/reports/${reportId}/GenerateToken`,
  {
    accessLevel: 'View',
    datasetId: DATASET_ID
    // For RLS filtering (custom):
    // identities: [{ username: user@tenant.com, roles: ['SalesManager_North'] }]
  },
  {
    headers: { Authorization: `Bearer ${accessToken}` }
  }
);

3. Frontend Embedding (Custom HTML/iframe)

// Step 3: Embed report in iframe (manual CORS, sandbox configuration)
<iframe
  src={`https://app.powerbi.com/reportEmbed?reportId=${reportId}`}
  allow="geolocation; microphone; camera"
  sandbox="allow-forms allow-popups allow-same-origin allow-scripts allow-top-navigation"
  style={{ width: '100%', height: '600px' }}
/>

Challenges:

• Maximum flexibility (full iframe customization possible)
• Extensive custom development (auth, token generation, iframe management)
• CORS configuration manual
• Security token management complex (refresh tokens, expiration, rotation)
• Error handling custom (token generation failures, embed failures)
• Embedding timelines 2-4 weeks typical for production-ready setup

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DataBrain: SDK-Native Embedding (Days, Not Weeks)

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"
    />
  );
}

Advantages:

• Native React/Vue integration (no iframe complexity)
• Simple token generation (one API call)
• Built-in white-labeling (CSS variables, theming, DOM control)
• Production-ready in days (not weeks)
• Full API control for theme, filters, interactions
• Simplified CORS configuration: No iframe sandbox attribute tuning or low-level CORS header work. Whitelist your app domain in DataBrain and use the SDK
• Automatic token refresh (SDK handles expiration)

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Pricing Models: Which Offers Better ROI?

Complete Power BI Embedded A-SKU pricing ( 2025 ) 

Note: Monthly costs assume 24/7 operation (730 hours per month). You can pause A-SKUs when not in use to reduce costs.

• Power BI Pro: As of 2025, Power BI Pro is priced at $14 per user per month (increased from $10 in April 2025). This license is required for content creators and developers who create and publish embedded reports.‍
• Power BI Embedded Premium Per User: This newer option provides premium features on a per-user basis. As of 2025, the cost is $24 per user per month (increased from $20 in April 2025), with a minimum number of users required. It offers organizations a more cost-effective option to access premium features without the need for dedicated capacity.
• Power BI Premium Capacity (P-SKUs): For enterprise organizations, Premium Capacity provides dedicated cloud compute and storage resources. As of 2025, pricing ranges from approximately $4,995 per month (P1) to higher tiers for increased capacity.
• ‍Power BI Embedded Report Server - This option is an on-premises solution designed for organizations with regulatory or compliance issues. The cost is $20,000 per server license, which includes one year of software assurance

It's important to note that Power BI Embedded requires a Power BI Pro license ($14 per user per month as of 2025) for report creation and editing. However, users who only need to view and interact with embedded reports and dashboards do not require a Power BI Pro license and also Estimates vary by Azure region and are current as of December 2025.

• Node-Based Pricing: Power BI Embedded Analytics has a predictable hourly rate based on the type of node(s) deployed. The total cost depends on the node type chosen and the number of nodes deployed. Node types differ based on the number of V-cores and RAM. It is recommended that ISVs buy Power BI Embedded, while enterprises buy Power BI Premium. In order to embed in a production environment, a capacity must be purchased. Autoscaling is not available in Embedded Premium capacities, and traffic may experience delays during overload. Utilization is evaluated every 30 seconds through interactive and background utilization.

To dive deeper into Power BI Embedded costs and capacity models, see our full Power BI Embedded pricing guide.

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DataBrain Pricing Approach

DataBrain offers transparent, predictable pricing with published tiers:

• Growth Plan: $499/month for up to 10 users, unlimited seats, and 3 data sources.
  
• Pro Plan: $1,250/month with unlimited data sources and advanced features like roles & permissions.
  
• Enterprise Plan: Custom pricing tailored to specialized needs.

Key Advantages:

• No per-user viewing fees.
  
• Flat pricing regardless of audience size.
  
• Predictable costs as you scale.
  
• Lower infrastructure requirements.
  

This pricing structure ensures that as your SaaS platform grows, your analytics costs remain predictable and manageable.

• You're an enterprise building internal or customer analytics with sophisticated business logic
• You need advanced data modelin through DAX (complex calculations, aggregations)
• Your team has strong data engineering and BI expertise
• You have 8-14+ weeks for implementation and can assign dedicated teams
• You need deep Microsoft ecosystem integration (Azure, Office 365, Teams)
• Your data volumes are very large and require Premium Capacity optimization
• You're willing to invest in operational overhead (capacity planning, gateway management, role maintenance)

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Governance & Compliance Comparision

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Conclusion: Which Is Right For Your SaaS Product?

When choosing between DataBrain and Power BI 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 programmatic, automatic tenant isolation (no manual RLS configuration per tenant)
• Time-to-market is critical (days vs weeks/months)
• Your team has limited DBA/BI expertise (product engineers can implement)
• You need governance-first multi-tenancy (compliance, data isolation, audit trails)
• Mobile-responsive, customer-facing analytics is a core differentiator
• You want to minimize operational overhead (managed infrastructure, no capacity planning)
• You need unlimited end-user access without per-seat licensing

Choose Power BI if you prefer:

• Building enterprise internal analytics with sophisticated DAX logic
• Need deep Microsoft ecosystem integration (Azure, Teams, Office 365)
• Have strong data engineering teams for architecture design and RLS governance
• Have 8-14+ weeks for implementation
• Require advanced data modeling capabilities
• Comfortable with multi-tenancy architecture complexity and operational overhead
• Have variable workloads that benefit from capacity pause/resume (dev/test environments) 

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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.

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Related Comparisons & Resources

Looking to compare your options or see how DataBrain measures up against other major embedded analytics platforms? Here are some helpful resources:

• 9 Best Power BI Embedded Alternatives in 2025: A comprehensive guide to top alternatives in the embedded analytics space.
• DataBrain vs GoodData: Detailed comparison between DataBrain and GoodData's enterprise analytics offerings.
• DataBrain vs Sisense: Side-by-side look at DataBrain and Sisense’s enterprise analytics capabilities.
• DataBrain vs Looker: In-depth analysis comparing DataBrain with Google’s Looker Embedded Analytics.
• DataBrain vs ThoughtSpot — Compare DataBrain with ThoughtSpot's AI-driven analytics platform