- Power BI Embedded lets you integrate Power BI reports and visuals into your application, while your team remains responsible for the surrounding product experience.
- App-owns-data and user-owns-data serve different audiences, with distinct authentication, licensing, and capacity considerations.
- ThoughtSpot Embedded offers another approach for software teams that want to integrate interactive, self-service analytics into customer-facing products.
- The right platform depends on how users explore data, the degree of customization you need, your security requirements, and your team's development workflow.
- Evaluate both platforms against real customer scenarios before making a long-term architecture decision.
Embedding analytics into your application involves more than displaying a dashboard. Your users expect insights to fit naturally into their workflows, respect their permissions, and help them answer business questions without unnecessary friction.
Microsoft Power BI Embedded provides a way to integrate Power BI content into applications. For organizations already invested in Power BI, it can be a practical option for delivering familiar reports through an existing product interface.
But what if your customers need more than predefined dashboards? What if they want to investigate trends, ask follow-up questions, or explore data without relying on your team to build a new report for every request?
That's where your choice of embedded analytics platform becomes a product decision, not just a technical one.
This guide explains how Power BI Embedded works, what to evaluate before implementing it, and how ThoughtSpot Embedded fits into the decision when interactive, self-service analytics is part of your product roadmap.
What Is Power BI Embedded Analytics?
Power BI Embedded is Microsoft's offering for independent software vendors and developers who want to integrate Power BI reports, dashboards, and visualizations into their applications.
Common use cases include customer portals, SaaS reporting, partner applications, and internal business tools.
Instead of directing users to the Power BI service, you can present embedded content within your own interface. Your application provides the surrounding navigation, context, permissions, and customer journey.
Microsoft's Power BI embedding documentation describes the supported integration approaches and their requirements.
However, embedding a report does not automatically create a complete analytics experience. Your team must still design authentication, tenant isolation, user permissions, navigation, and the workflows that connect insights to action.
Where Power BI Embedded Fits Best
Power BI Embedded can be a good fit when your organization already creates Power BI content and wants to make those reports available through another application.
For example, a software company might have an established reporting team, existing Power BI semantic models, and customers who primarily need access to standardized dashboards.
In this situation, embedding familiar reports can help bring analytics into the product without requiring the organization to replace its existing reporting processes.
The requirements change when analytics becomes a core part of the customer experience.
Consider a SaaS application where customers need to investigate declining sales, identify changes in customer behavior, or compare performance across different business units. A predefined dashboard might answer the first question but leave users looking for additional context.
Your team must decide whether users will primarily consume prepared reports or need a deeper self-service experience.
That distinction is an important starting point when evaluating Power BI Embedded alongside alternatives such as ThoughtSpot Embedded.
Understand Power BI Embedded's Access Models
Power BI Embedded offers two principal access patterns: app-owns-data and user-owns-data.
Choosing between them affects how users authenticate, how your application requests access to reports, and which licensing and capacity requirements apply.
Microsoft's embedded access-token documentation provides implementation details for both models.
| Decision area | App-owns-data | User-owns-data |
|---|---|---|
| Primary audience | External customers and application users | Internal users who already use Power BI |
| Authentication | Users authenticate through the host application | Users authenticate through Microsoft Entra and Power BI |
| Access control | Your application manages authorization and embedding access | Power BI permissions govern access to content |
| Viewer licensing | Individual Power BI viewer licenses generally aren't required for embedded content under this model | Requirements depend on the capacity and licensing configuration |
| Typical use case | Customer-facing SaaS applications and portals | Internal applications for employees |
| Key consideration | Application-managed identity, authorization, and tenant isolation | Existing Power BI identities, permissions, and capacity requirements |
This table provides a starting point rather than a substitute for licensing guidance. Confirm Microsoft's current requirements for your chosen capacity, publishing workflow, and intended audience before deployment.
App-Owns-Data for Customer-Facing Applications
In the app-owns-data model, users authenticate through your application rather than signing in to Power BI directly.
Your application uses an appropriate embedding identity, such as a service principal, to obtain embed tokens for authorized content.
This approach is designed for customer-facing experiences where users may not have individual Power BI accounts.
However, it also means your team must implement the surrounding authorization controls carefully.
You'll need to manage tenant mapping, token scope, expiration, permissions, and auditing. You must also test whether users can access only the reports and data they're authorized to see.
For a multi-tenant SaaS product, these responsibilities are central to the architecture.
User-Owns-Data for Internal Applications
In the user-owns-data model, users authenticate with their Microsoft Entra and Power BI identities.
Their existing Power BI permissions determine which content they can access.
This approach can make sense when employees already use Power BI and the application provides another interface for accessing their reports.
Viewer licensing depends on the capacity configuration. For example, Microsoft documents different licensing requirements for A SKUs and capacities below F64 compared with eligible larger capacities.
A SKUs do not support user-owns-data embedding.
Before choosing this model, validate the identity, licensing, and capacity requirements against your organization's intended use.
Plan for Capacity, Cost, and Production Operations
A successful development prototype does not establish production readiness.
Microsoft permits trial embed tokens for development testing, but production embedding requires an applicable capacity based on the selected access model.
Your cost and performance planning should account for more than the number of users.
Consider:
Concurrent viewers: How many people will access analytics during peak periods?
Report complexity: How many pages, visuals, and queries must each experience support?
Data volume: How complex are your semantic models and underlying datasets?
Refresh requirements: How frequently must data update?
Tenant distribution: How evenly is usage distributed across customers?
Usage spikes: Will launches, reporting deadlines, or seasonal activity create sudden demand?
Microsoft's Power BI Embedded pricing documentation explains the consumption model for A SKUs, including hourly billing and options for scaling resources.
Actual costs and performance depend on your configuration and workload. Test realistic scenarios before committing to a production capacity.
These operational considerations also matter when evaluating alternative platforms. Compare the full implementation and operating requirements rather than relying on a feature checklist or a headline pricing figure.
Treat Tenant Isolation as a Product Requirement
Multi-tenant analytics requires a deliberate security architecture.
In a customer-facing application, one customer's access must never expose another customer's data. Authentication, authorization, tenant mapping, and data filtering must work together.
Power BI supports approaches such as workspace-based isolation and row-level security. Each has implementation and operational implications.
With workspace isolation, you can separate customer content into distinct workspaces. With row-level security, shared models can restrict which records users can access.
Your choice depends on the data model, tenant lifecycle, governance requirements, and operational processes.
Test both permitted and denied access paths, including:
Attempts to access another customer's data.
Changes to customer roles and permissions.
Expired tokens and revoked access.
New-customer provisioning and customer deletion.
Administrative and support access.
Changes to tenant mappings and data-filtering rules.
These are not merely engineering details. A security failure can undermine customer trust and compromise the product experience.
Apply the same scrutiny when evaluating any embedded analytics platform, including ThoughtSpot Embedded. Verify how its documented capabilities and implementation patterns fit your identity model, data architecture, and governance requirements.
Evaluate the Experience Beyond the Dashboard
An embedded report is only one component of a larger product experience.
Before selecting a platform, establish what customers should be able to accomplish inside your application.
Ask questions such as:
Can users navigate analytics without leaving the relevant workflow?
Can they filter data and drill into details?
Can they investigate questions that aren't covered by predefined dashboards?
How much control do you need over branding, layout, and interactions?
Can developers manage analytics changes through their normal release process?
How will you govern content, user permissions, and tenant access?
Can users act on an insight without switching to another application?
Who owns monitoring, incident response, content quality, and customer support?
The answers help distinguish a reporting requirement from a broader embedded analytics requirement.
For example, if customers need a fixed set of operational reports, your team may prioritize existing report compatibility, predictable access controls, and straightforward integration.
If customers need to investigate changing business conditions, the requirements may extend to self-service exploration, natural-language analytics, and more interactive ways to work with data.
This is where ThoughtSpot Embedded becomes particularly relevant to your evaluation.
Power BI Embedded vs. ThoughtSpot Embedded: What Should You Compare?
Power BI Embedded and ThoughtSpot Embedded can both be considered when building analytics into an application. However, your decision should reflect the experience you want to deliver rather than assuming that all embedded analytics solutions serve the same purpose.
ThoughtSpot Embedded provides tools for integrating analytics experiences into applications through its Visual Embed SDK and APIs.
Its documented capabilities include natural-language exploration, drill-downs, custom actions, writebacks, multi-tenancy, governance, and analytics-as-code.
These capabilities can be relevant when you want customers to do more than view predefined reports.
Here's how to structure your evaluation.
| Evaluation criterion | Power BI Embedded | ThoughtSpot Embedded |
|---|---|---|
| Existing reporting investment | Relevant when your organization already builds and maintains Power BI reports and semantic models | Evaluate how ThoughtSpot fits your existing data architecture and analytics requirements |
| Prepared dashboards | Supports embedding Power BI reports and visualizations | Supports embedded analytics experiences that can include visualizations and interactive exploration |
| Self-service exploration | Evaluate the reports, interactions, and additional capabilities needed for your users' questions | Evaluate its documented self-service and natural-language exploration capabilities against your use cases |
| Customization | Assess how the embedded experience fits your application's interface and workflow | Assess the Visual Embed SDK, APIs, and customization options against your product requirements |
| Application workflows | Determine how insights connect to actions in your application | Evaluate documented custom actions, writebacks, and integration options for your workflows |
| Multi-tenancy and governance | Validate identity, workspace, row-level security, and operational requirements | Validate supported multi-tenancy and governance capabilities against your security architecture |
| Developer workflow | Assess embedding implementation, deployment, and ongoing maintenance | Assess embedding APIs, analytics-as-code, and development processes |
| Cost and operations | Model capacity, licensing, workload, and operating costs | Validate applicable licensing, deployment, usage, and operational costs with ThoughtSpot |
This is an evaluation framework, not a claim that one platform is universally better. Specific capabilities, licensing, and implementation requirements should be verified against the current product documentation.
When Power BI Embedded May Be the Better Fit
Power BI Embedded deserves consideration when your organization already relies on Power BI for reporting and wants to bring that investment into an existing application.
It may be appropriate when:
Your team already maintains Power BI reports and semantic models.
Your customers primarily need standardized reports and dashboards.
Your organization is comfortable managing the chosen embedding and security architecture.
The licensing, capacity, and operational model fits your requirements.
In these situations, reuse of existing assets and established reporting processes can be important selection criteria.
The key is to verify that your current reporting approach will also support the customer experience you intend to deliver as the product evolves.
When to Consider ThoughtSpot Embedded
ThoughtSpot Embedded is worth evaluating when your product roadmap calls for analytics to become a more interactive part of the customer experience.
It may be relevant when:
Customers need to explore questions beyond a fixed set of dashboards.
Natural-language analytics is part of the desired user experience.
You want to integrate analytics interactions into application workflows.
Your product team needs embedding and API capabilities that fit its development practices.
You want to assess features such as custom actions, writebacks, and analytics-as-code.
For example, imagine a SaaS platform that provides customers with sales performance dashboards.
A customer might begin by reviewing revenue trends, then want to investigate which products, regions, or customer segments contributed to a decline. They may also want to use those findings within an existing workflow.
The platform you choose should support the specific exploration and actions you intend to offer, within your security and governance boundaries.
ThoughtSpot's documented embedding capabilities provide a basis for evaluating that experience. Your team should validate the relevant workflows with realistic data and customer scenarios before making a decision.
Explore the ThoughtSpot Embedded product documentation to understand its available integration and customization options.
Build a Proof of Concept Around Real Customer Questions
Feature comparisons are useful, but they cannot replace testing.
Before selecting a platform, develop a small set of representative scenarios and evaluate each option against the same requirements.
For example:
Scenario 1: Standard reporting
Can a customer open the relevant dashboard, apply filters, and view the information permitted by their role?
Scenario 2: Follow-up exploration
Can the customer investigate a trend, compare segments, or answer a related question without waiting for your team to build another report?
Scenario 3: Tenant isolation
Can your application enforce access boundaries when users switch accounts, change roles, or request data from different tenants?
Scenario 4: Workflow integration
Can users move from an insight to a relevant application action without an awkward transition between tools?
Scenario 5: Production operations
Can your team monitor usage, manage releases, investigate failures, and maintain acceptable performance under realistic workloads?
Measure the results against agreed criteria, including implementation effort, user experience, security, performance, and operating cost.
This approach gives product, engineering, security, and data teams a shared basis for decision-making.
It also helps you identify the requirements that matter most before committing to a long-term architecture.
Make Your Embedded Analytics Choice a Product Decision
Choosing an embedded analytics platform means deciding how customers will interact with data inside your product.
Power BI Embedded can be a practical option for organizations looking to integrate existing Power BI reporting into applications. Its access models, capacity requirements, licensing rules, and security architecture should be evaluated against the intended audience and workload.
ThoughtSpot Embedded offers another option for teams exploring interactive, self-service analytics and ways to integrate data exploration into customer workflows.
The right choice depends on your existing investments, customer expectations, development processes, security requirements, and product roadmap.
Rather than selecting a platform based on a single feature, define the experience you want to deliver. Test both approaches against real customer questions, validate their technical requirements, and compare the effort required to maintain them over time.
If your goal is to make analytics an interactive part of your product rather than simply embedding reports, explore ThoughtSpot Embedded and evaluate how its capabilities align with your customer experience.
