Power BI Embedded for ISVs: The Complete Guide to Pricing, Architecture, and Multi-Tenant Implementation
Power BI Embedded enables ISVs (Independent Software Vendors) to integrate interactive dashboards and reports directly into their applications. Customers see analytics within your app's UI, branded with your logo, without needing their own Power BI licenses. This ISV-focused guide covers the App Owns Data architecture, capacity pricing for A-SKUs and Fabric F-SKUs, multi-tenant data isolation patterns, step-by-step implementation, security for regulated industries, and the common mistakes that derail ISV embedding projects. Based on 200+ embedded analytics projects delivered by EPC Group for SaaS companies and software vendors.
How Power BI Embedded Works for ISVs
For ISVs, Power BI Embedded is the fastest path to production-grade analytics inside your software product. Instead of spending 12-18 months building a custom charting engine (and maintaining it forever), you embed Microsoft's mature visualization platform into your application in weeks. Your customers get interactive reports, drill-through analysis, export to Excel/PDF, and natural language queries — all within your branded experience. At EPC Group, our Power BI consulting practice has helped over 200 ISVs embed analytics into their products, and the consistent outcome is the same: analytics becomes a revenue driver, not a cost center.
App Owns Data vs. User Owns Data
The foundational decision for every ISV is the embedding model. For ISVs building commercial software where end users are your customers (not your employees), the answer is always App Owns Data (also called "embed for your customers"). Here is how the two models compare:
| Characteristic | App Owns Data (ISV Model) | User Owns Data (Enterprise Model) |
|---|---|---|
| Also known as | Embed for your customers | Embed for your organization |
| Authentication | Service principal (Azure AD app registration) | User's own Azure AD / Entra ID token |
| End user license | None required | Power BI Pro or PPU required |
| End user identity | No Azure AD account needed | Must have Azure AD account |
| Billing model | Capacity-based (A-SKU or F-SKU) | Per-user license ($10-20/user/month) |
| ISV use case | SaaS products, customer portals, commercial software | Internal employee dashboards only |
| White-labeling | Full control — your branding only | Limited — Power BI chrome visible |
EPC Group recommends App Owns Data for 100% of ISV implementations. This model lets you control the entire user experience, eliminates license friction for your customers, and provides predictable capacity-based billing that scales with your business — not with your headcount.
Embed Tokens and Authentication Flow
The authentication flow for App Owns Data follows a server-side token generation pattern that keeps credentials secure:
- Step 1: Your application backend authenticates with Azure AD using a service principal (client ID + client secret or certificate). This happens server-side — credentials never reach the browser.
- Step 2: Using the Azure AD access token, your backend calls the Power BI REST API to generate an embed token for the specific report, dashboard, or dataset the user needs to see.
- Step 3: The embed token (along with the report embed URL and report ID) is passed to your frontend application.
- Step 4: Your frontend uses the Power BI JavaScript SDK to render the report in an HTML container element, passing the embed token for authentication.
- Step 5: The embed token expires after a configurable period (default: 1 hour). Your application refreshes the token in the background before expiration to maintain a seamless user experience.
This architecture ensures that your service principal credentials are never exposed to end users, embed tokens are scoped to specific content, and token expiration limits the blast radius of any token compromise. EPC Group implements token lifecycle management with automatic renewal, token caching, and graceful degradation for all ISV clients.
Capacity Planning: A/EM/F SKUs
Power BI Embedded runs on dedicated capacity — compute resources reserved for your workload. ISVs choose from three SKU families:
- A-SKUs (Azure Embedded): The original ISV SKU. Billed hourly via Azure, can be paused/resumed programmatically, scales from A1 (1 v-core, 3 GB) to A6 (32 v-cores, 100 GB). Best for ISVs needing granular cost control with pause/resume automation.
- EM-SKUs (Power BI Premium Embedded): Legacy SKU being replaced by Fabric. EM1-EM3 provided embedded capacity purchased through Office 365 admin center. Microsoft is migrating EM customers to Fabric F-SKUs — do not start new projects on EM-SKUs.
- F-SKUs (Microsoft Fabric): The current-generation SKU. Scales from F2 (smallest) to F2048 (largest). F64 and above include all Power BI Premium features (paginated reports, AI, deployment pipelines, Direct Lake). Best for new ISV projects — richer features, better price-performance, unified with Fabric data platform.
The right SKU depends on concurrent user count, dataset complexity, refresh frequency, and peak usage patterns. EPC Group runs capacity load testing during every ISV engagement to size the initial SKU and configure autoscaling policies for production. See our capacity planning guide for detailed sizing methodology.
Pricing Deep Dive for ISVs
One of the most common questions ISVs bring to EPC Group is: "How much will Power BI Embedded cost us?" The answer depends on the SKU family, capacity tier, and usage optimization. Here is the complete pricing breakdown as of 2026.
Azure A-SKU Pricing Table
| SKU | V-Cores | RAM | Hourly Rate | Monthly (24/7) | Concurrent Users |
|---|---|---|---|---|---|
| A1 | 1 | 3 GB | $1.00 | ~$735 | 5-10 |
| A2 | 2 | 5 GB | $2.00 | ~$1,470 | 10-25 |
| A3 | 4 | 10 GB | $4.00 | ~$2,940 | 25-50 |
| A4 | 8 | 25 GB | $8.00 | ~$5,880 | 25-100 |
| A5 | 16 | 50 GB | $16.00 | ~$11,760 | 100-500 |
| A6 | 32 | 100 GB | $32.00 | ~$23,520 | 500-2,000 |
Microsoft Fabric F-SKU Pricing
| SKU | CUs | PBI Premium Features | Monthly Cost | ISV Use Case |
|---|---|---|---|---|
| F2 | 2 | No | ~$263 | Dev/test only |
| F8 | 8 | No | ~$1,051 | Small production workloads |
| F32 | 32 | No | ~$4,204 | Medium production workloads |
| F64 | 64 | Yes (all features) | ~$6,059 | Most popular ISV SKU |
| F128 | 128 | Yes | ~$12,118 | Large ISV with 500+ concurrent users |
| F256 | 256 | Yes | ~$24,236 | Enterprise ISV, high concurrency |
Cost Optimization Strategies for ISVs
ISVs that optimize capacity utilization can reduce their Power BI Embedded costs by 40-60% without affecting end-user performance. EPC Group implements these strategies for every ISV client:
- Pause/resume automation: If your SaaS product is used primarily during business hours, automate capacity pause at 8 PM and resume at 6 AM. This alone saves 40% on A-SKU costs. Use Azure Automation runbooks or Logic Apps for scheduling.
- Azure Reserved Instances: Commit to a 1-year or 3-year reservation on A-SKUs for 20-40% discount over pay-as-you-go pricing. Best for ISVs with stable, predictable embedded analytics usage.
- Right-size capacity: Monitor the Capacity Metrics app weekly. If average CPU utilization is below 30%, you are over-provisioned and should scale down. If CPU regularly exceeds 80%, scale up before performance degrades.
- Dataset optimization: Smaller datasets render faster, consume less memory, and allow more concurrent users on the same capacity tier. Remove unused columns, implement aggregations, and use star schema design to minimize model size.
- Shared capacity for dev/test: Use a small F2 or A1 capacity for development and staging environments. Reserve production-tier capacity for customer-facing workloads only.
Per-User vs. Per-Capacity Economics
The economic advantage of Power BI Embedded for ISVs becomes clear when comparing per-user licensing against capacity billing. With Power BI Pro at $10/user/month, an ISV with 500 customers accessing analytics would pay $5,000/month in per-user fees. With 2,000 customers, that rises to $20,000/month. Meanwhile, an A4 capacity at $5,880/month can serve 100+ concurrent users regardless of total user count — if your 2,000 customers do not all use the product simultaneously, you pay $5,880/month instead of $20,000/month. This is why App Owns Data with capacity billing is the universally preferred ISV model. EPC Group models the break-even point for every ISV engagement based on total user count, concurrency ratio, and growth projections.
Architecture Patterns: Multi-Tenant vs. Single-Tenant
The second critical architectural decision (after choosing App Owns Data) is how to isolate data between your customers. ISVs with 10 tenants have different needs than ISVs with 10,000 tenants. EPC Group has implemented all three patterns below and recommends the appropriate model based on tenant count, data volume, compliance requirements, and customization needs.
Pattern 1: Multi-Tenant with Row-Level Security (RLS)
One Power BI workspace contains a single dataset serving all tenants. Data for all customers lives in the same tables. Row-Level Security (RLS) rules filter rows based on a tenant identifier (e.g., customer_id) passed through the embed token when the report loads. Each customer sees only their data.
Best for:
- ISVs with 5-100 tenants with similar data structures
- Products where all customers see the same report layouts
- Cost-sensitive early-stage ISVs (one dataset = lower capacity consumption)
Trade-offs:
- All tenants share the same data model — no per-tenant customization of columns or measures
- Large tenants can slow performance for small tenants (noisy neighbor problem)
- Dataset size grows with total tenant count — may hit model size limits
Pattern 2: Single-Tenant with Dedicated Workspaces
Each customer gets a dedicated Power BI workspace with their own copy of the dataset and reports. Your ISV application provisions new workspaces programmatically using the Power BI REST API when a new customer signs up. Data isolation is absolute — no RLS needed because each customer's data lives in a separate dataset.
Best for:
- Compliance-heavy industries (HIPAA, SOC 2, FedRAMP) requiring physical data separation
- Enterprise customers with unique data models or custom report requirements
- ISVs with fewer than 200 tenants where workspace management overhead is manageable
Trade-offs:
- Higher capacity consumption (N datasets instead of 1, each consuming memory)
- Report updates must be deployed to every workspace (use deployment pipelines)
- Workspace provisioning and lifecycle management adds engineering complexity
Pattern 3: Hybrid Multi-Tenant Architecture
The hybrid approach combines both patterns based on customer tier. Standard-tier customers share a multi-tenant workspace with RLS-based isolation. Enterprise-tier customers get dedicated workspaces with isolated datasets and optional custom report development. This is the pattern EPC Group recommends for growth-stage ISVs that need to serve both small and large customers efficiently.
How it works:
- Self-service and SMB customers are provisioned into shared workspaces with RLS
- Enterprise customers (large data volumes, compliance requirements, or custom analytics) are provisioned into dedicated workspaces
- Your application's tenant routing layer determines which workspace to embed from based on customer tier
- One capacity serves both patterns — shared workspaces consume less memory per tenant, freeing capacity for dedicated enterprise workspaces
Step-by-Step Implementation for ISVs
This 8-step implementation guide covers the end-to-end process for embedding Power BI into your ISV application. EPC Group follows this exact sequence for every ISV engagement, typically completing steps 1-5 in the first 2 weeks and steps 6-8 in weeks 3-4. The result is a production-ready embedded analytics experience in under 30 days.
Step 1: Register an Azure AD (Entra ID) Application
Create a service principal in Azure Entra ID that your application will use to authenticate with Power BI. Navigate to Azure Portal > Microsoft Entra ID > App registrations > New registration. Set the application type to "Web" and configure a client secret or certificate for authentication. Grant the application the following Power BI API permissions: Report.ReadWrite.All, Dataset.ReadWrite.All, Workspace.ReadWrite.All, and Content.Create. Record the Application (client) ID, Directory (tenant) ID, and client secret — these are used by your backend to acquire Azure AD tokens.
Step 2: Create a Power BI Workspace
Create a workspace in the Power BI service (app.powerbi.com) assigned to your embedded capacity. For multi-tenant RLS patterns, create one workspace (e.g., "Production - Analytics"). For workspace-per-tenant patterns, your application will create workspaces programmatically via the REST API. Add your service principal as a workspace member with "Admin" or "Member" role. Assign the workspace to your A-SKU or F-SKU capacity — without capacity assignment, embed tokens will not generate.
Step 3: Publish Reports to the Workspace
Develop your Power BI reports in Power BI Desktop using your production data model. Design reports with embedding in mind: use report-level filters instead of page-level navigation (which does not work well in embedded scenarios), set a responsive layout for different container sizes, and apply a custom theme matching your application's visual identity. Publish the .pbix file to the workspace using Power BI Desktop > Publish > select your workspace. For CI/CD, use the Power BI REST API or deployment pipelines (available on F64+ or Premium capacity) to automate report deployment across dev, staging, and production workspaces.
Step 4: Configure Embed Tokens
Build a server-side endpoint in your application that generates embed tokens using the Power BI REST API. The endpoint should: authenticate with Azure AD using your service principal credentials, call POST /v1.0/myorg/groups/{workspaceId}/reports/{reportId}/GenerateToken with the appropriate access level and RLS identity (for multi-tenant scenarios), return the embed token, embed URL, and report ID to your frontend. Set token expiration to 1 hour (the default) and implement proactive token renewal 5 minutes before expiration. Never expose the service principal credentials or the token generation endpoint directly to client-side code.
Step 5: Integrate with Your Application (React/Angular/.NET)
Use the Power BI JavaScript SDK (powerbi-client) to embed the report in your frontend application. The SDK provides a powerbi.embed() method that accepts a container element, embed configuration (token, URL, report ID, settings), and event handlers. For React applications, use the official powerbi-client-react wrapper which provides a <PowerBIEmbed> component with proper lifecycle management. For Angular, use powerbi-client-angular. For .NET server-rendered applications, embed using the JavaScript SDK with server-generated embed configurations passed to the client. EPC Group has production-grade embedding components for React, Angular, Vue, and Blazor that handle token refresh, error recovery, responsive sizing, and analytics event tracking.
Step 6: Implement Row-Level Security for Multi-Tenancy
If using the multi-tenant RLS pattern, define RLS roles in your Power BI data model using Power BI Desktop. Create a role (e.g., "TenantFilter") with a DAX expression that filters the tenant dimension table: [TenantID] = USERNAME() or [TenantID] = USERPRINCIPALNAME(). When generating embed tokens, pass the customer's tenant identifier as the effective identity. The Power BI engine applies the RLS filter before rendering any data, ensuring each customer sees only their own records. Test RLS thoroughly by generating embed tokens with different tenant identities and verifying data isolation. EPC Group validates RLS with automated integration tests that verify cross-tenant data leakage is impossible.
Step 7: Monitor Capacity Utilization
Install the Power BI Premium Capacity Metrics app (works for both A-SKUs and F-SKUs) to monitor CPU utilization, memory consumption, query duration, and active vs. background operations. Set alerts for: CPU sustained above 80% (indicates capacity pressure), memory above 80% (risk of dataset eviction), query duration above 10 seconds (degraded user experience), and overload events (capacity throttling report rendering). EPC Group configures Azure Monitor integration for ISV clients, forwarding capacity metrics to the ISV's existing monitoring stack (Datadog, Prometheus/Grafana, New Relic) for unified observability.
Step 8: Set Up Automated Data Refresh
Configure scheduled refresh for your datasets to keep embedded reports current. For import-mode datasets, schedule refreshes at intervals matching your data freshness requirements (hourly for operational dashboards, daily for analytical reports). For datasets with more than 1 million rows, implement incremental refresh to reduce refresh duration and capacity consumption. Use the Power BI REST API to trigger on-demand refreshes from your data pipeline when source data updates. For Microsoft Fabric F-SKU capacities, consider Direct Lake mode which eliminates refresh entirely — reports read directly from OneLake storage in near-real-time. See our general embedded analytics guide for additional optimization techniques.
Security for Multi-Tenant ISVs
Security is the non-negotiable requirement for ISV embedded analytics. One customer must never see another customer's data. EPC Group has architected embedded analytics security for ISVs in healthcare (HIPAA), financial services (SOC 2, PCI DSS), and government (FedRAMP), and the security patterns below are battle-tested across these regulated industries.
RLS Patterns for Customer Isolation
Row-Level Security operates at the Power BI data engine level — it filters data before any query executes, making it impossible for a report visual to display unauthorized data. There are two RLS implementation patterns for ISVs:
- Static RLS: Define fixed roles with hardcoded filter expressions (e.g.,
[Region] = "West"). Simple but does not scale — you would need a separate role per tenant. Not recommended for ISVs. - Dynamic RLS: Use
USERNAME()orUSERPRINCIPALNAME()in the DAX filter expression to dynamically resolve the tenant context from the embed token's effective identity. One role serves all tenants. This is the pattern EPC Group implements for all ISV multi-tenant scenarios.
For complex permission structures (one user accessing multiple tenants, hierarchical access, role-based column visibility), EPC Group implements a security mapping table in the data model that joins the user identity to their allowed data scope through DAX relationships.
Object-Level Security (OLS)
OLS hides entire tables or columns from specific roles. ISVs use OLS to create tiered analytics experiences: a basic plan shows revenue and unit counts, while a premium plan also reveals cost, margin, and forecast columns. OLS is defined using Tabular Model Scripting Language (TMSL) in the data model and enforced through the embed token's role assignment. Unlike RLS (which filters rows), OLS removes columns or tables entirely from the model metadata — users cannot even discover that hidden columns exist.
Data Residency Considerations
ISVs serving global customers must consider data residency — where customer data is stored and processed. Power BI Embedded capacity is deployed in a specific Azure region, and all data processing occurs in that region. For GDPR compliance, European customer data should be processed on EU-based capacity. For data sovereignty requirements (Australia, Canada, Brazil), deploy separate capacities in the appropriate Azure regions. EPC Group helps ISVs design multi-region embedded analytics architectures that comply with data residency regulations while minimizing capacity cost and operational complexity. This includes region-based tenant routing, cross-region report template synchronization, and multi-geo capacity management.
5 Common Mistakes ISVs Make with Power BI Embedded
After delivering 200+ ISV embedded analytics projects, EPC Group has seen the same mistakes repeatedly. Avoiding these pitfalls saves months of rework and prevents production incidents.
Mistake 1: Using a Master Account Instead of a Service Principal
Early Power BI Embedded documentation showed authentication via a "master account" — a regular Azure AD user account whose credentials are stored in the application. This creates a single point of failure: if the user account password expires, MFA is enforced, or the account is disabled by IT policy, your entire embedded analytics experience goes down for all customers. Service principals do not have passwords that expire, are immune to MFA enforcement, and can be managed through Azure AD app registrations with proper RBAC. EPC Group migrates all ISV clients from master accounts to service principals as a first step.
Mistake 2: Skipping Capacity Load Testing
ISVs deploy to production on an A2 because it handles 15 concurrent users in dev/test, then discover on launch day that 100 concurrent users cause 30-second report load times and capacity throttling. Capacity requirements are not linear — complex DAX calculations, large datasets, and concurrent refresh operations all compete for the same v-cores. EPC Group runs load testing simulations with the ISV's actual reports and realistic user counts before production go-live, then configures autoscaling policies to handle demand spikes.
Mistake 3: Exposing Embed Token Generation to the Client
Some ISV developers call the Power BI REST API directly from the browser to generate embed tokens, embedding the service principal credentials in client-side JavaScript. This is a critical security vulnerability — anyone can extract the credentials, generate tokens for any report, and access all tenant data. Embed token generation must always happen server-side through a secured API endpoint. The client receives only the short-lived embed token, never the service principal credentials.
Mistake 4: Not Planning for Dataset Growth
ISVs launch with a 100 MB dataset serving 10 tenants. Twelve months later, 200 tenants have pushed the dataset to 8 GB. Import refresh now takes 45 minutes. The A2 capacity runs out of memory. Report rendering slows to a crawl during refresh windows. The fix requires rearchitecting the data model, implementing incremental refresh, and upgrading capacity — all under production pressure. EPC Group builds growth projections into the initial architecture: dataset partitioning strategies, incremental refresh from day one, and capacity upgrade triggers based on tenant growth milestones.
Mistake 5: Ignoring the Report Design for Embedding
Reports designed for Power BI Desktop or the Power BI service do not automatically work well in embedded contexts. Page navigation behaves differently. The filter pane overlaps content in narrow containers. Tooltips position off-screen. Bookmarks fail to load. Mobile layouts are not available. ISVs must design reports specifically for embedded consumption: test in the actual application container size, use custom visual layouts for responsive design, implement bookmarks and report events for application-controlled navigation, and disable Power BI chrome elements (filter pane, page tabs) that conflict with the application's UI. EPC Group's report designers build embedding-optimized reports from the start, eliminating the redesign cycle that catches most ISVs by surprise.
Frequently Asked Questions
What is Power BI Embedded and how does it work for ISVs?
Power BI Embedded enables ISVs (Independent Software Vendors) to integrate interactive dashboards and reports directly into their applications. Customers see analytics within your app's UI, branded with your logo, without needing their own Power BI licenses. The ISV uses the "App Owns Data" model where a service principal authenticates with Power BI on behalf of all end users. The ISV pays for Azure or Fabric capacity (not per-user licenses), and end users interact with fully interactive visualizations — filters, slicers, drill-through, export — as a native part of the ISV's product. EPC Group has delivered Power BI Embedded implementations for over 200 ISV and SaaS clients across healthcare, financial services, education, and logistics.
How much does Power BI Embedded cost for ISVs?
Power BI Embedded pricing is capacity-based, not per-user. Azure A-SKUs start at A1 ($1/hour, approximately $735/month) with 3 GB RAM and 1 v-core. The most common ISV SKUs are A2 ($2/hour, approximately $1,470/month) for early-stage products and A4 ($8/hour, approximately $5,880/month) for production workloads supporting 25-100 concurrent users. Microsoft Fabric F-SKUs start at F2 ($0.36/hour, approximately $263/month) and scale to F2048. Fabric F64 ($6,059/month) is the most popular ISV choice as it includes Power BI Premium features. Both A-SKUs and F-SKUs can be paused when not in use, reducing costs by 40-60% for applications with predictable usage windows. There are zero per-user fees for embedded content consumers — this is the key ISV advantage.
What is the difference between App Owns Data and User Owns Data for ISVs?
"App Owns Data" (embed for your customers) is the correct model for ISVs. Your application uses a service principal or master account to authenticate with Power BI, generates embed tokens server-side, and serves embedded reports to end users who have no Power BI accounts or Azure AD identities. This is how SaaS products, customer portals, and commercial software embed analytics. "User Owns Data" (embed for your organization) requires each user to sign in with their own Azure AD account and Power BI Pro license — this model is for internal enterprise apps, not ISV products. EPC Group recommends App Owns Data for 100% of ISV implementations and provides architecture guidance to ensure proper service principal configuration, token lifecycle management, and capacity planning.
How do ISVs handle multi-tenant data isolation in Power BI Embedded?
Multi-tenant isolation in Power BI Embedded uses two primary patterns. Row-Level Security (RLS) multi-tenancy stores all tenant data in a single dataset and applies DAX filters based on a tenant identifier passed via the embed token — this is cost-effective and simpler to manage for up to 50-100 tenants with similar data structures. Workspace-per-tenant multi-tenancy creates isolated workspaces and datasets for each customer, providing complete data separation — required for compliance-heavy industries (HIPAA, SOC 2) or tenants exceeding 1 GB of data. A hybrid approach uses RLS for small tenants and dedicated workspaces for enterprise customers. EPC Group designs the isolation architecture based on regulatory requirements, data volume per tenant, and scalability targets.
Can ISVs white-label Power BI Embedded reports?
Yes. Power BI Embedded supports full white-labeling. ISVs apply custom themes (colors, fonts, logos) to match their application branding, hide the Power BI navigation pane and action bar, embed reports in custom page layouts using the Power BI JavaScript SDK, disable or customize the filter pane, and control which interactions users can perform (disable export, restrict drill-through, hide specific visuals). End users never see the Power BI brand unless the ISV chooses to display it. The embedded report looks and feels like a native feature of the ISV application. EPC Group builds custom React and Angular embedding components that provide seamless white-labeled analytics experiences for ISV clients.
How do I choose between Azure A-SKUs and Fabric F-SKUs for my ISV product?
For new ISV projects in 2026, EPC Group recommends Microsoft Fabric F-SKUs over legacy Azure A-SKUs. F-SKUs include all Power BI Premium features at F64 and above (paginated reports, AI visuals, large datasets, deployment pipelines, Copilot). F-SKUs support Direct Lake mode for near-real-time report rendering without scheduled refreshes. F-SKUs provide unified billing with other Fabric workloads (data engineering, data science). F-SKUs offer more granular scaling options (F2 through F2048). A-SKUs remain valid for ISVs already in production on Azure Embedded capacities or those needing pause/resume automation via Azure Resource Manager APIs. The key decision factor: if your ISV product already uses Azure data services, stay with A-SKUs for billing simplicity; if starting fresh, choose Fabric F-SKUs for the richer feature set.
What security certifications does Power BI Embedded support for ISV compliance?
Power BI Embedded inherits Microsoft Azure's comprehensive compliance certifications: SOC 1/2/3, ISO 27001, ISO 27018, HIPAA BAA, FedRAMP High, HITRUST, PCI DSS, and GDPR. For ISVs serving regulated industries, the key security layers are: Row-Level Security (RLS) for tenant data isolation enforced at the data engine level, Object-Level Security (OLS) for hiding sensitive columns or tables from specific user roles, embed token expiration (configurable, default 1 hour) with server-side generation preventing client-side tampering, Azure Private Link for network-level isolation, customer-managed encryption keys (BYOK) for data-at-rest encryption, and data residency controls via Azure region selection. EPC Group architects ISV embedded analytics solutions that meet HIPAA, SOC 2, and FedRAMP requirements with documented security controls for audit readiness.
Partner with EPC Group for ISV Embedded Analytics
EPC Group has delivered over 200 Power BI Embedded analytics projects for ISVs and SaaS companies across healthcare, financial services, education, logistics, and manufacturing. Our Power BI consulting team handles the complete embedded analytics lifecycle: architecture design (multi-tenant vs. single-tenant), capacity sizing and cost optimization, report development optimized for embedding, RLS and OLS security implementation, production deployment, and ongoing performance monitoring. ISVs partner with EPC Group because we have delivered embedded analytics at scale — solutions handling millions of monthly report views with sub-second rendering and zero cross-tenant data leakage. From proof-of-concept through production launch, we deliver embedded analytics that becomes a competitive differentiator for your product. Learn more about EPC Group.