The Tool Desk
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“Tenant-level analytics” can mean either separate analytics assets for each customer or a shared model filtered to each customer’s rows. A “shared dashboard” describes the presentation layer, not how its underlying data is isolated. Decide based on the data model and enforcement boundary, not the labels.
How the two approaches differ
| Decision area | Shared assets with RLS | Separate assets per tenant |
|---|---|---|
| Data and reports | Customers use a shared report, model, or dataset; tenant-aware RLS limits which rows each user can see. | Each customer has separate workspace, model, report, or dataset assets. Database schemas or instances may also be separated. |
| Onboarding and maintenance | Fewer assets to provision and maintain. Microsoft says this can simplify onboarding for smaller customer bases. | More customer-specific assets to provision, update, and retire. AWS notes the added automation and development overhead. |
| Isolation boundary | Tenant data may reside in one shared model or dataset, so the correctness and administration of RLS rules are critical. | Separation can make customer boundaries more explicit at the workspace, model, dataset, schema, or database level, but the application still needs correct authorization and tenant mapping. |
| Scale and cost visibility | Shared capacity has scaling constraints. In QuickSight, shared assets can make per-tenant SPICE cost tracking less straightforward and may bring storage limits into play. | Separate models can be administered and scaled independently, though capacity and refresh constraints remain. Per-tenant costs can be easier to track. |
| Regional placement | A shared model may not fit a requirement to place or separate a particular customer’s data. Confirm the service, contract, and regulatory obligations. | Microsoft documents assigning tenant workspaces to capacities in desired regions; duplicating assets across regions can raise cost and management complexity. |
| Customization | Common changes can roll out through one report or model, but customer-specific divergence can complicate the shared design. | Separate assets support customer-specific management and customization, with more lifecycle work. |
These trade-offs are reflected in Microsoft’s Power BI multitenancy guidance, its customer-embedding guidance, and AWS guidance on QuickSight multi-tenant applications and tenant isolation.
When shared analytics with RLS is a good fit
A shared model and dashboard are a reasonable candidate when your customer base and semantic models are modest, customers need a common analytics experience, and your team can rigorously validate the link between authenticated users and tenant-specific RLS rules. This approach can avoid duplicating assets and reduce onboarding and maintenance work.
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Microsoft specifically describes one model and report with dynamic RLS as a way to simplify maintenance and onboarding for smaller ISVs with relatively few customers and small- to medium-sized semantic models. The trade-off is that data and capacity are shared, which can constrain scaling as usage or model demands grow.
When separate workspaces, models, or datasets make sense
Consider separation when customers need stronger asset-level boundaries, independent administration or scaling, distinct regional placement, or substantial customer-specific customization. Microsoft recommends workspace separation for customer-facing multitenant embedding and documents service principal profiles for managing customers through separate workspaces. AWS also identifies industry-specific isolation needs as a reason to use tenant-specific QuickSight assets.
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These are vendor architecture patterns, not a substitute for evaluating your threat model, contracts, and compliance obligations. A separate dashboard does not by itself prove that the application has prevented cross-tenant access.
Analytics isolation is only one layer
A shared dashboard can sit over data that is partitioned elsewhere, and distinct dashboards can still be exposed through an application that mishandles tenant access. AWS distinguishes three broader database patterns:
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- Silo: a database instance per tenant.
- Bridge: a shared database instance with tenant schemas.
- Pool: shared database objects with database-level RLS.
These patterns can complement analytics-layer decisions. AWS’s tenant isolation guidance stresses that isolation is separate from general security mechanisms: authentication and authorization alone do not necessarily stop one tenant’s user from reaching another tenant’s resources. Tenant context must scope access across every relevant resource path.
Validate the tenant boundary from identity to data
- Carry tenant identity from the authenticated application context. Establish how the signed-in user maps to a tenant, and pass that context consistently into analytics authorization. AWS cautions against relying on a separate, standalone user-to-tenant mapping.
- Map every enforcement point. Document whether access is restricted in the application or service, database, semantic model or dataset, workspace, or multiple layers. AWS recommends using a store’s native isolation feature when appropriate.
- Configure embedded identity correctly. For Power BI embedded customer scenarios, the application authenticates its users and sets the effective identity on the embed token as required for RLS. Customers do not necessarily sign in with Power BI accounts. See Microsoft’s Power BI embedded RLS security guidance.
- Test attempted boundary crossings. Include negative cases such as changed tenant identifiers, missing or stale identity context, exports, cached results, background jobs, and administrative paths. These are prudent validation cases based on the requirement to scope access by tenant; they are not an exhaustive test list prescribed by the cited official materials.
- Plan refresh and capacity. Separate models bring refresh and capacity considerations; shared versus per-tenant QuickSight assets affect SPICE storage and per-tenant cost visibility.
- Verify current service constraints. Microsoft states that production customer embedding requires a billable capacity-backed workspace type and advises customers to consider Fabric capacity as Power BI Premium per-capacity SKUs are consolidated. Check current licensing, capacity limits, and region behavior in the applicable service documentation before implementation.
Make the choice against your actual requirements
- Isolation and compliance: What boundary do contracts, regulations, or your threat model require, and where must it be enforced?
- Customer needs: Can customers share a common analytics experience, or do they need separate reports, administration, or customization?
- Operating effort: Can your team automate provisioning, updates, and cleanup for per-tenant assets, or is minimizing duplicated maintenance more important?
- Growth and capacity: How will customer count, model size, refresh frequency, and usage affect the shared or separate capacity?
- Cost and geography: Do you need per-tenant cost visibility or customer-specific regional placement, and what overhead would those requirements add?
There is no universal customer-count or model-size threshold that settles the decision for every SaaS. Use the vendor guidance as a pattern, then validate the design against your own data flows and operating requirements.
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