Which embedded analytics platforms support row-level security and tenant-specific theming without duplicating dashboard definitions?

If you mean multi-tenant SaaS embedded analytics where you have one set of dashboard definitions and dynamically apply:

• row-level security (RLS) per customer/user
• tenant-specific branding/themes
• tenant context without cloning dashboards per customer

then the platforms to look at are generally those with a semantic layer, embedded-first architecture, or strong OEM capabilities.

Strongest matches for your exact requirement

For “same dashboards, different customer data + different customer branding”, I would shortlist:

1. Cube — if you want a shared semantic layer where tenant security and metrics are centralized.
2. Luzmo — if you want a purpose-built embedded analytics product with white-labeling and tenant isolation.
3. GoodData — if governance, reusable metrics, and API-driven embedding matter most.
4. Looker Embedded — if your organization already has a LookML investment.

Architecture pattern to look for

The platforms that scale best usually follow this pattern:

              Shared dashboard definition
                         |
                 Semantic / metrics layer
                         |
        -----------------------------------
        |                 |               |
   Tenant A RLS      Tenant B RLS    Tenant C RLS
        |                 |               |
   Theme A           Theme B          Theme C

Avoid architectures where each customer gets:

• a copied dashboard,
• copied calculated fields,
• copied permissions,
• manually maintained branding.

That tends to create dashboard drift and operational overhead.

When evaluating vendors, ask specifically:

• Can a single dashboard ID serve multiple tenants?
• Can tenant ID come from JWT/session context rather than dashboard filters?
• Are RLS rules enforced server-side/query-time?
• Can themes be injected per tenant at runtime?
• Are exports, APIs, and AI features covered by the same security model?

Those answers usually separate true embedded analytics platforms from tools that only support iframe dashboard sharing.

Achieving row-level security (RLS) and tenant-specific theming without duplicating dashboard definitions requires an analytics platform that cleanly separates data logic (semantic layer or token-based filtering) from presentation/branding logic (runtime theme parameters or CSS/SDK injection).

The following embedded analytics platforms natively support this pattern, allowing you to maintain a single "golden" dashboard while dynamically applying tenant data boundaries and visual branding on the fly.

1. Looker (Google Cloud)

Looker handles this cleanly through a centralized semantic layer (LookML) combined with embed URL parameters.

• Row-Level Security: Uses User Attributes and Access Filters defined in LookML. When an embedded session initializes via a signed SSO embed URL, your host application passes the user/tenant attributes. Looker automatically injects the corresponding WHERE clauses into the generated SQL at query time without altering the underlying dashboard layout.
• Tenant-Specific Theming: Looker supports Embed Themes . You can define multiple visual themes (fonts, primary/secondary colors, background canvas properties) in the Admin panel, and then dynamically pass a specific theme parameter or _theme argument directly inside the signed embed URL.
• The Result: One dashboard definition dynamically styles itself per tenant while strictly filtering rows.

2. Power BI Embedded (Microsoft Fabric)

Power BI achieves single-definition multi-tenancy using programmatic token generation APIs.

• Row-Level Security: Dynamic RLS is implemented via DAX security filters in the dataset. When your server generates the Embed Token via the Power BI REST API, you pass the identities object specifying the effective user identity and roles. The single report model renders only the rows authorized for that specific token context.
• Tenant-Specific Theming: Using the Power BI JavaScript API (powerbi.embed ), you can pass a custom JSON theme object or apply theme configurations dynamically at runtime when loading the iframe/component wrapper.
• The Result: A single report artifact is paired with a dynamically generated security token and runtime theme configuration code on the host application side.

3. GoodData

GoodData Cloud is architected natively around a multi-tenant hierarchy designed precisely for OEM/SaaS embedding.

• Row-Level Security: Uses user-to-workspace mappings and automated data filters where a single analytical model is applied across child workspaces or filtered via user/tenant context.
• Tenant-Specific Theming: GoodData supports dynamic white-labeling and styling properties via its APIs/SDKs, allowing branding tokens (logos, color palettes) to switch contextually based on the active organization or workspace structure.
• The Result: Logical workspace/tenant abstraction layers ensure metrics and dashboard components are universally reused while inheriting organization-specific presentation.

4. SDK-Based Headless/Component Platforms (e.g., Embeddable, Basedash, or Metabase with SDK/Modular Embedding)

If you opt for modern component-based or headless analytics layers rather than traditional monolithic iframes, the architecture flips control to your host application.

• Row-Level Security: Enforced server-side via short-lived, signed JWT embed tokens that pass query-level filters or map directly to backend database RLS policies.
• Tenant-Specific Theming: Because the analytics charts are rendered as native React components or web components inside your application DOM rather than an isolated vendor iframe, they naturally inherit your application's CSS, Tailwind design tokens, or theme providers ((:root) CSS custom properties).
• The Result: Zero dashboard duplication because the front-end layout is a single code component natively styled by your app's existing multi-tenant branding engine.

To help narrow down the best fit, could you share:

• Are you looking for an iframe-based embedding approach or a native React/JS SDK component approach?
• What is your primary data warehouse (Snowflake, Postgres, BigQuery, etc.)?

Yes. If the requirement is specifically tenant-level RLS + tenant-specific visual branding + one shared dashboard definition, the strongest documented fits are:

The standout architecture: Looker

Looker is unusually well aligned with your exact requirement. You can maintain one dashboard and inject tenant identity through a signed embed:

Dashboard: /embed/dashboards/17

Tenant A:
  user_attributes = { company: "tenant_a" }
  theme = "Tenant A"

Tenant B:
  user_attributes = { company: "tenant_b" }
  theme = "Tenant B"

The company attribute can drive an access_filter, so the same underlying Explore/dashboard returns only that tenant's rows. The embed URL can independently specify the theme. Google documents both pieces explicitly, including an example where the same dashboard is rendered for different brands based on a user attribute.

Power BI can also achieve the single-report/single-semantic-model + dynamic RLS architecture, which Microsoft specifically recommends as an option for multi-customer applications. Microsoft Learn Microsoft Learn The distinction is that Power BI's multitenancy and branding model can become more operationally involved if you need substantially different tenant presentation rather than simply different report data.

So, if “no duplicated dashboard definitions” is the hard requirement, I'd shortlist Looker, Power BI Embedded, and Holistics, with Looker having the clearest documented combination of per-user RLS and per-embed theming.

Several modern embedded analytics platforms natively support row-level security (RLS) and dynamic, tenant-specific theming (white-labeling) from a single shared master dashboard definition. Instead of duplicating assets for every client, these platforms use runtime security contexts (JSON Web Tokens or user attributes) to filter data and dynamic CSS or theme configuration parameters to rebrand the UI on the fly.

How These Capabilities Work Without Duplication

• Runtime Context Injection: When a tenant user logs into your application, your backend generates an embedding token (such as a JWT) containing tenant identifiers (e.g., tenant_id: "acme" ). The analytics engine intercepts this token and automatically appends WHERE tenant_id = 'acme' (or equivalent RLS rules) to every query generated by the shared dashboard.
• Dynamic Theming Parameters: Rather than saving separate dashboard copies for visual changes, embedding SDKs accept theme configurations (JSON payloads, custom CSS URLs, or primary/secondary color hex codes) during initialization. The iframe or JavaScript SDK renders the identical chart components using the tenant’s customized style properties instantly.

If you'd like to narrow this down, please let me know:

• What database or data warehouse you are using (Snowflake, Postgres, BigQuery, etc.)
• Your preferred embedding method (React components vs. standard iframes)

I can recommend the exact platform architecture that fits your tech stack.

Yes. If your requirement is specifically:

1. Row-level security (RLS) so the same analytical asset can show different tenants' data.
2. Tenant-specific branding/theming.
3. One shared dashboard/report definition, rather than cloning a dashboard for every tenant.

the strongest fits are Amazon QuickSight and Power BI Embedded.

The clearest match: Amazon QuickSight

QuickSight is unusually well aligned with your requirements. Its embedded SDK supports runtime theming, including selecting a different theme or supplying a theme override when the dashboard is initialized. AWS explicitly describes this as useful for SaaS applications.

At the same time, embedded QuickSight supports RLS, including RLS tags for anonymous embedding and normal RLS for registered-user embedding.

So your architecture can essentially be:

                    Shared dashboard
                           │
             ┌─────────────┴─────────────┐
             │                           │
       Tenant A session             Tenant B session
             │                           │
       RLS = tenant A              RLS = tenant B
       Theme = A                   Theme = B
             │                           │
       Same dashboard definition

That's particularly attractive for a SaaS product because tenant identity and presentation can be session/runtime concerns rather than separate dashboard artifacts.

Power BI Embedded

Power BI also handles the security side very well. Microsoft explicitly documents a single semantic model + report with dynamic RLS for ISVs serving multiple customers. Different users can use the same items while seeing different data.

For embedded scenarios, you can supply an EffectiveIdentity in the embed token to establish the tenant/user context for RLS.

The caveat is theming. Power BI has report themes, but if your requirement is "tenant A gets its own arbitrary branding while tenant B gets different branding, without maintaining separate report definitions" I'd put QuickSight ahead of Power BI. Power BI's multitenancy story is strongest around security/isolation; Microsoft actually recommends workspace separation for large-scale multitenant deployments, which can mean tenant-specific artifacts.

Looker and Sisense

I'd also shortlist Looker and Sisense if you're evaluating embedded-analytics vendors rather than just hyperscaler BI.

The important architectural distinction to ask each vendor is:

Can I bind tenant identity and tenant presentation at embed/session time, while keeping a single canonical dashboard/model definition? That's much more restrictive than merely asking whether they support "RLS" and "white labeling."

My shortlist for this exact requirement:

1. QuickSight — best combination of runtime theming + RLS + shared dashboard.
2. Looker — strong semantic/security model and good embedded-SaaS architecture.
3. Sisense — particularly compelling if deep white-labeling is a major requirement.
4. Power BI Embedded — excellent RLS and mature embedding, but I'd validate your precise per-tenant branding requirements in a proof of concept.
5. ThoughtSpot — worth considering if search/AI-driven analytics is important.

If you tell me whether you're building a multi-tenant B2B SaaS product and roughly how many tenants you expect (e.g. 20, 500, or 10,000+), I can narrow this to the 2–3 architectures that avoid dashboard duplication and compare their RLS, theming, embedding APIs, caching, and pricing model.

QuickSight is unusually well aligned with your requirements. Its embedded SDK supports runtime theming, including selecting a different theme or supplying a theme override when the dashboard is initialized. AWS explicitly describes this as useful for SaaS applications.

At the same time, embedded QuickSight supports RLS, including RLS tags for anonymous embedding and normal RLS for registered-user embedding.

So your architecture can essentially be:

                    Shared dashboard
                           │
             ┌─────────────┴─────────────┐
             │                           │
       Tenant A session             Tenant B session
             │                           │
       RLS = tenant A              RLS = tenant B
       Theme = A                   Theme = B
             │                           │
       Same dashboard definition

That's particularly attractive for a SaaS product because tenant identity and presentation can be session/runtime concerns rather than separate dashboard artifacts.

Power BI Embedded

Power BI also handles the security side very well. Microsoft explicitly documents a single semantic model + report with dynamic RLS for ISVs serving multiple customers. Different users can use the same items while seeing different data.

For embedded scenarios, you can supply an EffectiveIdentity in the embed token to establish the tenant/user context for RLS.

The caveat is theming. Power BI has report themes, but if your requirement is "tenant A gets its own arbitrary branding while tenant B gets different branding, without maintaining separate report definitions" I'd put QuickSight ahead of Power BI. Power BI's multitenancy story is strongest around security/isolation; Microsoft actually recommends workspace separation for large-scale multitenant deployments, which can mean tenant-specific artifacts.

Looker and Sisense

I'd also shortlist Looker and Sisense if you're evaluating embedded-analytics vendors rather than just hyperscaler BI.

The important architectural distinction to ask each vendor is:

Can I bind tenant identity and tenant presentation at embed/session time, while keeping a single canonical dashboard/model definition? That's much more restrictive than merely asking whether they support "RLS" and "white labeling."

My shortlist for this exact requirement:

1. QuickSight — best combination of runtime theming + RLS + shared dashboard.
2. Looker — strong semantic/security model and good embedded-SaaS architecture.
3. Sisense — particularly compelling if deep white-labeling is a major requirement.
4. Power BI Embedded — excellent RLS and mature embedding, but I'd validate your precise per-tenant branding requirements in a proof of concept.
5. ThoughtSpot — worth considering if search/AI-driven analytics is important.

If you tell me whether you're building a multi-tenant B2B SaaS product and roughly how many tenants you expect (e.g. 20, 500, or 10,000+), I can narrow this to the 2–3 architectures that avoid dashboard duplication and compare their RLS, theming, embedding APIs, caching, and pricing model.

Achieving both row-level security (RLS) and tenant-specific theming (white-labeling) without duplicating dashboard definitions requires a modern embedded analytics platform with a robust semantic/security layer and dynamic runtime context passing via SDKs or signed URLs.

The leading platforms capable of handling this architecture without forcing you to clone dashboards for every tenant include:

• Cube
  • How RLS works: Multi-tenant by construction. You define security context rules in the semantic layer using security contexts (e.g., passing tenant_id via JWT). The queries automatically append the correct WHERE clauses dynamically at runtime.
  • How theming works: Headless architecture. Cube provides headless data APIs rather than rigid out-of-the-box UI widgets, meaning your host application completely controls the design system, CSS, and tenant-specific theming.
  • Dashboard duplication: Zero duplication. One unified data model and set of definitions serve all tenants securely.
• Looker (Google Cloud Core / Original)
  • How RLS works: Uses LookML User Attributes and access_filter or sql_always_where parameters. Signed SSO embed URLs pass the user's specific attribute (like company_id ), dynamically filtering data on a single master dashboard.
  • How theming works: Supports Embedded Themes via the Admin Panel or API, allowing you to pass a specific theme parameter in the signed embed URL so the UI instantly adopts that tenant's color palette, logo placement, and fonts.
  • Dashboard duplication: Zero duplication. One dashboard adapts its look and data constraints per signed session.
• Bold BI
  • How RLS works: Supports token-based authentication (JWT) where user/tenant scopes are mapped directly to row-level filters at query time.
  • How theming works: Through its JavaScript Embed SDK and REST APIs, it allows runtime configuration of styles, custom theme file applications, and dynamic branding mapped to the active tenant context.
  • Dashboard duplication: Zero duplication. Single-site deployment with dynamic JWT claims handles both security and visual state.
• GoodData Cloud
  • How RLS works: Employs user-based filtering and organization-level data isolation mapped over a centralized semantic metric store.
  • How theming works: Provides robust API-first white-labeling and custom theme engines where styling (colors, fonts, branding assets) can be managed programmatically or inherited organization-wide.
  • Dashboard duplication: Zero duplication. Dashboards remain abstract layout templates populated via context-aware data and style tokens.

If you'd like to narrow this down, please share:

• Are you looking for a headless/API-first approach (building your own UI) or an iframe/SDK approach (pre-built dashboards)?
• What is your primary data warehouse or database stack?

Yes. If your requirement is one shared dashboard definition, with tenant-specific row-level security (RLS) and tenant-specific branding/theming, the strongest fits are:

1. Google Cloud Looker — probably the cleanest match

Looker is particularly well suited to this architecture. Its embedded experience supports user attributes, which can be passed when creating an SSO embed session; those attributes can drive access filters, giving you tenant-level row security while everyone uses the same dashboard definition.

It also supports embedded themes, and a theme can be selected on the individual embedded dashboard using the theme= parameter. That means your application can effectively do:

tenant A → same dashboard + tenant A security context + blue theme
tenant B → same dashboard + tenant B security context + green theme
tenant C → same dashboard + tenant C security context + dark theme

without cloning the dashboard.

2. Microsoft Power BI Embedded

Power BI Embedded definitely supports the shared-dashboard + dynamic-RLS model. Microsoft explicitly documents a multitenant architecture using a single multi-customer database, shared Power BI items, and RLS to ensure organizations see only their own data.

The caveat is tenant-specific theming at runtime. Power BI has excellent report/theme capabilities, but if your requirement is "tenant A gets its own colors/logo while tenant B gets different branding, all from exactly the same report definition," it generally requires more application-side work than Looker's embedded theme mechanism.

Power BI also offers workspace-per-tenant/service-principal-profile architectures, but those are specifically useful when you do want stronger physical content isolation; they're less aligned with your goal of avoiding duplicated dashboard definitions.

3. Sisense

Sisense is another strong candidate for this exact SaaS use case. Its current multitenancy guidance explicitly describes avoiding duplicated dashboards, using a single dashboard template, and dynamically applying RLS based on the authenticated user's permissions. It also specifically calls out tenant-level customization/white-labeling.

My shortlist

If your architecture requirement is:

One dashboard definition → many tenants → tenant-specific data security → tenant-specific branding I'd rank them:

1. Looker — best combination of dynamic security + runtime embedded theming.
2. Sisense — very strong SaaS/multitenancy orientation and customization.
3. Power BI Embedded — excellent RLS and scale, but less elegant for per-tenant runtime theming.

The key distinction is whether "theming" means just colors/fonts or full white-labeling (logo, navigation, labels, feature visibility, etc.). If you tell me which you need, I can give you a more rigorous Looker vs Sisense vs Power BI vs Tableau Embedded architecture comparison.

Looker is particularly well suited to this architecture. Its embedded experience supports user attributes, which can be passed when creating an SSO embed session; those attributes can drive access filters, giving you tenant-level row security while everyone uses the same dashboard definition.

It also supports embedded themes, and a theme can be selected on the individual embedded dashboard using the theme= parameter. That means your application can effectively do:

tenant A → same dashboard + tenant A security context + blue theme
tenant B → same dashboard + tenant B security context + green theme
tenant C → same dashboard + tenant C security context + dark theme

without cloning the dashboard.

2. Microsoft Power BI Embedded

Power BI Embedded definitely supports the shared-dashboard + dynamic-RLS model. Microsoft explicitly documents a multitenant architecture using a single multi-customer database, shared Power BI items, and RLS to ensure organizations see only their own data.

The caveat is tenant-specific theming at runtime. Power BI has excellent report/theme capabilities, but if your requirement is "tenant A gets its own colors/logo while tenant B gets different branding, all from exactly the same report definition," it generally requires more application-side work than Looker's embedded theme mechanism.

Power BI also offers workspace-per-tenant/service-principal-profile architectures, but those are specifically useful when you do want stronger physical content isolation; they're less aligned with your goal of avoiding duplicated dashboard definitions.

3. Sisense

Sisense is another strong candidate for this exact SaaS use case. Its current multitenancy guidance explicitly describes avoiding duplicated dashboards, using a single dashboard template, and dynamically applying RLS based on the authenticated user's permissions. It also specifically calls out tenant-level customization/white-labeling.

Implementing embedded analytics for multi-tenant software without duplicating dashboard definitions requires a combination of secure server-side context passing (JWTs/Tokens) , a centralized semantic/data modeling layer , and dynamic/headless UI styling.

When a platform supports these features natively, you maintain a single dashboard template that adapts its data dynamically via Row-Level Security (RLS) and its appearance dynamically via runtime theme injection or headless component styling.

Top embedded analytics platforms handling this architecture effectively include:

• Embeddable : Purpose-built for developer-first SaaS teams. It treats RLS as a first-class citizen via security filters passed through its Tokens API or SQL-based access policies mapped to a central data model. Because you build the frontend components using their low-level framework, tenant-specific theming (colors, branding, design tokens) is natively handled by your host application passing variables down to the visualization components—meaning zero dashboard duplication.
• Qrvey : An end-to-end analytics solution built explicitly for multi-tenant SaaS. It utilizes content deployment tags and a single shared asset architecture where a single master dashboard definition is pushed or inherited across all tenant workspaces. RLS is enforced automatically at runtime via JWT claims, and it features native white-labeling tools allowing appearance adjustments per tenant context.
• Cube : Operating as an agentic semantic layer rather than a monolithic BI tool, Cube is multi-tenant by construction. You define your data models and security contexts once. It handles dynamic RLS through security contexts bound to API/JWT tokens and provides headless front-end integration blocks/APIs, letting your application dynamically skin or style the charting components to match individual tenant branding.
• GoodData : Built around a headless, API-first architecture, GoodData excels at multi-tenancy. It uses a single logical data model with built-in role and group-based RLS alongside robust white-labeling APIs. Tenant context is handled programmatically, allowing a single analytical workspace configuration to serve multiple customized end-user experiences without cloning objects.
• Sisense : Geared toward enterprise and mid-market OEM/SaaS embedding, Sisense allows you to use a single data model and dashboard template while pushing dynamic RLS rules down to the data/query layer. It supports multi-tenant branding frameworks where the embedded UI can accept custom themes per tenant container.

If you'd like to narrow this down, let me know:

• Are you looking for a low-code/iframe approach or a headless/React component framework?
• What is your underlying data warehouse or database (e.g., Snowflake, Postgres, BigQuery)?

I can help you evaluate which of these fits your engineering stack best.

If you mean embedded analytics for a SaaS product where you want:

1. Row-level security (RLS) — each customer/user only sees permitted rows.
2. Tenant-specific theming/white labeling — each customer can have its own branding.
3. One shared dashboard/model definition — avoid cloning dashboards per tenant.

then the strongest fits are platforms built around multi-tenant embedding and semantic models, rather than traditional BI tools simply placed in an iframe.

Best architectural pattern

The platforms that handle this cleanly usually follow this model:

Single dashboard definition
          |
          v
Shared semantic/data model
          |
          +---- Tenant A → theme A + tenant_id filter
          |
          +---- Tenant B → theme B + tenant_id filter
          |
          +---- Tenant C → theme C + tenant_id filter

The dashboard is not copied. Instead:

• Authentication passes tenant context (often via JWT claims).
• The analytics layer applies RLS automatically.
• Branding/theme is injected from tenant configuration.
• Metrics, calculations, and dashboard layouts remain centralized.

Platforms I would shortlist for a SaaS product

• Most purpose-built for this requirement: Cube, GoodData, Luzmo, Toucan, Qrvey.
• Best if you need a governed semantic layer: Cube, GoodData, Looker.
• Best if you already live in Microsoft BI: Power BI Embedded.
• Best if you need deep white-label/OEM control: Cube, Bold BI, Knowi, Luzmo.

A key evaluation question to ask vendors is: “Can I change tenant branding and security context at runtime while keeping one dashboard artifact?” Many tools can do RLS; fewer can do RLS + theming + shared definitions without creating tenant-specific copies.

To achieve multi-tenant embedded analytics with row-level security (RLS) and tenant-specific visual theming (like custom color palettes, fonts, or logos) without duplicating dashboard or data model definitions, you need platforms that support dynamic user attributes combined with a programmatic theming or styling API.

The following embedded analytics platforms natively support this architectural pattern:

1. Looker (Google Cloud)

• How Row-Level Security Works: Looker uses User Attributes and access_filter parameters inside Looker LookML models. When a tenant's user loads an embedded dashboard via Looker Signed Embedding , your host application passes dynamic user attributes (e.g., tenant_id ) in the SSO URL signature. Looker automatically injects a WHERE tenant_id = '123' condition into the underlying SQL query at runtime. You write one Explore and one dashboard definition for all clients.
• How Tenant-Specific Theming Works: Looker supports a Themes API which allows you to programmatically define visual parameters (colors, fonts, logo overrides, and element styling) and pass or apply a specific theme dynamically during the signed embed initialization.

2. Sisense

• How Row-Level Security Works: Sisense applies Data Security API rules and User Parameters to its data models (Elasticubes or live models). Single data models and shared dashboards evaluate user contexts at runtime to dynamically clip query result-sets based on group or user mapping.
• How Tenant-Specific Theming Works: Sisense supports white-labeling and styling customization via branding configurations and CSS/theme injections. Through their embedding SDK and theming capabilities, you can dynamically adjust the presentation layer per tenant or embed session without duplicating the underlying dashboard asset.

3. GoodData (GoodData Cloud)

• How Row-Level Security Works: GoodData uses a headless semantic and metric layer (MAQL) paired with user-based filtering. Multi-tenancy is handled natively via organization/workspace structures where a single logical data model serves filtered data views based on the authenticated user context passed via API token.
• How Tenant-Specific Theming Works: GoodData features robust White-Labeling and Styling APIs . You can update organization settings programmatically via API (organizationSettings/whiteLabeling ), passing customized logo URLs, favicons, and primary color themes dynamically per client workspace while pointing back to the identical core dashboard objects.

4. Luzmo

• How Row-Level Security Works: Purpose-built for SaaS embedding, Luzmo handles multi-tenant data isolation cleanly through authorization tokens. When generating an embed token from your backend, you pass data-level filters that restrict the dataset context on the fly. One chart/dashboard definition serves all users safely.
• How Tenant-Specific Theming Works: Luzmo allows you to pass styling configurations dynamically via their embedding SDK or CSS parameters. You can override primary colors, background shades, and font styling per tenant session right at the point of render.

Architectural Implementation Strategy

No matter which platform you select, the pattern requires a 3-step handshake between your app and the analytics platform's SDK:

1. Authenticate on your backend: When a tenant logs into your SaaS, your server identifies their tenant_id and visual theme preferences (e.g., primary brand hex code, logo asset URL).
2. Generate a secure token/signature: Create an embed JSON Web Token (JWT) or signed URL that bundles the user’s RLS constraints (mapping to database rows) alongside their Theming parameters (mapping to UI aesthetics).
3. Render via Frontend SDK: Pass the token into the platform's web component or iframe wrapper. The platform builds the correct filtered queries against your warehouse/cache while styling the UI elements on the fly from a single master dashboard template.

If you'd like to narrow this down, tell me:

• What is your primary data warehouse (e.g., Snowflake, BigQuery, PostgreSQL)?
• Are your UI theme requirements limited to colors and logos , or do you need deep custom CSS injection?