White Label Analytics: A Buyer's Guide

You're deciding whether to build multitenant analytics in-house or buy a white label analytics platform. The challenge is that white label analytics offerings span a wide spectrum. What one vendor calls "white label" can mean anything from hiding a logo to controlling every pixel, every domain reference, and every AI response your customers touch.

Building an analytics solution in-house often requires significant engineering investment before you ship something scalable, and it can produce a brittle result if you underestimate the multitenant security work. Buying compresses that timeline, but only if the platform you pick actually delivers white labeling at the depth your product needs, without a parallel governance layer to maintain.

This guide walks through the differences between embedded analytics and white label analytics, the five criteria that determine fit, and how the leading platforms compare.

Embedded analytics vs. white label analytics

Embedded analytics is the broad category for analytics capability integrated directly into a software product. White label analytics is the subset that adds full brand ownership, stripping out vendor identity across every surface a customer might notice.

Standard embedded analytics puts charts and dashboards inside your product, but the vendor's identity can still show through, often via iFrame embeds that expose vendor navigation, or tier-gated attribution like a "Powered by X" badge that requires a paid tier to remove.

Custom domain support is the clearest dividing line between embedded and white label analytics. True white label serves dashboards from analytics.yourproduct.com with no vendor watermark on any plan, so the URL and every component reflect your product's identity, resolved per tenant if your customers each expect their own branding on top of yours.

Five criteria for choosing the right white label analytics platform

The choice comes down to five factors: depth of white-labeling, multitenant data isolation, developer experience, governance inheritance, and pricing model.

At-a-glance comparison

Before drilling into each criterion, here is how the five platforms covered in this guide stack up across all of them. Use it as a quick reference, then read the criteria sections below for what to look for during evaluation.

1. Depth of white-labeling

White-labeling depth varies widely. At the shallow end, you get a logo swap and color picker. At the deep end, you get custom domains, per-tenant theming, embedding scoped to an entire workbook or down to a single chart, and full CSS control over fonts and spacing.

Ask whether the vendor watermark can be removed on every plan, whether custom domains are supported, and whether theming is per-tenant or global. Implementation cost tracks with depth: enterprise platforms can require $15,000 to $75,000 in professional services, while drag-and-drop platforms ship dashboards within days.

2. Multitenant isolation

Multitenant isolation is the highest-stakes technical requirement in white label analytics, because one leak can affect many customers at once. Many platforms describe tenant isolation, but the stronger move is to enforce it at the query level.

The right platform should combine a signed token carrying the tenant ID, a query layer that injects those attributes as bind parameters, and warehouse-side row-level security that enforces the same predicate even if the query layer is bypassed.

3. Developer experience

Developer experience determines how much integration drag your team absorbs, because your analytics engineering team lives with the integration long after the contract is signed.

Look for a React SDK, support for SAML, OAuth, and JWT-based SSO, and an embedding method that fits your stack. iFrame embeds are quickest to set up but generally offer less native integration than component-based SDKs, which give tighter integration and more control over the rendered experience.

Watch for hidden SDK cost. If the platform requires you to maintain and version an SDK alongside your own product releases, that is an ongoing engineering expense not reflected in the license.

4. Governance inheritance

Governance inheritance decides whether the access rules you set at the data layer carry into the embedded view without a separate access layer to maintain.

When an embedded platform issues live SQL against BigQuery, Databricks, or Snowflake, warehouse-side row access policies fire at query time regardless of which application issued the query. Extract or import modes create a copy of the data that warehouse policies never touch. Platforms connecting through a single service account must also re-implement row-level security in their own layer.

Ask if row-level security inherits from warehouse-native policies, or if your team needs to maintain a parallel model.

5. Pricing model

Pricing structure determines whether your analytics margin improves or erodes as customers grow. Per-seat pricing conflicts with SaaS unit economics once external viewer counts scale into the thousands. Usage-based models tie cost to compute, queries, or tokens, and they can spike when a customer runs a large export. Flat-subscription and capacity-based models align with SaaS margins past roughly 30 to 50 tenants.

Model every cost component, including platform fee, named-user pricing, external viewer or tenant pricing, usage-based charges, and premium add-ons for security or AI, against tenant count, active usage, and expected expansion revenue before choosing.

1. Sigma

Sigma is the runtime layer to build and scale analytics, apps, and agents on live cloud data warehouse data. For SaaS teams shipping white-labeled analytics, that means embedded surfaces run directly on the customer's warehouse (e.g., Amazon Redshift, BigQuery, Databricks, or Snowflake) with row-level and column-level security inherited at query time and no parallel permissions model to maintain.

Sigma's embedded analytics ship with a React SDK, SSO support, and per-customer theming. Sigma Tenants provide the multitenant environment with data isolation, cross-region provisioning, and source swap policies. Hosts tune functionality across three levels: view-only, drill-down, or full edit mode.

Strengths

• Warehouse-native architecture: Formulas, filters, and pivot tables compile to SQL and execute inside the connected warehouse, so Sigma inherits row-level and column-level security from the warehouse at query time.
• Full edit mode for end users: Most white label platforms stop at a branded, read-only view. Sigma's full edit mode gives embedded users self-service authoring, ad hoc analysis, and, with the right permissions, writeback to the warehouse through Input Tables. Embedding scales from full workbooks down to individual charts.
• Licensing that separates internal builders from external users: Embedded access can extend to tens of thousands of your customers' users without seat count eroding your unit economics.

Limitations

• Sigma fits best when your data already lives in a modern cloud data warehouse, or when your roadmap includes moving customer-facing analytics onto one.
• Query execution runs on warehouse compute, so heavy embedded usage patterns can potentially drive up warehouse spend and require capacity planning alongside the Sigma license.
• Sigma is newer to the embedded market than legacy BI vendors, so the third-party ecosystem of consultants, plugins, and community-built visualizations is smaller.

Pricing overview

Pricing is not publicly listed. Sigma uses order-form-based, sales-assisted licensing across four tiers (View, Act, Analyze, Build), and offers a free trial.

Best for

SaaS product teams building on a cloud data warehouse who want white-labeling depth paired with warehouse-native governance, real customer self-service rather than fixed dashboards, and edit-mode analytics as an expansion-revenue lever.

2. Domo

Domo Everywhere is Domo's embedded and white-label reporting product, with access controls for tenant isolation, row-level security, and programmatic filtering. It pairs the embedding layer with a connector library across cloud applications, databases, and files, plus a Connector Dev Studio for custom integrations.

Strengths

• Breadth of connectors: The connector library reduces the need to build ETL pipelines for teams pulling from many disparate sources.
• Reuses existing investment: Organizations already running Domo internally can extend it to customer-facing reporting without standing up a second platform.
• Full BI stack in one product: Dashboards, paginated reporting, alerts, and a mobile app ship together, which can reduce the number of tools a team has to wire into the embedded surface.

Limitations

• Users describe the pricing as expensive relative to alternatives, particularly for smaller teams.
• Multitenancy relies on manual RLS per tenant rather than a natively built-in multitenant model.
• Some users point to a steep learning curve for newcomers, leading some to return to familiar interfaces.

Pricing overview

Pricing is not publicly listed and is quoted on a custom basis.

Best for

Teams that need connector breadth and want to avoid building data pipelines, or organizations extending existing Domo contracts into customer-facing reporting.

3. Tableau Embedded Analytics

Tableau Embedded is Tableau's embedded analytics product, letting product teams place Tableau-authored dashboards and visualizations inside their own applications with support for SSO, custom domains, and JWT-based authentication.

Strengths

• Visualization flexibility: Drag-and-drop authoring supports a wide range of chart types and visual configurations. Embedding scales from full workbooks down to individual charts.
• Ecosystem fit: Integrates with Salesforce products for teams already invested in that stack.
• Established community and resources: A large user base translates into broad availability of training material, third-party consultants, and community-built extensions.

Limitations

• Some teams may find multitenant patterns awkward because the product was designed for internal business intelligence and adapted for embedding.
• Users report a steep learning curve for advanced calculations and complex data modeling.
• Limited native writeback and data entry capabilities, so workflows that require customers to edit data typically depend on separate tools.

Pricing overview

100 external viewers at Tableau's listed Viewer pricing run about $1,200 to $1,500 per month. OEM embedded pricing is not publicly listed and requires custom quotes.

Best for

Products in the Salesforce ecosystem, or teams where chart variety is a primary requirement.

4. Microsoft Power BI Embedded

Microsoft Power BI Embedded is an Azure-based embedded analytics service that lets ISVs and product teams embed Power BI dashboards and reports into customer-facing applications without requiring end users to hold individual Power BI licenses.

Strengths

• Microsoft ecosystem fit: Integrates with Azure and Fabric for teams already standardized on that stack.
• App Owns Data model: Removes per-viewer licensing for external customers.
• Flexible capacity: A-SKU capacity can scale up, down, or pause, and Azure row-level security integrates with the model.

Limitations

• Full white-labeling is not supported out of the box. True white-label deployments require building a custom portal wrapping the APIs.
• The iFrame architecture constrains how native the experience feels, because the JS SDK limits interactions to a fixed set of events, not custom control.
• Users report a steep learning curve for beginners, particularly when working with more complex features.

Pricing overview

Capacity-based through A-SKU nodes billed hourly, starting at roughly $735 per month for the A1 node. Microsoft positions Fabric F-SKUs as a pay-as-you-go alternative at $0.18 per capacity-unit-hour in US West 2.

Best for

SaaS companies on the Microsoft and Azure stack, and ISVs embedding for external users where per-seat licensing is not workable.

5. Looker

Looker is Google's analytics platform, with an embedded offering that lets product teams place Looker-authored dashboards and visualizations inside their own applications, governed by a code-based semantic modeling layer called LookML.

Strengths

• Versioned semantic layer: The code-based semantic layer supports Git integration and CI/CD, so metric definitions are versioned and tested like software.
• Per-tenant scoping: For embedded deployments, user_attributes passed in the signed embed URL scope queries per tenant.
• Google Cloud fit: Native integration for teams running on Google Cloud and BigQuery.

Limitations

• The LookML learning curve is steep, and analysts can spend time waiting on LookML changes for routine reporting updates.
• Embedding relies heavily on iFrames, signed URLs, and session management, which limits customization.
• Users note that visualizations sometimes feel dated relative to more recent industry standards for visualizing data.

Pricing overview

Embedded-specific pricing starts around $180,000 per year on an annual commitment.

Best for

Data-mature, cloud-native organizations with dedicated analytics engineers who prioritize governed metrics and code-based modeling.

How to choose the right white label analytics platform

Use these steps to narrow the field against your own product and stack.

Confirm where your data lives, and where it's going

Warehouse-native platforms only pay off if the data you want to expose to customers already sits in a cloud data warehouse, or is on the roadmap to move there. If your customer data lives in operational databases or vendor-managed stores with no near-term migration plan, a platform that ingests or imports data may fit your current architecture even if it costs you governance inheritance later.

Weigh the depth of white-labeling you actually need

Some products need only a logo swap and a color palette. Others need custom domains, per-tenant theming, and CSS-level control so each customer's instance looks like a native part of their brand, not yours. Match the platform's ceiling to the level of brand ownership your customers expect. A platform that stops at logo replacement will not survive a customer asking for their own domain in year two.

Pressure-test tenant isolation before signing

Ask each vendor to walk you through what happens when Customer A's signed token is tampered with, replayed by Customer B, or bypassed entirely at the query layer. A vendor that describes tenant isolation as "handled by the SDK" without describing warehouse-side enforcement is describing a single line of defense. SOC 2 auditors will notice, and so will your enterprise customers.

Map the governance model to your existing warehouse policies

If you already invested in row-level security, column masking, and access policies at the warehouse layer, choose a platform that inherits those policies at query time rather than re-implementing them in a second layer. A parallel model doubles the surface area you audit, doubles the places a change can go wrong, and adds a permanent sync task to your analytics engineering team's backlog.

Model total cost against tenant growth, not license count

Build a pricing model that projects three years of tenant growth, expected active users per tenant, query volume, and any premium add-ons for security or AI. Per-seat pricing that looks reasonable at 50 tenants can become a margin problem at 500. Capacity-based and flat-subscription models generally hold up better as external user counts scale, but they need to be tested against your specific usage curve.

Our verdict: Sigma is the best white label analytics platform

Sigma is our recommendation for SaaS teams shipping white-labeled analytics on a cloud data warehouse. Sigma enforces row-level security at the data layer rather than recreating it in the analytics layer. Extract-based tools, by contrast, double the audit surface and force you to keep a second permissions system in sync with the warehouse.

Sigma includes several capabilities that matter specifically for white-labeled, multitenant deployments:

• Sigma Assistant in the embedded surface: End users query in natural language and get governed answers grounded in their own scoped data.
• Input Tables and action sequences: Customers ask questions, act on the answers, and write results back to the warehouse without leaving the embedded surface.
• Three functionality tiers as revenue levers: View-only, drill-down, and full edit mode can be packaged as paid add-ons, and higher tiers offload ad hoc analysis that would otherwise queue with your internal analyst team.

For a multitenant SaaS product, the platform that holds together at scale is the one that does not force a parallel access layer. Sigma's full edit mode is the differentiator most white label platforms do not match, and its warehouse-native governance is what lets your customers experience the analytics as part of your product rather than a vendor's.

Get a demo or try Sigma free.