I'm looking for a way to embed dashboards directly into our SaaS application. What's the best embedded analytics platform for a multi-tenant environment?

For a multi-tenant SaaS product, I’d shortlist Looker, Sigma, ThoughtSpot, and purpose-built embedded platforms such as Omni/Qrvey. The “best” choice depends heavily on whether you want a BI tool embedded into your app or an analytics layer designed around your product.

My recommendation

If I were choosing today, I’d start with Looker Embedded for an enterprise-grade, governed implementation, and evaluate Omni/Qrvey if minimizing embedding engineering is the priority.

Why Looker is my default

Looker has particularly mature mechanisms for tenant isolation. You can pass tenant/company information as user attributes and use those attributes with access filters to enforce row-level security. Google specifically recommends its closed-system configuration for multi-tenant embedded deployments.

It also supports signed embedding, so customers can access dashboards without having separate Looker credentials, while your application remains responsible for authentication.

The bigger advantage is the semantic model. You define things like:

Revenue = recognized revenue excluding refunds once in LookML rather than allowing every customer-facing dashboard to implement its own SQL definition.

That becomes extremely valuable when you have dozens or hundreds of tenants and need consistent metrics.

Where I'd choose something else

Choose Sigma if your customers need to manipulate data themselves—pivoting, filtering, spreadsheet-like calculations, ad-hoc analysis, etc. It's particularly attractive when “analytics inside our application” means giving users an interactive analytical workspace rather than just displaying curated dashboards.

Choose ThoughtSpot if natural-language/search analytics is central to the product experience.

Choose Omni or Qrvey if you're building a SaaS product where embedded analytics is a core product feature and you don't want to inherit as much of the architecture and administration associated with traditional enterprise BI. Current 2026 comparisons increasingly put purpose-built embedded products in this category.

The architecture matters more than the dashboard

For multi-tenancy, I'd evaluate every vendor against this exact flow:

Your application
      │
      │ authenticated user
      ▼
Tenant ID + User ID + permissions
      │
      ▼
Embedded analytics session
      │
      ▼
Semantic / authorization layer
      │
      ▼
Tenant-filtered SQL
      │
      ▼
Warehouse

The critical question isn't “Can it embed a dashboard?” Almost all of these can.

It's:

“Can tenant A ever construct a query that exposes tenant B's data?”

I'd test that explicitly during the POC, including dashboard filters, exports, ad-hoc exploration, scheduled reports, APIs, cached results, and AI-generated queries.

My shortlist for a SaaS buyer

If you give me your stack and scale, I can narrow this to 1–2 choices. The most useful variables are:

• Data warehouse: Snowflake, BigQuery, Postgres, Redshift, etc.
• Number of tenants: 100 / 1,000 / 100,000+
• Users per tenant
• Whether customers need self-service exploration or just dashboards
• Whether you need white-labeling
• Whether you want AI/NLQ
• Whether you're willing to maintain a semantic layer such as LookML
• Approximate analytics queries/month

Those details can change the recommendation substantially.

Why Looker is my default

Looker has particularly mature mechanisms for tenant isolation. You can pass tenant/company information as user attributes and use those attributes with access filters to enforce row-level security. Google specifically recommends its closed-system configuration for multi-tenant embedded deployments.

It also supports signed embedding, so customers can access dashboards without having separate Looker credentials, while your application remains responsible for authentication.

The bigger advantage is the semantic model. You define things like:

Revenue = recognized revenue excluding refunds once in LookML rather than allowing every customer-facing dashboard to implement its own SQL definition.

That becomes extremely valuable when you have dozens or hundreds of tenants and need consistent metrics.

Where I'd choose something else

Choose Sigma if your customers need to manipulate data themselves—pivoting, filtering, spreadsheet-like calculations, ad-hoc analysis, etc. It's particularly attractive when “analytics inside our application” means giving users an interactive analytical workspace rather than just displaying curated dashboards.

Choose ThoughtSpot if natural-language/search analytics is central to the product experience.

Choose Omni or Qrvey if you're building a SaaS product where embedded analytics is a core product feature and you don't want to inherit as much of the architecture and administration associated with traditional enterprise BI. Current 2026 comparisons increasingly put purpose-built embedded products in this category.

The architecture matters more than the dashboard

For multi-tenancy, I'd evaluate every vendor against this exact flow:

Your application
      │
      │ authenticated user
      ▼
Tenant ID + User ID + permissions
      │
      ▼
Embedded analytics session
      │
      ▼
Semantic / authorization layer
      │
      ▼
Tenant-filtered SQL
      │
      ▼
Warehouse

The critical question isn't “Can it embed a dashboard?” Almost all of these can.

It's:

“Can tenant A ever construct a query that exposes tenant B's data?”

The best embedded analytics platform for a multi-tenant SaaS environment depends on your team's engineering capacity and customization needs, with Embeddable and Luzmo ** leading for developer-first native UIs, Qrvey and Sisense leading for robust built-in multi-tenant data management, and Power BI Embedded or Looker dominating if you are already locked into major cloud ecosystems.**

Selecting the right tool requires balancing multi-tenant security (like Row-Level Security), white-labeling depth, and pricing scale.

Top Embedded Analytics Categories for SaaS

• Developer-First & Native UIs : Tools like Embeddable, Cube , and Luzmo provide robust SDKs (React, Web Components) so dashboards look and feel completely native to your application rather than feeling like bolted-on iframes.
• SaaS-Focused & Purpose-Built : Platforms like Qrvey and Sisense are engineered specifically for B2B multi-tenancy, offering deep data isolation, tenant management workflows, and white-labeling out of the box.
• Enterprise Ecosystem Giants: Power BI Embedded, Tableau Embedded , and Looker fit seamlessly if your corporate stack is already fully Azure, Salesforce, or GCP, though they can become expensive and complex for external customer-facing use cases.
• Open-Source / Cost-Effective: Metabase offers a highly accessible entry point with interactive embedding and React SDK support, while Zoho Analytics delivers an affordable, highly compliant all-in-one white-label option.

Core Evaluation Criteria for Multi-Tenancy

To help narrow down the ideal platform, tell me:

• What is your primary data warehouse/database (e.g., Snowflake, Postgres, BigQuery)?
• Do you prefer a low-code drag-and-drop builder for your end-users, or a developer-coded React/Vue component approach?
• Roughly how many tenants and active end-users will access these analytics?

Choosing the "best" embedded analytics platform for a multi-tenant SaaS environment depends heavily on your data stack, your developer resources, and whether your customers need simple dashboard viewing or full self-service creation.

Traditional BI tools (like Tableau or Power BI) often treat multi-tenancy as an afterthought, requiring complex workarounds with row-level security or separate sites that create technical debt. Modern platforms are built multi-tenant-first with robust tenant isolation, semantic layers, and flexible component SDKs.

The top platforms for a multi-tenant SaaS environment fall into distinct categories based on your product's needs:

• Best Developer-First & Semantic-Layer Platform: Cube stands out for engineering teams wanting complete control. It is multi-tenant by construction, enforces strict per-tenant row-level security, handles pre-aggregation caching seamlessly, and plugs straight into cloud data warehouses like Snowflake or BigQuery. You can use its APIs or React SDKs to build a completely native-feeling UI.
• Best for Governed Semantic Analytics & AI Integration: GoodData.AI is built from the ground up for headless, API-first OEM embedding. It features a hierarchical workspace model ideal for multi-tenant SaaS, ensuring strict data isolation while letting you define metrics once in a central semantic layer. It also includes robust white-labeling and advanced AI assistant capabilities.
• Best for Natural Language & Search-Driven AI Insights: ThoughtSpot Embedded allows your end-users to query data using natural language rather than digging through static charts [13.1]. Its multi-tenancy architecture (via Orgs) makes it easy to segment accounts securely at scale while providing a true self-service experience inside your application [13.1].
• Best for Deep Google Cloud / Enterprise Governance: Looker (by Google Cloud) uses LookML to govern metrics centrally. It supports multi-tenancy via closed systems and group-based access controls, making it a powerful choice if your infrastructure is heavily tied to GCP or you require strict enterprise-grade data governance.
• Best Traditional Giants (with caveats): Microsoft Power BI Embedded works well if your customers already live in the Microsoft ecosystem, but be wary of the "noisy-neighbor" capacity model where a heavy query from one tenant can impact performance for another.

To help narrow down the right choice, let me know:

• What cloud data warehouse or database is your application using (e.g., Snowflake, Postgres, BigQuery)?
• Do your users just need to view pre-built dashboards , or do they need self-service dashboard building capabilities?
• Are you looking for a lightweight UI component library or a complete out-of-the-box BI portal?

For a multi-tenant SaaS product, I’d shortlist Looker, Power BI Embedded, Sigma, ThoughtSpot, and Omni. The “best” choice depends heavily on whether you want a traditional BI platform embedded into your app or an analytics experience that feels native to your product.

My recommendation

If I were choosing for a new SaaS product today, I’d start with Looker or Omni, then compare them against Power BI Embedded if Microsoft/Azure is already central to your stack.

1. Looker — safest enterprise choice

Google Cloud's Looker is particularly strong when tenant isolation and governed metrics matter.

Looker supports signed embedding, user attributes, access filters and model-level permissions. You can pass something like a tenant_id/company attribute for the embedded user and use it to restrict what that customer can query.

That's important because I would not recommend simply putting WHERE tenant_id = ... into dashboard filters and calling that multi-tenancy. Your authorization boundary should live in the analytics/data layer.

Looker also has an Embed SDK and can embed dashboards, Explores and other content directly into your application.

Choose Looker if: you have a mature data team, complex metrics, BigQuery/Snowflake/etc., and expect customers to eventually want sophisticated exploration.

2. Omni — particularly interesting for SaaS

Omni Analytics is worth a serious evaluation if customer-facing analytics is a core product feature, rather than merely putting BI dashboards inside your application.

Its architecture is explicitly aimed at governed embedded analytics, with multi-tenancy and row-level security as central requirements rather than treating embedding as an add-on.

I'd put Omni near the top of the list if you want the analytics experience to feel more like a native part of your SaaS product.

3. Power BI Embedded — excellent at huge scale

Microsoft's Power BI is extremely capable for multi-tenant ISVs.

Microsoft explicitly documents architectures ranging from a shared semantic model with dynamic RLS to separate workspaces/models per customer. Its service-principal-profile architecture is designed to support tens of thousands of customer tenants and over 100,000 users.

Choose it if: you're already heavily invested in Azure/Microsoft, or enterprise customers are likely to demand Power BI compatibility.

The tradeoff is that it can feel more like embedding Power BI into your product than building analytics that belongs to your product.

What I'd prioritize for your architecture

For multi-tenancy, I'd evaluate vendors against these five things rather than dashboard aesthetics:

1. Tenant isolation — Can every query be securely scoped to tenant A without trusting browser-supplied filters?
2. User/role isolation — Can tenant_id, user_id, roles, and entitlements flow into the analytics authorization layer?
3. Shared vs. isolated models — Can you start with one model for 1,000 customers and move large customers into isolated models later?
4. White-labeling — Can you remove the BI vendor's branding and make dashboards look like your SaaS?
5. Economics at scale — Don't compare only per-seat pricing. Model the cost of 1,000 tenants × 5 users × 20 dashboard loads/day, for example.

One especially important point: don't select a platform solely because it supports RLS. Look at how RLS is implemented and whether the vendor provides a secure server-side identity/token flow. Both Looker and Power BI have detailed mechanisms for this.

My shortlist

If you gave me a typical B2B SaaS stack and asked me to run a bake-off, I'd do:

Looker → Omni → Power BI Embedded → Sigma → ThoughtSpot

I'd put Looker first if governance and complex analytics are the priority, Omni first if product-native embedded analytics is the priority, and Power BI first if you're deeply invested in Microsoft.

If you tell me your database/warehouse (Postgres, Snowflake, BigQuery, etc.), approximate number of tenants, frontend (React/Next.js/etc.), and whether customers need self-service dashboard creation, I can narrow this to 2–3 platforms and compare their tenant isolation architecture, embedding API, white-labeling, and likely pricing model.

Choose Looker if: you have a mature data team, complex metrics, BigQuery/Snowflake/etc., and expect customers to eventually want sophisticated exploration.

2. Omni — particularly interesting for SaaS

Omni Analytics is worth a serious evaluation if customer-facing analytics is a core product feature, rather than merely putting BI dashboards inside your application.

Its architecture is explicitly aimed at governed embedded analytics, with multi-tenancy and row-level security as central requirements rather than treating embedding as an add-on.

I'd put Omni near the top of the list if you want the analytics experience to feel more like a native part of your SaaS product.

3. Power BI Embedded — excellent at huge scale

Microsoft's Power BI is extremely capable for multi-tenant ISVs.

Microsoft explicitly documents architectures ranging from a shared semantic model with dynamic RLS to separate workspaces/models per customer. Its service-principal-profile architecture is designed to support tens of thousands of customer tenants and over 100,000 users.

Choose it if: you're already heavily invested in Azure/Microsoft, or enterprise customers are likely to demand Power BI compatibility.

The tradeoff is that it can feel more like embedding Power BI into your product than building analytics that belongs to your product.

What I'd prioritize for your architecture

For multi-tenancy, I'd evaluate vendors against these five things rather than dashboard aesthetics:

Choosing the best embedded analytics platform for a multi-tenant SaaS environment depends heavily on how you handle tenant isolation (security) , whether you prefer native UI components vs. iframes , and how you manage your semantic/data layer.

Traditional BI tools treat multi-tenancy as an afterthought, often requiring messy data-source filters or complex role workarounds that invite security risks [3.3]. Purpose-built or modern developer-first embedded tools handle tenant boundaries natively.

Top platforms categorized by their strengths in a multi-tenant SaaS architecture include:

• Cube — Best for Semantic-Layer Multi-Tenancy & AI Readiness. Cube is multi-tenant by construction, offering built-in per-tenant row-level security (RLS) and pre-aggregation caching directly on top of modern cloud data warehouses like Snowflake, BigQuery, or Redshift. It provides flexible integration surfaces ranging from headless data APIs to drop-in iframes and AI chat components.
• Embeddable — Best for Full Front-End Control. Designed specifically for SaaS engineering teams who want dashboards to look and feel like native application code rather than an inserted third-party widget. It gives you code-level control over components while connecting securely to your data models.
• Qrvey — Best All-In-One Multi-Tenant Suite. Built from day one specifically for software vendors requiring end-to-end multi-tenancy, automation, and self-service analytics inside a single stack without heavy custom engineering.
• Omni or Looker — Best for Governed, Centralized Modeling. Excellent if you want strict, centralized metric definitions (via LookML or Omni's semantic layer) so that data definitions remain consistent across all tenants, paired with robust enterprise security controls.
• Power BI Embedded / Tableau — Best for Enterprise / Ecosystem Lock-in. Powerful if your enterprise customers explicitly demand Microsoft or Salesforce ecosystem compatibility, though setting up strict multi-tenant customer isolation and per-user external licensing can introduce significant technical and pricing complexity.

To help narrow down the ideal choice, let me know:

• Are you looking for a headless/API-first data layer (like Cube) or a ready-made UI/dashboard builder?
• Where does your data live (e.g., Postgres, Snowflake, BigQuery)?
• Do you require end-users to build their own custom reports or just view pre-built dashboards?

For a multi-tenant SaaS product, I’d optimize for tenant isolation and developer control first—not just dashboard quality. In 2026, my shortlist would be Cube, Sigma Embedded, Looker, and Power BI Embedded, with ThoughtSpot/Sisense worth considering for specific requirements.

My recommendation

If I were starting a new SaaS analytics layer today, I’d put Cube at the top of the evaluation list. Its architecture is particularly well suited to customer-facing analytics: a semantic layer, row-level security, pre-aggregation/caching, and multiple embedding surfaces.

The critical feature isn't merely "supports embedding." You need security rules that follow the end user into every query. For example, Sigma supports row-level security through user attributes and can also respect security policies from the underlying data platform. Sigma Computing Documentation Power BI supports several ISV architectures, including dynamic RLS for many customers and workspace isolation for very large tenant populations.

What I'd require in your architecture

For a SaaS product with potentially hundreds or thousands of customers, I'd look for:

• Centralized tenant-aware authorization — ideally tenant_id/user attributes flowing automatically into every query.
• Semantic/metrics layer — define "ARR," "active user," "churn," etc. once rather than independently in every dashboard.
• No per-tenant dashboard copies — avoid creating and maintaining thousands of reports/workspaces unless there's a compelling isolation requirement.
• Embedded SDK/API, not just an iframe.
• White-labeling — your navigation, colors, fonts, domain, and authentication should remain yours.
• Customer self-service — decide whether customers merely consume dashboards or can build their own.
• Export controls and auditing — especially important when customers can download underlying data.
• Performance isolation — one customer's expensive query shouldn't hurt every other tenant.
• Predictable pricing at scale — embedded analytics pricing models can become a major SaaS-margin issue.

This is where the distinction between ordinary BI and product analytics infrastructure becomes important. Recent comparisons emphasize that multi-tenancy, RLS, semantic modeling, and workload isolation matter more than the ability to make attractive dashboards.

My decision tree

Choose Cube if you're engineering the product around analytics and want maximum control over the embedded experience and data/security architecture.

Choose Sigma Embedded if you want customers to do substantial ad-hoc analysis themselves and a spreadsheet-like interface is attractive. Its RLS model is explicitly designed for embedded scenarios.

Choose Looker if your organization already uses LookML/Google Cloud and you want a mature governed semantic model. Its access filters can be applied across embedded and API queries.

Choose Power BI Embedded if you're deeply invested in Azure/Microsoft and your team already knows Power BI/DAX. Microsoft documents both dynamic-RLS and workspace-isolation patterns for ISVs.

Choose ThoughtSpot if natural-language/search-driven analytics is a major part of the product rather than merely a nice-to-have.

Choose Metabase if you need to get something working quickly and cheaply, but I'd be more cautious about making it the foundation of a very large, deeply multi-tenant analytics product.

Bottom line: for a new B2B SaaS product, I'd start a bake-off with Cube vs. Sigma Embedded vs. Looker. If you're Microsoft-centric, substitute Power BI Embedded for Looker. The winner should be determined by a proof-of-concept with real tenant isolation, concurrent load, customer self-service, exports, and your actual pricing model—not by the dashboard demo.

For a multi-tenant SaaS product, I’d avoid choosing purely on dashboard quality. The hard part is securely giving thousands of customers access to their data, while keeping the experience native to your app.

My shortlist

Current 2026 comparisons consistently emphasize tenant isolation, row-level security, semantic modeling, embedding flexibility, and performance under concurrent tenants as the key differentiators.

What I'd choose

If you're building this from scratch: I'd start with Cube. Its architecture is particularly attractive when analytics is part of the product rather than simply an embedded BI report: a semantic layer, row-level security, caching/pre-aggregations, and APIs/headless options let you control the UX yourself.

If you want a more complete BI experience out of the box: I'd evaluate Omni. It has a strong combination of semantic modeling, embedded analytics, tenant-level security, and AI, which is useful if customers will eventually explore data rather than just view predefined dashboards.

If your customers love Excel/Sheets: put Sigma near the top. Its spreadsheet-like interface is unusually well suited to letting customers slice, pivot, and calculate without building a BI-style UX yourself.

The architecture matters more than the vendor

For a SaaS app, I'd specifically require the vendor to demonstrate this flow:

Your authenticated user
        ↓
tenant_id / customer_id
        ↓
embedded analytics session
        ↓
row-level security
        ↓
warehouse query
        ↓
ONLY that tenant's rows

Don't accept a design where the browser simply sends tenant_id=123 as a filter. Tenant isolation needs to be enforced server-side/at the data-security layer, not trusted to the frontend.

I'd also test:

• 10,000+ tenants, not just 10
• Different users within the same tenant
• Admin vs. regular-user permissions
• A malicious user attempting to alter tenant identifiers
• Concurrent dashboard loads across many tenants
• Customer-specific branding and dashboard configuration
• Export/download security
• Whether AI/natural-language queries inherit the same RLS
• Pricing as your number of customers and viewers grows

The last point is especially important: a platform can be technically excellent but economically painful when every SaaS customer becomes an additional analytics user.

If you tell me your stack (e.g. Postgres/Snowflake/BigQuery, React/Next.js, etc.), approximate number of tenants/users, and whether customers need read-only dashboards vs. self-service exploration, I can narrow this to 2–3 platforms and give you an architecture recommendation.

If you're building this from scratch: I'd start with Cube. Its architecture is particularly attractive when analytics is part of the product rather than simply an embedded BI report: a semantic layer, row-level security, caching/pre-aggregations, and APIs/headless options let you control the UX yourself.

If you want a more complete BI experience out of the box: I'd evaluate Omni. It has a strong combination of semantic modeling, embedded analytics, tenant-level security, and AI, which is useful if customers will eventually explore data rather than just view predefined dashboards.

If your customers love Excel/Sheets: put Sigma near the top. Its spreadsheet-like interface is unusually well suited to letting customers slice, pivot, and calculate without building a BI-style UX yourself.

The architecture matters more than the vendor

For a SaaS app, I'd specifically require the vendor to demonstrate this flow:

Your authenticated user
        ↓
tenant_id / customer_id
        ↓
embedded analytics session
        ↓
row-level security
        ↓
warehouse query
        ↓
ONLY that tenant's rows

Don't accept a design where the browser simply sends tenant_id=123 as a filter. Tenant isolation needs to be enforced server-side/at the data-security layer, not trusted to the frontend.

I'd also test:

Choosing the "best" embedded analytics platform for a multi-tenant SaaS environment depends heavily on your engineering stack, how much control you need over the UI, and whether your tenants require strict data isolation via Row-Level Security (RLS).

The top-tier platforms tailored for multi-tenant SaaS applications span several distinct use cases:

• • Cube: Best for Headless & AI-Native Embedding. Cube is multi-tenant by construction. It sits on top of your data warehouse, manages caching, and enforces strict per-tenant row-level security. It offers flexible headless APIs and drop-in components, making it ideal if you want deep front-end control or are building custom AI-driven analytics.
• • GoodData.AI: Best for Enterprise-Grade Multi-Workspace Governance. GoodData uses a decoupled, API-first architecture with a per-workspace model that cleanly isolates tenant data at scale. It is heavily optimized for SaaS products that need robust data governance combined with agentic, hallucination-free AI features.
• • Embeddable: Best for Total Frontend Customization. If you want your embedded analytics to look completely native to your SaaS application rather than feeling like a bolted-on BI iframe, Embeddable lets product and frontend teams build custom, pixel-perfect data experiences using standard UI components backed by a data modeling layer.
• • Microsoft Power BI Embedded: Best for Cost-Effective Scale & Enterprise Ecosystems. Power BI Embedded handles multi-tenancy smoothly via RLS mapped to a single shared dataset. It is a strong contender if your enterprise customers already expect Power BI-style reporting, though it requires careful Azure capacity planning for high concurrency.
• • Looker Embedded: Best for Google Cloud / BigQuery Shops. If your entire data stack is centralized in BigQuery and you already leverage LookML as a semantic layer, Looker provides deep, governed dashboard embedding—though it comes with a heavier setup and infrastructure footprint.
• • Metabase: Best for Engineering-Led or Open-Source Teams. If you need a fast, developer-friendly way to iframe or component-embed simple interactive charts and dashboards without heavy enterprise licensing overhead, Metabase offers a lightweight path.

To help narrow down the ideal choice, tell me:

• What is your primary data warehouse/database (e.g., Snowflake, BigQuery, PostgreSQL)?
• Do your tenants need to build their own ad-hoc reports , or just view pre-built dashboards?
• Do you prefer a headless/API-first approach or a ready-made UI component library?

For a multi-tenant B2B SaaS product, I’d shortlist Power BI Embedded, Looker, ThoughtSpot Embedded, Sigma Embedded, and Cube. The “best” choice depends heavily on whether you want a turnkey BI experience or analytics that feels native to your product.

My recommendation

Power BI deserves particular consideration: Microsoft explicitly supports an “app owns data” architecture designed for ISVs embedding analytics for external customers, so your customers don't need Power BI accounts or licenses.

For serious multi-tenancy, Power BI also has service-principal profiles, where you can map individual customers to profiles/workspaces and automate provisioning through the REST API. Microsoft documents this architecture for solutions with potentially tens of thousands of customer tenants.

The architecture I'd look for

Regardless of vendor, don't make the browser responsible for tenant security. Your architecture should roughly be:

Your SaaS auth → tenant context → analytics authorization → short-lived embed token → embedded dashboard

And enforce tenant isolation at the analytics/data layer through RLS and/or tenant-specific semantic models/workspaces. Power BI, for example, supports both workspace-based isolation and RLS, including combinations of the two.

What I'd choose by scenario

• You already run heavily on Azure/Microsoft: Power BI Embedded
• You need a sophisticated metrics/semantic layer: Looker
• You want customers to ask questions of their data: ThoughtSpot Embedded
• You want customers to manipulate data like a spreadsheet: Sigma Embedded
• You want the analytics UI to be deeply integrated into your own product and have a strong engineering team: Cube
• You want the least BI-engineering work possible: look particularly closely at purpose-built embedded platforms rather than simply embedding a traditional BI product.

One important distinction: multi-tenancy isn't just “can I put a dashboard in an iframe?” The key evaluation criteria are tenant isolation, RLS, provisioning automation, white-labeling, embed-token security, query/performance isolation, and—often overlooked—how pricing behaves when you go from 50 customers to 5,000.

If you tell me your stack (e.g. React/Node, .NET, Python), data warehouse (Snowflake/BigQuery/Postgres/etc.), approximate number of tenants/users, and whether customers need self-service analytics, I can narrow this to the top 2–3 and compare architecture + likely cost.

The architecture I'd look for

Regardless of vendor, don't make the browser responsible for tenant security. Your architecture should roughly be:

Your SaaS auth → tenant context → analytics authorization → short-lived embed token → embedded dashboard

And enforce tenant isolation at the analytics/data layer through RLS and/or tenant-specific semantic models/workspaces. Power BI, for example, supports both workspace-based isolation and RLS, including combinations of the two.

What I'd choose by scenario

Choosing the best embedded analytics platform for a multi-tenant SaaS application depends heavily on your engineering bandwidth, how much UI customization you need, and how you handle row-level security (RLS).

The top platforms tailored for multi-tenant SaaS environments are broken down below by their core strengths:

• Cube : Best for AI-native, headless architecture with full UI control . Cube provides a powerful semantic layer and rock-solid multi-tenant security, making it ideal if your front-end team wants to build custom UI components using their React/Vue SDKs rather than dropping in an rigid iframe.
• Embeddable : Best for deep front-end control and native-feeling dashboards . It gives developers a toolkit to build custom components that look and feel completely native to your application while abstracting away the complex data querying and caching layers.
• Qrvey : Best for purpose-built multi-tenant SaaS . Unlike traditional BI tools retrofitted for embedding, Qrvey was built natively for multi-tenancy, offering end-to-end data automation, user management, and built-in multi-tenant security out of the box.
• Omni : Best for governed, semantic-layer-aware analytics and tenant-safe AI . It is built from the ground up for modern data stacks, handling complex data modeling and secure multi-tenant data isolation cleanly.
• Luzmo : Best for fast time-to-market . If you need to ship interactive, white-labeled dashboards quickly with lighter semantic requirements, Luzmo offers streamlined embedding APIs specifically designed for SaaS products.
• Metabase : Best for open-source and engineering-led teams . It offers a lightweight, cost-effective embedding approach if you want self-hosted control and straightforward interactive iframe/SDK embedding without enterprise bloat.

To help narrow down which platform fits your architecture best, let me know:

• What is your primary database or data warehouse (e.g., PostgreSQL, Snowflake, BigQuery)?
• Do you prefer low-code iframe embeds or full code-level UI control via SDKs/React components?
• How complex is your row-level security (RLS) across tenants?