Executive Summary
When analytics ship inside a product, every customer becomes a user of the analytics vendor. The contract has to work at that scale.
Embedded analytics is reporting built into an application for that application's users. A SaaS company adds dashboards to its product; a manufacturer gives distributors a portal with their sales data; a bank shows clients their spending. The business intelligence guide covers analytics for a company's own staff. This guide covers analytics a company ships to its customers.
The difference changes the evaluation. The users are outside the company, so security between customers comes first. The analytics must look like the product. The price has to work when the number of viewers grows with the customer base. The approaches in this guide reflect those needs: embedded BI platforms, products built for SaaS teams, multi-tenant platforms with flat pricing, developer toolkits, semantic layers, and the embedding options of BI suites.
Why Embedded Analytics Is a Separate Purchase
A BI tool licensed for employees is built around named users inside one company. A product with thousands of customer accounts needs analytics that separate each customer's data, match the product's design, and cost a predictable amount per customer. Building that in-house is possible. The cost shows up later, in maintaining charts, permissions and performance as customers ask for more.
Tenant separation is the first test. Sisense describes enforcing tenant-safe access and row-level security at the query layer to help prevent one customer's data from appearing in another's answer. Qrvey describes native tenant isolation and permissions for multi-tenant analytics.
The look is the second. Embeddable states that it renders inside the customer's app, in their design system, with no per-seat pricing. Luzmo states that all of Luzmo is included from day one: white-label, self-service, AI and APIs.
Build, Embed a BI Suite, or Buy an Embedded Platform
Product teams have three choices. They can build charts with open-source libraries and their own queries. They can embed the BI suite the company already uses. Or they can buy a platform designed for customer-facing analytics. Building gives full control and the full maintenance load. Embedding a BI suite reuses skills and licenses, within that suite's licensing rules. A dedicated platform trades some control for multi-tenancy and pricing designed for products.
Check licensing for viewers before choosing a BI suite. Microsoft states that with secure embed, the viewer of the report must have the proper Power BI license. Other embedding options have different terms, so ask which one fits customer-facing use.
| Model | What you get | What you take on |
|---|---|---|
| Build in-house | Full control of design and queries | Charts, permissions and performance to maintain |
| Embed a BI suite | Reuse of existing models and skills | The suite's licensing and look |
| Embedded BI platform | Dashboards, self-service and security for products | A vendor in your product's critical path |
| Developer toolkit | Components rendered in your own app | More engineering than a packaged platform |
| Semantic layer | One governed model for many front ends | Building or buying the front end separately |
How do you evaluate Embedded Analytics Platforms?
Evaluate these products inside your own application, with your data and a realistic number of tenants. The capabilities that decide results are multi-tenancy and security, design control, self-service for customers, performance, AI questions, and the developer experience.
AI answers raise the stakes on governed data. Cube describes BI for humans and AI agents, for teams and their customers, with one semantic model keeping every answer governed and consistent. Cube states that it gives AI agents governed, reliable business data through MCP and APIs. Embeddable describes native dashboards and conversational self-serve for customers, with Embeddable handling security, permissions, performance and scale.
| Capability | What it does | Buyer translation |
|---|---|---|
| Multi-tenancy | Separates each customer's data and permissions | The first test; ask to see it enforced. |
| White-labeling | Matches the product's design | Ask what can and cannot be styled. |
| Self-service | Lets customers build their own views | Reduces custom report requests. |
| Performance | Fast queries at many tenants | Test with your data volumes. |
| AI questions | Natural-language answers on governed data | Ask how answers respect tenant security. |
| Developer experience | SDKs, APIs, components, version control | Decides how fast your team ships. |
| Semantic model | Shared metric definitions | Keeps numbers consistent across views. |
| Deployment | Vendor cloud or your own environment | Matters for data residency and security reviews. |
Vendors in Embedded Analytics Platforms
Representative vendors include Cube, Embeddable, Luzmo, Microsoft, Qrvey and Sisense. The list is alphabetical, is not a ranking, and is not for sale. How we choose vendors
The approaches below differ by how much they provide and how much the product team controls. Several vendors serve more than one kind of buyer, so each approach describes where a product is strongest.
Experience counts differently here than in internal BI. Sisense states that it has 20+ years of experience powering analytics in customer-facing products. Newer entrants are built around components, semantic models and AI agents. Ask each vendor how many customer-facing deployments of your size it supports today.
| Vendor | Approach | Where it fits |
|---|---|---|
| Microsoft | BI suite embedding | Power BI content embedded in applications |
| Embeddable | Developer toolkits | Dashboards rendered in the product's design system |
| Luzmo | Embedded analytics for SaaS | White-label analytics with published pricing |
| Sisense | Embedded BI platforms | Customer-facing analytics with tenant-safe access |
| Qrvey | Multi-tenant platforms | Tenant isolation with flat-rate pricing |
| Cube | Semantic layers | One semantic model for people and AI agents |
Approaches came first and one representative was selected for each afterwards; inclusion cannot be bought. About twenty vendors offer embedded analytics, and BI suites offer embedding options of their own. Vendors in this category, named here or not, can set out their own case in the Spotlight below.
How much should you budget for Embedded Analytics Platforms?
Embedded analytics is priced by viewers, tenants, usage or a flat platform fee, and the choice matters more than the list price. Qrvey describes unlimited tenants, users and dashboards under a single flat rate. Luzmo states that its public price is billed annually. Luzmo states that its license includes 500 AI conversations and 100 million Warp rows, with $0.25 per extra million rows beyond that.
Model the cost at your expected scale, not at launch. Include the engineering time to integrate and maintain the analytics, and the revenue the analytics can earn if you sell them as a premium tier of your product.
| Meter | You are charged for | Grows with | Where it goes wrong |
|---|---|---|---|
| Viewers | People viewing analytics | Customer base | Costs rising with every new customer |
| Tenants | Customer accounts | Customer count | Small customers costing as much as large ones |
| Usage | Queries, rows or AI conversations | Activity | Spikes from heavy users or AI features |
| Flat platform fee | The platform regardless of users | Contract tier | Tier limits on features or deployment |
| Developer seats | Builders and authors | Team size | Paying for occasional authors |
How long does implementation take for Embedded Analytics Platforms?
Embedded analytics projects depend on tenant security and data modeling. Prove separation between customers first, then design the experience.
Model the data and metrics. Configure tenant isolation and test it with several test accounts.
Embed the first dashboards in the product and match its design. Test performance at realistic data volumes.
Release to a group of customers. Measure use, support tickets and requests for new views.
Add self-service and AI questions. Decide which analytics are included and which are a paid tier.
What should you ask vendors about Embedded Analytics Platforms?
Ask each vendor to answer inside your application, with your data and tenants.
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Is your company already standardized on a BI suite?Yes Check that suite's embedding licenses first.No Continue.
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Does your product team want analytics rendered in its own components?Yes Shortlist developer toolkits and semantic layers.No Continue.
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Do you have many customer accounts with uneven usage?Yes Shortlist multi-tenant platforms with flat or tenant pricing.No An embedded BI platform or SaaS-focused product fits.