Customer-facing analytics has moved from a nice extra to a line item on most SaaS pricing pages. Buyers expect white-label dashboards inside the product, the ability to build their own reports, and now a plain-language way to ask questions of their data. Building that in-house means owning a charting layer, a query engine, tenant isolation and an AI layer, which is why most product teams buy one of the embedded analytics platforms below instead.
We compared ten options on how they embed (iframe, JavaScript or React SDK, native components), how they isolate tenants, what self-service and AI features end users get, where they can be deployed, and how pricing scales as your customer count grows. If you are still weighing general BI tools, our comparisons of Power BI vs Tableau and Power BI vs Looker cover the internal reporting side of the decision.
Embedded Analytics Platforms At A Glance
Platform
Best For
Embedding Method
Deployment
Pricing Model
Sisense
Product teams that want deep UI control and AI features
Compose SDK, APIs, iframe
SaaS, dedicated cloud, your cloud or on-premises
Quote-based plans
ThoughtSpot Embedded
Natural-language and agent-style analytics inside apps
Visual Embed SDK, REST APIs
Vendor-managed, one Org per customer
Free developer tier, custom enterprise pricing
Power BI Embedded
Microsoft-centric products with existing Power BI skills
JavaScript client and REST APIs
Azure capacity
Hourly or monthly capacity, no per-user license for app users
GoodData
Engineering teams that manage analytics as code
React SDK, Web Components, iframe
SaaS or self-hosted on Kubernetes
Quote-based
Looker
Companies on Google Cloud that want a governed semantic model
Private, signed and cookieless embedding, Embed SDK
Google Cloud
Quote-based
Yellowfin
ISVs that need flexible OEM licensing and automated insights
JavaScript API, white-label UI
Self-managed or fully managed
OEM models: usage, revenue share or server core
Sigma
Products built on a cloud data warehouse
Signed iframe embeds, REST API
Vendor cloud querying your warehouse
Quote-based
Qrvey
SaaS teams that want analytics inside their own cloud account
Embedded dashboards and dashboard builder
Your AWS, Azure or GCP account
Flat annual platform fee
Tableau Embedded
Teams already standardized on Tableau
Embedding APIs and REST API
Tableau Cloud or Tableau Server
Per user or usage-based licensing
Luzmo
Smaller SaaS teams that want a fast launch and published pricing
Dashboard embedding, Flex SDK, Agent API
Vendor cloud, private infrastructure on Enterprise
Published annual fee plus usage
Best Embedded Analytics Platforms For SaaS In 2027
1. Sisense — Compose SDK For Building Analytics Into Your Own Front-End Code
Sisense has been one of the default answers to “embedded BI” for a long time, and its current pitch is developer control. The Compose SDK gives front-end teams JavaScript APIs to build charts and dashboards directly in their own components, while iframes and widgets remain available for faster integrations. Theming, colors and plugins handle white-labeling, and a multi-tenant architecture with SSO and row-level security handles customer isolation.
The AI layer covers natural-language queries, auto-generated narratives, AI-driven anomaly and trend detection, a semantic layer for consistent metrics, and MCP and API connections so your own AI agents can reach Sisense data. On the Enterprise plan you can bring your own LLM.
Deployment is the most flexible on this list: Sisense SaaS, a dedicated cloud, your own AWS, Azure or GCP account, or on-premises, with a 99.99% premium SLA available. Pricing is not published. A Self-Serve plan with a free trial targets smaller teams, while Enterprise (column-level security, HIPAA readiness, full white-labeling, a dedicated success manager) is sold through sales. Expect a longer implementation than the lighter tools further down the list if you want to use the SDK fully.
Best for: SaaS companies that treat analytics as a core product feature and have front-end engineers to build on it.
2. ThoughtSpot Embedded — Spotter AI Analyst That End Users Query In Plain Language
ThoughtSpot started as search-driven analytics, and its embedded product is built around that idea. Spotter is a white-labeled AI analyst that answers questions in plain language, and its skills can be exposed to your own agents through MCP and APIs. Dashboards are AI-augmented, surfacing anomalies and changes without being asked, with drill-downs under every chart.
Developers embed with the Visual Embed SDK plus REST APIs, and SpotterCode, a coding agent that runs in the IDE, generates embed logic. Each customer gets its own Org in a multi-tenant setup, so onboarding a new tenant is configuration rather than redeployment. ThoughtSpot cites SOC 2, GDPR and HIPAA readiness, and it manages AI token consumption itself, so you do not take on variable LLM costs.
The free Developer plan (one year, up to 10 users and 25 million rows) makes it easy to prototype. Production embedded pricing is custom, with the Enterprise tier including unlimited users and data. ThoughtSpot works best when your data is already modeled cleanly in a cloud warehouse; messy source data limits how good the natural-language answers can be.
3. Power BI Embedded — Capacity-Based Pricing With No Power BI License Needed For App Users
Power BI Embedded lets ISVs put Power BI reports and dashboards inside their own applications using the “embed for your customers” model, in which the app authenticates with a service principal or master user and end users never sign in to Power BI. Microsoft states that app users in this model do not need a Power BI license, which is the core economic argument for it.
You pay for capacity instead. Azure A SKUs are billed hourly with no commitment and can be scaled or paused, which suits development and seasonal workloads. Microsoft Fabric F SKUs and Power BI Premium EM or P SKUs also support customer embedding, with F64 and above unlocking more scenarios. Reports are built in Power BI Desktop, so if your team or your customers already know Power BI, the authoring curve is short.
The limitations come from the same design. Tenant isolation is something you architect (separate workspaces, row-level security or both) rather than a built-in tenant model, R and Python visuals are not supported in the customer embedding model, and A SKUs do not support secure URL embedding. Capacity sizing also takes care: a busy month can force a move up a SKU.
4. GoodData — Analytics As Code With Shared Workspaces Rolled Out Across Every Tenant
GoodData is aimed at engineering-led teams. You build dashboards, metrics and AI agents once in a governed semantic layer and roll them out across customer workspaces, and changes made centrally propagate to every tenant. React, Python and JavaScript SDKs, CI/CD support, versioning and declarative APIs let you treat analytics like the rest of your codebase.
Embedding options cover the spectrum: a React SDK for the deepest customization, Web Components for Angular or Vue apps, and iframes for quick proofs of concept. The AI Assistant and agents draw on the same semantic layer as dashboards, and you can bring your own LLM, including OpenAI, Claude, Gemini, DeepSeek, Llama and xAI models.
GoodData Cloud is a managed SaaS service on AWS or Azure, and a self-hosted edition on the same codebase runs on Helm, Docker or Kubernetes for data residency needs. There is a 30-day free trial, but pricing beyond that is quote-based. Teams without engineers comfortable with code-based modeling may find it slower to start than click-first tools.
5. Looker — LookML Semantic Model Governing Every Embedded Metric
Looker’s strength is LookML, the modeling language developers use to define metrics and relationships once so every dashboard and API call returns consistent numbers. For embedded use, Looker supports private, signed and cookieless embedding, an Embed SDK, and a REST API that covers most of the product. Embedded users can navigate reports, download data and set up schedules and alerts inside the iframe, and every option supports theming.
Looker queries the database directly rather than extracting data, uses git-based version control for continuous deployment, and supports integrating generative AI on top of its governed metrics. Google positions it for OEM and white-label use, including offering Looker features as premium tiers inside your own product.
Looker Embedded is sold as part of Looker (Google Cloud core) on a quote basis, so cost is hard to estimate early. It also needs someone who can write and maintain LookML, which is a real commitment for a small product team. Note that Looker is a different product from the free Looker Studio.
6. Yellowfin — Signals Automated Monitoring And OEM Licensing Matched To How You Sell
Yellowfin is a long-standing BI platform with a specific focus on software vendors. It offers white-label analytics, a JavaScript API for embedding reports, charts and dashboards, custom JavaScript visualizations, pre-built embeddable dashboards and self-service reporting for your customers. Guided natural-language query and AI-assisted reports cover the conversational side.
Two features stand out. Signals automatically analyzes data and alerts users to significant changes, which suits products where customers want to be told when something moves rather than check a dashboard. Stories and Present combine charts and narrative for report-style output. Licensing is unusually flexible for ISVs: an aligned utility model priced per unit you sell (per site, app or device), a revenue share on an analytics add-on module, or a fixed server-core price, along with user-based tiers. Yellowfin states that dev and test licenses are not limited.
You can self-manage Yellowfin on-premises or in your cloud, or have it fully managed. No prices are published, and its developer tooling is less code-centric than Sisense or GoodData, so teams that want to compose analytics entirely in their own React code may prefer those. For vendors who want a complete BI product to white-label under commercial terms that fit their own pricing, it is a strong fit.
7. Sigma — Spreadsheet Interface With Write-Back To Your Cloud Warehouse
Sigma’s interface is a spreadsheet, which makes it approachable for business users, and its architecture is warehouse-first: it queries live data in Snowflake, Databricks, BigQuery, ClickHouse and other cloud platforms rather than copying it. Input Tables let your customers write data back to the warehouse, which opens up planning and data-entry use cases most embedded tools cannot handle.
Embedding uses JSON Web Tokens and signed, one-time URLs, with full workbooks or single visualizations placed through secure iframes or the REST API. JavaScript events connect the embed to your app, and Sigma Tenants let you provision customers through APIs, swap data sources per tenant and monitor usage across them. Row-level, column-level and role-based security, localization and the “Ask Sigma” natural-language feature are included.
Sigma only makes sense if your product data already lives in a supported cloud warehouse, and warehouse compute costs rise with customer activity. Pricing is not published.
8. Qrvey — Flat-Fee Analytics That Runs Inside Your Own AWS, Azure Or GCP Account
Qrvey is built for SaaS companies and takes a different deployment approach: it runs as Kubernetes containers in your own AWS, Azure or GCP account, so customer data never leaves your cloud. For products with strict data residency or security reviews, that removes a common objection.
Pricing is a flat annual platform fee with no per-seat or per-tenant charges, and Qrvey advertises unlimited users, dashboards and tenants. Multi-tenant security is enforced at row, column, object, asset and feature levels. End users get a drag-and-drop dashboard builder with custom formulas and drill-ins, and the AI features include natural-language queries, anomaly detection, AI-generated dashboards and agents, with support for any LLM and MCP.
The trade-off of running in your account is that your team operates the infrastructure and carries its cloud costs. Qrvey’s brand is also less familiar to enterprise buyers than the larger names above it.
Tableau brings its well-known visualization depth to embedding, along with AI features such as Tableau Pulse and Einstein. The Embedding Playground provides a hands-on environment for building, and embedded deployments support single sign-on, on-demand access without pre-provisioning users, and an allowlist of sites that can host Tableau content.
For external audiences, Tableau offers usage-based licensing as an alternative to per-viewer licenses. You buy a pool of Analytical Impressions that viewers draw down as they interact, and overages are billed in arrears rather than cutting off access. That helps SaaS companies with large but lightly active user bases.
Tableau ranks lower here because it was designed first as an internal BI product. Multi-tenant SaaS setups take more design work than on platforms built for ISVs, pricing goes through sales, and the full value comes when your team already builds in Tableau.
10. Luzmo — One Published Plan With Every Feature Included
Luzmo, a company with roots in Belgium and the United States, is built specifically for SaaS product teams that want customer-facing dashboards running in days. Its product family includes Luzmo Studio for self-service dashboards with a white-label responsive UI, the Flex SDK for composable analytics, and Luzmo IQ and Luzmo AI for conversational and summary agents. AI can also be delivered through an embedded MCP server, an Agent API or white-label chat assistants.
Pricing is published and simple: a single plan starting at €1,995 per month billed annually, with every feature included. Usage beyond the included allowances (100 million synced rows and 500 AI conversations a month) is billed at stated rates, and some contracts scale on monthly active end users instead. Enterprise changes the deployment, adding private infrastructure, a custom SLA and compliance controls, rather than unlocking features.
Luzmo runs the production layer for you, so there is less to operate but also less deployment control than with Qrvey or self-hosted Sisense. Its semantic modeling is lighter than Looker’s or GoodData’s, which matters for products with complex metric definitions.
How To Choose An Embedded Analytics Platform
How Deeply Do You Need To Integrate?
An iframe gets dashboards into your app quickly but always feels slightly separate. If analytics is central to your product, look for an SDK that renders components in your own front end, such as the Sisense Compose SDK, the GoodData React SDK or the ThoughtSpot Visual Embed SDK. Prototype the exact screen you plan to ship before signing.
How Will Pricing Scale With Your Customer Base?
Per-user pricing can quietly eat your margin as customer accounts grow. Compare flat platform fees (Qrvey, Luzmo’s base plan), capacity pricing (Power BI Embedded), usage pools (Tableau’s Analytical Impressions) and OEM models tied to your own pricing (Yellowfin). Model the cost at today’s tenant count and at three times that number.
Where Does Your Data Live?
Warehouse-native tools such as Sigma and Looker query your data in place, which keeps it fresh but shifts compute cost to your warehouse bill. Platforms that cache or ingest data can be faster and cheaper per query but add a sync step. If customers ask where their data is processed, self-hosted options such as Qrvey, GoodData and Sisense help with security reviews.
How Is Multi-Tenant Analytics Handled?
Ask each vendor to show exactly how a new customer is onboarded, how row-level security is enforced, and how a dashboard change rolls out to every tenant. Platforms with a native tenant model (ThoughtSpot Orgs, Sigma Tenants, GoodData workspaces) save engineering time compared with assembling isolation from workspaces and filters.
What Should AI Do For Your Users?
Most platforms now offer natural-language questions, but quality depends on the semantic layer underneath. Check whether answers are grounded in governed metrics, who pays for LLM tokens, whether you can bring your own model, and whether the platform exposes MCP or APIs so your own product agents can use it.
Conclusion
Sisense and ThoughtSpot lead this year for SaaS teams that want analytics and AI to feel native to their product, while Power BI Embedded remains the pragmatic choice for Microsoft-centric companies thanks to its capacity pricing and license-free app users. GoodData and Looker suit teams that want governed, code-managed metrics, Yellowfin stands out for automated monitoring and OEM licensing built around how vendors sell, and Sigma, Qrvey, Tableau and Luzmo each answer a specific need: warehouse write-back, running in your own cloud, an existing Tableau investment, or a quick launch at a published price.
Shortlist two or three embedded analytics platforms, build the same customer-facing screen in each with your real data and tenant model, and price them at your projected scale before you commit.
