Best Embedded Analytics BI Tools: A Comparison Guide

The concise guide to evaluating embedded analytics tools.

LAST UPDATED February 20, 2025

AUTHOR

Holistics Team

Most embedded analytics guides exist to game search engines. Not this one. Here’s a fact-based, methodical breakdown of embedded tools you can actually use.

Our approach:

Facts are prioritized over opinions, no recommendations pushed
Details are backed by official documentation
High-level criteria are broken down into specific, measurable sub-points
Findings are presented in in clear, comparable tables
Linking to real-world discussions from actual users

The embedded analytics platforms compared in this document are: Holistics, Looker, Tableau, Power BI, Metabase, Domo, Sisense, Sigma Computing, Luzmo, Explo, Reveal BI.

We understand we might come across as biased, since we’re also a vendor selling embedded analytics. Rather than claiming neutrality, we’ll let the content below speak for itself.

Found an inaccuracy or want your tool added? Use this form.

Feature-by-Feature Comparison Table

Scroll to the right to see more

Dimension	Holistics	Looker	Tableau	Power BI	Metabase	Domo	Sisense	Sigma Computing	Luzmo	Explo	Reveal BI
Demo Playground
	Available source Status: This is a demo WIP status	❌	Available source	Available source	❌	❌	Available source	Available source	❌	Available source	❌
Pricing Structure for Embedded Learn more Pricing is among the most important factors to consider when evaluating BI tools. A good embedded pricing should be easy to start when doing POC, as well as not be too expensive when you scale up. Typically, embedded analytics tools have a pricing structure that scales with the number of embedded viewers or the usage of your application. Common pricing structures are: Seat-based pricing: Pricing by embed viewers. Platform pricing: One fixed price (usually quoted and negotiated) for unlimited viewers. Usage-based pricing: Pricing by some usage metric like report runs or active sessions. Additionally, some tools may have feature-tiered pricing. Some vendors may offer discounted pricing for your POC period.
Pricing Metric	Query runs/Query slots Select between Query Runs or Concurrent Workers. With unlimited dashboard viewers. source	API calls One production instance, 10 Standard Users, 2 Developer Users, upgrades, up to 500,000 query-based API calls per month, and up to 100,000 administrative API calls per month. source	Analytical Impressions Pay for entitlements of Analytical Impressions. Applies to users with the Viewer role. source	Node Type & Node Instances Pricing based on node types and node instances. To publish content to an embedded capacity, at least one user requires a Power BI Pro license. source	Feature-tiered Full-powered embedding starts from Pro plan ($500/mo). source	Usage-based pricing Domo uses a credit-based pricing model based on data rows, executions, and platform activities. source	Not Available Sisense pricing isn't public and depends on use case and budget; contact Sisense for a tailored quote.	User Licences & Platform Fees Pricing isn't publicly available, but discussions on Reddit suggest it's based on a platform fee and user licenses.	MAV & MAD For the Pro Plan (USD 2,050/month), you get 500 Monthly Active Viewers (MAV) and 5 Monthly Active Designers (MAD). source	Customer Groups Priced by Customer Groups, which determine the number of distinct client segments you can manage and provide customized analytics. source	Not Available Not publicly available, but appears to follow an SDK flat-rate model. source
Pricing Estimate	$9,600+/year Starting at $800 per month for the standard plans. Contact sales for detailed embedded pricing. source	$83,665+/year Vendr's data shows Looker costs ~$83,665/year on average, while embedded pricing starts at $180K/year, per Reddit users. source 1 , source 2	Not Available No pricing estimate available. Contact sales for a custom quote. source	$9,000+/year Power BI Embedded starts at $735.91/month for an A1 node (1 vCore) and increases with node type and count. source	$6,000+/year Embedded analytics is available in the Pro Plan ($500/month + $10/user). source	Not Available Vendr data shows Domo costs ~$134K/year on average. Requires speaking to sales for embedded pricing. source	Not Available Requires speaking to sales for embedded pricing. Vendr data shows standard Sisense costs ~$136K/year on average. source	Not Available Requires speaking to sales for embedded quote. Vendr data shows Sigma standard plans costs ~$57K/year on average. source	$11,940+/year The Basic plan starts at $995/month for 100 Monthly Active Viewers (MAV) and 1 Monthly Active Designer (MAD). source	$9,540+/year Paid plans start with the Growth Plan at $795+/month for 25 customer groups. source	Not Available Requires talking to Sales.
Embedding Methods Learn more Embedding methods affect the customizability of your embedded apps. Typically with higher customization, the tradeoff is higher development effort. Common embedding methods are: iFrame: Embeds a pre-built dashboard or report into your app using an HTML iframe tag. Web Components: Embeds analytics as reusable custom web components that integrate natively into your app's UI framework. API integration: Uses APIs to fetch and render data dynamically, allowing custom-built dashboards within your app. SDK: Provides a software development kit (SDK), often in a specific programming language or framework (e.g., React SDK), to integrate and customize analytics. Each method has its pros and cons. You should evaluate embedding approaches based on your app's architecture, required level of customization, and available development resources.
iFrame Embedding	iFrame embedding Provide sandbox UI for generating the iFrame backend code source	Signed embedding Allow developers to generate signed embed URLs for dashboards, Looks, or Explores. source	iFrame embedding Provide embed codes with configurable URL parameters for iframes on external applications. source	iFrame embedding Create secure token-based URLs for iframes, with access controlled via Azure Active Directory. source	Static embedding Allow users to generate signed URLs with tokens for secure embedding. source	iFrame embedding Allows developers to embed cards and dashboards via iframe using URL attributes source	iFrame embedding Supports embedding via iframe with web access token and customizable filter settings. source	iFrame embedding Provide embed URLs and secure authentication tokens that can be integrated into external apps via configurable iframe parameters source	iFrame embedding Not Available	iFrame embedding Supports constructing the iFrame link using two methods - Using Embed Secret (Recommended), and Using Dashboard ID & Customer Token source	❌
SDK Embedding	❌	JavaScript SDK Offers Embed SDKs for JavaScript, allowing developers to streamline embedding workflows. source	SDK embedding Not Available	JavaScript SDK Provides a JavaScript SDK with pre-built functions for authentication, event handling, and embedded report interaction source	React SDK Offers an Embedding SDK for React, allowing developers to integrate individual Metabase components. source	❌	Compose SDK Supports embedding analytics with Compose SDK, a set of client-side libraries and components for query composition and data visualization. source	❌	SDK Embedding Provides a web component to securely embed a specific Luzmo dashboard. source	Explo JS Explo supports web component embedding in HTML, vanilla JS, and any JS framework like React, Vue, or Angular. source	Reveal SDK Supported with Reveal SDK Web Component Wrappers, currently in Beta. source
API Integration	❌	Embed API Embed API allows developers to programmatically control embedded content, such as dynamically generating embed URLs and interacting with Looker content. source	JavaScript API Provides a JavaScript API that allows developers to interact with embedded dashboards programmatically. source	REST API Provides REST APIs that enable developers to programmatically retrieve embed URLs, generate embed tokens, and manage content. source	Embed API Provides an embedding API that enables developers to fetch cards and dashboards via JSON Web Tokens (JWTs). source	Embed API Enables automated embed token creation for secure access, with authentication and pre-set filters for controlled data access. source	Embed API Offers a REST API for advanced user and developer access to server functions like user management, data model manipulation, and dashboard customization. source	REST API Enables programmatic management of users, permissions, workbooks, and tasks like onboarding, data export, and row-level security. source	Core API Provides access to the same API used to build Luzmo, which uses REST with default HTTP verbs and JSON Content. source	❌	Reveal API Supports receiving data directly from Reveal SDK. source
Public Embedding	Shareable Link Allow users to create a shareable link of a dashboard, and embed it directly to websites. source	Embeddable URLs Allow users to share data visualizations publicly via embeddable URLs source	Tableau Public Supports public embedding via Tableau Public, enabling users to publish visualizations to a publicly accessible server. source	Publish To Web Via "Publish to Web" option, which generates a publicly accessible embed URL for sharing reports without authentication. source	Public Links Allow dashboards and questions to be shared via public links, which generate a shareable URL anywhere. source	Public Embedding Allows users to embed dashboards with a publicly accessible link. source	❌	Public Embedding Allows sharing content (workbooks, pages, or individual elements) with publicly accessible links. source	Public Embedding Allows users to publish dashboards to a publicly accessible link. source	Public Embedding Available but lacks dedicated documentation; details are only provided on the pricing page.	❌
Permission & User Access Control Learn more Embedded tools should ensure each customer can only see their own data and prevents unauthorized modifications. This is called multi-tenancy. Most embedded BI tools support the basic version of this capability. When evaluating, look into permission levels (e.g., column-level access, row-level access, password-protected sharing) and ensure permission settings are intuitive.
Multi-tenancy	Multi-tenancy Support row-level access controls managed through secure server-side tokens (JWT).	Multi-tenancy Supports Row-Level Access Control (RLS), and user attributes to securely segment data and tailor experiences for different tenants.	Multi-tenancy Supports site-level segmentation, user permissions, and Row-Level Security (RLS) to isolate data and content for different tenants.	Multi-tenancy Supports row-level security, user filters, tenant-specific configurations.	Multi-tenancy Supports row-level security via JWT tokens and multi-tenant deployments.	Multi-tenancy Configurable via programmatic filtering. Also supports configurable rows and columns policies using PDP (Personalized Data Permissions). source	Multi-tenancy Enables row-level security with rules controlling user access to raw data, automatically tailoring dashboards to display user-specific data. source	Multi-tenancy Supports column-level security by configuring the embed API to pass user attributes at runtime. source	Multi-tenancy Supports multi-tenancy through flexible token-based overrides and plugin architecture. source	Customer Hierarchy Allows defining hierarchical user roles and managing data visibility at various levels. source	RLS with Usercontext Supports user-specific data access with row-level security. source
Dynamic Data Sources	Dynamic Data Sources Allow connecting to different data sources (databases) based on different customers (tenants). source	❌	❌	❌	❌	Dataset Switching Allow users to dynamically change the underlying data of embedded dashboards. source	Dynamic Elasticube Dynamically determines the appropriate data source for powering a given dashboard. Supports only Windows and Linux. source	Dynamic Warehouse Switching Sigma supports dynamic Warehouse and Role switching at runtime, configurable via the Embed API. source	❌	Multiple Data Sources Customers link to data sources per schema. Dashboards pull data from these sources, using a specified data_source_id or default if none is provided. source	Via UserContext Reveal BI dynamically configures data sources based on user context such as permissions, or tokens to determine access and usage. source
Look & Feel Learn more The BI tools should allow developers to maintain a branding consistency, deliver customized dashboards and cater to diverse visualization needs. When evaluating, look into the level of customization for colors, fonts, layout. Assess the variety of chart types and their customizability.
CSS & Custom Styles	Custom Theme and CSS Styling Allow users to create custom, reusable themes using code through canvas-based dashboard. source	Custom Themes & CSS Styling Allow admin users to build and customize themes through LookML or UI settings. source	Custom Themes & CSS Styling Supports indirectly via extensions and web embedding with CSS and JavaScript for advanced theming. source	Custom Themes & CSS Styling Allow users to import JSON-formatted theme files. source	Custom Themes & CSS Styling Available natively. source	Custom Themes & CSS Styling CSS and custom style available in Domo Bricks. source	Custom Themes & CSS Styling CSS can be dynamically injected into dashboards using JavaScript. source	Custom Themes & CSS Styling Through theme selection, custom font uploads, and URL parameters. source	Dynamic CSS Injection Supports both dynamic and static CSS injection, giving users flexibility in applying styles either on the fly or as predefined settings. source	Custom Styles Allow developers to customize dashboard elements with CSS. source	Via Reveal SDK Create a custom RevealTheme to define colors and fonts for Reveal SDK UI elements. source
Custom Visualizations	Via Vega-lite Supports custom charts via Vega-Lite. source	Via Looker Marketplace Available through Looker marketplace. source	Via Viz Extensions API Rich visualization options. Custom charts are supported with Viz Extensions API. source	Via Power BI Visual SDK Supported with Power BI Visual SDK, limited native theme customization source	Custom Visualizations Requires external tools or programming languages (e.g., Python with libraries like Seaborn or D3.js). source	Via D3 Add-ons Supports custom visualizations through an app ecosystem. source	Via Highcharts API Allow users to customize widgets using Highcharts API. source	Via D3/Recharts Plugins Available Plugins include Recharts Bar Chart, D3 Candlestick/Graph/Sunburts, Narrative Science Quill, and Frappe Heatmap. source	Luzmo Flex Supports custom visualizations library for Luzmo Flex SDK. source	❌	Via Reveal SDK Supports flexibility to define custom visualization components. source
Custom Layout	Canvas Dashboard Through canvas-based dashboard that allows more fluid dashboard design. source	Code-based Customization LookML Dashboards in Looker allow code-based customization of tile positioning, sizing, and settings. source	Custom Layout Supports custom dashboard layouts with drag-and-drop, tiled or floating containers, flexible sizing, and Tableau extensions. source	ICustomLayout & IPageLayout Allows custom layout definition via ICustomLayout and IPageLayout in embed configurations. source	Custom Layout Limited support for custom layout via drag-and-drop interface to arrange charts, tables, and text cards on the dashboard. source	Domo Stories Domo Stories allow users to create custom layout or use existing templates. source	Custom Layout Offers limited customization options for its embedded solution using JavaScript and the add-on frameworks. source	Grid System Supports custom layouts via a drag-and-drop interface and grid system that allows users to arrange, resize, and customize visual components. source	Luzmo Flex Allow users to programmatically create custom data products and blend them with your own components. source	❌	❌
Performance & Scalability Learn more Tools should ensure reasonable performance, and should scale well with increasing viewers and data volumes. There are typically 2 methods that affect report performance: Caching: Embedded vendors load data into their own caching layer to speed up serving of reports. Direct Querying: BI tool querying your database/data warehouse directly.
Query Caching	Query Caching Stores query results on demand for configurable durations. source	Query Caching Stores query results and leverages persistent derived tables (PDTs) to precompute and cache aggregated or intermediate query results. source	Query Caching Store query results in its in-memory data engine and utilizing a query cache on Tableau Server to improve data retrieval speed. source	Query Caching Supports storing initial query results locally for semantic models in Import mode. source	Query Caching Caches query results with configurable expiration for embedded dashboards. source	Query Caching Provides a live cache layer, Adrenaline, an in-memory processing engine that caches data and executes queries directly in memory for real-time analytics. source	Query Caching Part of Sisense's Data Engine and is an add-on feature. Users have the option of caching their data to either the Redshift Cache or the Snowflake Cache. source	Query Caching A workbook's TTL controls how long Sigma uses cached query results. If expired, Sigma retrieves cached results, re-queries the warehouse, and updates the workbook when new data is available. source	Query Caching Query results are cached for a specified time period, during which identical queries are served from the cache. source	Dashboard-level Cache Caching is set at the dashboard level. When caching is enabled, the results of any query will be cached in Explo’s Redis ephemeral data storage. source	Query Caching Store queried data in an embedded database, retrieve it instantly on cache hits, and only reprocess modified steps in a query pipeline to minimize redundant data requests. source
Query Optimization	Aggregate Awareness Automatically pick the most optimal aggregated tables per each query for maximal query performance. source	Aggregate Awareness Automatically pick the most optimal aggregated tables per each query for maximal query performance. source	Hyper Data Engine Hyper is Tableau's in-memory Data Engine technology optimized for fast data ingests and analytical query processing on large or complex data sets. source	User-defined Aggregation Supports aggregation tables that cache data at an aggregated level in-memory. Requires users to manually define aggregation rules and mappings. source	Various Methods Via database indexing, caching query results, and allowing users to write custom SQL for precise and efficient data retrieval. source	❌	Materialized Views Enables pre-aggregation with Materialized Views, in-memory processing, cross-database joins via Elastic Data Engine, and query performance monitoring. source	Query Optimization Sigma shows query execution time and performs some client-side calculations for better performance.	Luzmo Warp Luzmo Warp pulls data into an optimized analytical datastore at regular intervals. Queries are then executed against stored data instead of source database. source	FIDO Offer a microservice to handle customer database interactions which called Fast Interoperable Data Orchestrator (FIDO). source	❌
Self-service Report Creation Learn more Embed viewers should be able to customize and build their own reports based on a predefined set of data dimensions and metrics. Viewers should also be able to interact, download, and share custom reports with other embed viewers. When evaluating: Assess the depth and flexibility of self-serve features like filtering, drill-through, grouping, and visualization options. Look for embedded report builders and assess how easily users can build reports without assistance.
Self-service Exploration	Data Exploration Through a drag-and-drop interface within datasets, using filters, drills, native period comparison and curated dimensions and measures. source	Data Exploration Through pre-built Explores, filters, pivoting, drill-downs, custom dimensions and measures. source	Data Exploration Through a drag-and-drop interface, filters, pre-defined parameters, drill-down hierarchies, and natural language queries via Ask Data. source	Self-Service Data Exploration Enables analysis with drill-throughs, slicers, filters, and natural language Q&A. source	Data Exploration Through an intuitive interface that includes a graphical query builder, interactive dashboards, and a data browser for navigating databases and tables. source	Data Exploration Via interactive dashboards, drag-and-drop tools, Beast Mode calculations, dynamic filters, and ad hoc reporting. source	Data Exploration Viewers can explore data using filters or choose a drill-down path from a complete list of available fields. source	Data Exploration Viewers can drill down, add filters and view underlying metrics. source	Data Exploration Viewers can sort, add dynamic filter, drill down, and use Luzmo AI to explore data. source	Data Exploration Data drill-down filter and editable section for adding, removing, and resizing charts. source	Data Exploration Support configurable filter and dynamic filtering. Viewers are allowed to drill down into data. source
Alert & Scheduling	Alert and Scheduling Condition-based automated alert. Email/Slack scheduling is available in PDF/CSV format. source	Alert and Scheduling Allow users to set data-driven alerts and schedule report deliveries via email, Slack, or webhooks. source	Alert and Scheduling Through data-driven alerts for threshold notifications and automated schedules. source	Alert and Scheduling Allows users to set up automated alerts, schedule extract refreshes and report deliveries. source	Alert and Scheduling Enables users to configure notifications for specific data conditions, delivering updates via email or Slack at defined intervals. source	❌	Alert and Scheduling Supports conditional alerts based on data values and allows email scheduling for notifications source	Scheduled Export Allow users to schedule an export to send only if specific conditions are met. source	Condition-based Alerts Notify users when a certain threshold in a chart is reached. source	Scheduled Emails Supports scheduled email reports. No alerts. source	❌
Embedded Report Builder	Embedded Report Builder WIP feature.	❌	Embedded Report Builder Through Embedded Web Authoring feature that enables end-users to create and modify reports within the embedded environment. source	❌	Embedded Report Builder Through Interactive Embedding (Pro and Enterprise plans). source	Embedded Report Builder Supported with Domo Everywhere source	Embedded Report Builder Basic embedded dashboard editing can be granted to editors, with more advanced use cases available through the add-ons. source	Embedding Workbook Available by embedding Sigma Workbooks. source	Embedded Dashboard Creator The Embedded Dashboard Editor allows end-users to create and modify reports directly within the embedded environment. source	Embedded Dashboard Creator Available exclusively with the Enterprise Plan. Embedded viewers can configure their reports by selecting columns, applying filters, aggregating data, and more. source	Embedded Dashboard Creator Dashboard can be configured to support certain capablities of editing, such as adding filter, adding calculation, and visualizations. source
Security & Compliance Learn more Your embedded BI tools should protect your customers' sensitive data and builds trust by meeting industry security standards. Common security compliance certificates are: SOC2, HIPAA/BAA (for health tech companies), and GDPR.
Compliance	SOC2, BAA, and GDPR compliant source	SOC2, HIPPA, and GDPR compliant source	SOC2, HIPPA and GDPR compliant source	SOC2, HIPPA, and GDPR compliant source	SOC2, CCPA, and GDPR compliant source	HIPAA, SOC2, CCPA, ISO, GDPR compliant source	HIPAA, ISO, SOC2 compliant source	HIPAA, ISO, SOC2 compliant source	SOC2 & GDPR compliant	HIPAA, SOC2 compliant source	GDPR, SOC2 compliant source
Server Location	Servers in US (San Francisco), Europe (Germany), and APAC (Singapore). source	Multiple locations in US, EU, APAC and Middle East. source	Multiple locations in North America, Europe and APAC source	Multiple locations across the world based on Azure regions. Default based on the region of signup. source	Information Not Available	Information Not Available	Information Not Available source	Information Not Available	Multiple locations in US & EU	Information Not Available	Information Not Available
Maintainability Learn more Should allow the reuse of analytics logic and components across customers to reduce the maintenance burden for developers and product engineers. Common functionalities to support this are: Semantic layer to allow defining reusable, centralized metrics Ability to define logic using code language (analytics as code) 2-way integration with Git version control
Semantic Modeling Layer	Code-based Semantic Layer Centralizes business logic in modular, reusable data models, allowing consistent definitions across reports. source	LookML Data Modeling Enables defining centralized, modular data logic that can be reused across reports. source	Logical Layer Proprietary. Tableau's Logical Layer visually defines relationships and aggregation rules within workbooks or data sources. source	GUI-based Modeling Project-focused and workbook-scoped, using visual interfaces and DAX for defining relationships and calculations. Limited global reusability unless datasets are explicitly shared. source	Lightweight Modeling Layer Provides drag and drop ETL, joining datasets, calculated fields and custom metrics. source	GUI Data Modeling Provides drag and drop ETL, joining datasets, calculated fields and custom metrics, with DataFlows offering flexibility for advanced transformations. source	Hybrid Data Modeling Supports flexible data modeling with ElastiCube for in-memory analytics, Live models for real-time queries, B2D for cloud data warehousing, and hybrid models combining both for optimized performance. source	Data Models (Beta) Sigma's Data Models offer a semantic layer for building reusable metrics and centralizing relationships, metrics, and transformations. source	Not Available Support custom aggregation, adding and linking SQL datasets only. source	Datasets Supports joins and custom aggregates only. For more complex modeling, it is recommended to perform these tasks in the ETL layer. source	❌
Analytics as Code	Code-based definition & querying languages (AMQL) Define models and dashboards using code, enabling components to be parameterized and reused. source	Code-based definition language (LookML) Enables defining centralized, modular data logic that can be reused across reports. source	❌	Supported via TMDL format An object model definition syntax for tabular data models at compatibility level 1200 or higher. source	❌ Product not designed to support code-based definition natively. Workaround using Serialization. source	❌ Product not designed to support code-based definition natively. source	Supported via Javascript format Enables UI customization with JavaScript and the add-on frameworks, allowing developers to script lifecycle events to adjust styles, data, and queries source	❌	Luzmo Flex SDK Allows data team to code custom analytics products directly in the app. source	❌	Partially Supported Dashboard is saved in proprietary format, but its configuration and how it should be rendered in the client, can be coded. source
Version Control	Native Git Version Control Support Git version control natively for both dashboards and models. source	Git-based Version Control Git-based version control for data models, proprietary dashboard versioning. source	Proprietary Version Control Supports version control indirectly by relying on Tableau Server or Tableau Online to maintain revision histories of published workbooks. source	Proprietary Version Control No native Git integration. But new text-based format (TMDL) makes it easy for developers to set up manual integration with Git version control. source	Proprietary Version Control Proprietary. Allows one-way Git-based version control through Serialization, available starting with the Pro Plan. source	Proprietary Version Control Proprietary. Pro-code Editor and Card Details supports non-git based version control. source	Git-based Version Control Git integration in Sisense allows for version control of assets through JSON-based file representations. source	Version Tagging Proprietary version control. Allows users to manage, control, and organize multiple iterations of workbooks via Version Tagging. source	Version History Non-Git based version control. Support publishing and restoring dashboards through version history. source	Version History Non Git-based version control with proprietary dashboard versioning and supports environment tagging. source	Limited Version Control Embedded dashboard configurations can be coded and stored to version control system like Git. source

Evaluation Criteria for Embedded Analytics Tools: Our Methods

We’ll provide a detailed explanation of each evaluation criterion, including subtle nuances you might have overlooked in the comparison table.

1. Embedding Methods

Embedding methods affect the customizability of your embedded apps. Typically with higher customization, the tradeoff is higher development effort.

Common embedding methods are:

iFrame: Embeds a pre-built dashboard or report into your app using an HTML iframe tag.
Web Components: Embeds analytics using reusable HTML-based components that work across frameworks and libraries.
API integration: Uses APIs to fetch and render data dynamically, allowing custom-built dashboards within your app.
SDK: Provides a software development kit (SDK), often in a specific programming language or framework (e.g., React SDK), to integrate and customize analytics.

Embedding methods affect the customizability of your embedded apps. Typically with higher customization, the tradeoff is higher development effort. You should evaluate embedding approaches based on your app’s architecture, required level of customization, and available development resources.

Method	Best For	Potential Limitations
iFrame	Quick, simple embedding needs	Limited customization, slower performance
SDK	Feature-rich, tighter integration	Complexity, dependency on vendor updates
API	Full control, seamless experience	High development effort and maintenance
Web Components	Reusable, and encapsulated	Moderate effort

2. Pricing Structure

Most embedded analytics tools offer one of the following pricing structures:

Seat-based pricing: Pricing by embed viewers
Platform pricing: One fixed price (usually quoted and negotiated) for unlimited viewers
Usage-based pricing: Pricing by some usage metric like report runs or active sessions

Additionally, there might be pricing based on feature tiers, and some vendors may offer discounted pricing for your POC period. When evaluating embedded BI tools, consider those with pricing that meets these criteria:

Easy to start with when doing experimental POC
Not becoming prohibitively expensive as more users onboard.

3. Look and Feel

Your embedded analytics tools should allow developers to maintain a branding consistency, deliver customized dashboards, and cater to diverse visualization needs. When evaluating, look into the level of customization for colors, fonts, and layout. Assess the variety of chart types and their customizability.

4. Performance

Tools should ensure reasonable performance, and should scale well with increasing viewers and data volumes. There are typically 2 methods that affect report performance:

Caching: Embedded vendors load data into their own caching layer to speed up serving of reports.
Direct Querying: BI tool querying your database/data warehouse directly.

5. Self-Service Exploration

Embed viewers should be able to customize and build their own reports based on a predefined set of data dimensions and metrics. Viewers should also be able to interact, download, and share custom reports with other embedded viewers.

When evaluating embedded bi tools, you should:

Assess the depth and flexibility of self-serve features like filtering, drill-through, grouping, and visualization options.
Look for embedded report builders and assess how easily users can build reports within the embedded environment, without writing code or asking for help.

6. Security and Compliance

Your embedded BI tool Should protect your customers’ sensitive data and build trust by meeting industry security standards. Common security compliance certificates are:

SOC2: Ensures systems are designed to protect data privacy and security, fostering trust in your service.
HIPAA/BAA: Required for health tech companies, this ensures compliance with healthcare data protection standards.
GDPR: Protects personal data for EU users, ensuring compliance with strict privacy regulations.

7. Permission & User Access Control

Embedded analytics tools should ensure each customer can only see their own data and prevent unauthorized modifications. This is called multi-tenancy. Most embedded BI tools support the basic version of this capability.

When evaluating, look for:

Column-level access control: This allows administrators to restrict access to specific columns within a dataset based on a user’s role or permissions. For example, a sales team might see only aggregated revenue data, while the finance team gets access to granular transaction-level details. This fine-grained control ensures data privacy and relevance at all levels.
Dynamic data sources: This feature points charts to your clients’ respective data sources dynamically to serve similar needs across multiple clients without duplications.

Look into permission levels (e.g., column-level access, row-level access, passsword-protected sharing) and ensure permission settings are intuitive.

8. Maintainability

We think embedded BI should allow the reuse of analytics logic and components across customers to reduce the maintenance burden for developers and product engineers. Common functionalities to support this are:

Semantic layer to allow engineers to define reusable, centralized metrics.
The ability to define logic, metrics, and dashboards using code language (analytics as code).
2-way integration with Git version control.

Community Discussions

Discover what other practitioners are discussing about this topic.

r/BusinessIntelligence

Posted on Oct 2022 View source

Best BI tool for Embedded Analytics

What is the best BI for embedded analytics? The answer is probably subjective, but I'm curious to know what tool I'd need to pick if a company is building customer facing embedded dashboards.

MrPeeps28 Oct 2022

This is an interesting topic with no straightforward answer. There are a lot of factors involved with a decision like this:

• Where does your data live? If you use Google cloud services, you may get better deals/have an easier time using a Google BI product, etc.
• What OS do you and your company primarily use? If most people use macOS, then PowerBI is not a good option for you.
• What is your budget?
• What level of devops and data engineering support will you have?

There are open source tools that work well, but the setup and support might require a lot more engineering on your end. PowerBI is popular but a pain to use on macOS. Tableau/Looker/etc offer user control and embedded functionality built into their software, but the cost of user licenses can get very expensive depending on how many customers you have.

InitiativeOk6728 Sep 2024

You might want to take a look at Holistics.io. It's a cloud-hosted, SQL-first BI platform with strong support for version control (Git) and a software dev workflow (CI/CD, dev/prod environments). The platform is highly programmable, and we offer pixel-perfect dashboards for precise reporting.

We also have usage-based pricing (not user-based) and built-in email/Slack report scheduling. If you're considering alternatives to PBI or Qlik Sense, Holistics could be a solid fit for embedded analytics.

Check out the playground: https://hooli.getholistics.com/

r/BusinessIntelligence

Posted on Nov 2024 View source

Which embedded analytics?

Hi, we'd like to offer interactive dashboards for our customers. Each project will be quite unique, we need a solution that allows us to

jessillions Nov 2024

Metabase does all the things you list (I work here) www.metabase.com/product/embedded-analytics

Based on your requirements and the approx. number of clients needing access, I'd say <$10k is very realistic.

We just recently added an option to use Metabase with built-in storage, so you can also continue working with your data in excel.

r/BusinessIntelligence

Posted on Feb 2022 View source

Which is the best embedded analytics tool for multi single-tenant databases?

Hi all, I'm currently researching for an embedded BI tool for my company use case. We need to provide embedded analytics to our customers (OOTB dashboards + self-serving capabilities).

trafalger Feb 2022

Hello! I'm a domo consultant and I have a client with an extremely similar use case - they're a software company who embeds Domo into their product and gives their customers more reporting using an embedded Domo. They sound extremely similar, they don't have a giant team but do most of the work themselves but reach out to us when they have questions. Only downside is the data refresh depending on how large/complex your data is, 15 mins is the standard refresh time. Happy to setup a quick phone call if you want to know more!

r/BusinessIntelligence

Posted on Apr 2024 View source

Qlik for embedded analytics

I searched far and wide but found nothing. I know how most people feel about Qlik, but what about using it for embedded analytics?

InitiativeOk6728 Sep 2024

Totally get your interest in Qlik for embedded analytics, but if you're open to alternatives, I'd suggest checking out Holistics.io (I work here).
Easy Embedding: You can embed interactive, pixel-perfect dashboards into your application with just a few lines of code. It's designed for a quick setup, usually taking only 30 minutes to an hour.
User Experience: Users can drill down into data and perform ad-hoc analysis, giving them more control and insights. Additionally, our unique canvas layout allows you to create beautiful, customized dashboards that match your application’s look and feel.
Pricing: We offer usage-based pricing, which can be more cost-effective compared to flat fees or consumption-based models, especially as you scale. You get unlimited users and only pay based on consumption.
Check out the playground and experience it for yourself: https://hooli.getholistics.com/

r/BusinessIntelligence

Posted on Oct 2021 View source

Embedded analytics solution for the modern data stack

tl;dr: Questions: Which providers would you recommend / what is your experience with them and does it make sense to combine the BI tool with the embedded analytics solution?

Hi folks,

We are currently searching for an embedded analytics solution and I'm wondering whether some of you might have some insights or advise.

tl;dr: Questions: Which providers would you recommend / what is your experience with them and does it make sense to combine the BI tool with the embedded analytics solution?

Our situation:

Start-up with one mid-size BI team (analytics engineers, web analyst, BI analysts). Most our systems are usually referred to as part of the modern analytics stack (data pipeline tool, dwh, dbt etc) with the expection of the frontend BI tool where we are using PowerBI. We are now looking into embedding our analytics solution into our main application (B2B but >1.000 clients) in order to provide more transparency to our clients. We are not talking about full self service exploration (altough this might come at a later stage) but more showing dashboards and maybe allowing to schedule to receive csv / excel files when they need them. We are hesitant to do that with PowerBI for a couple of reasons:

• Max 8 data refreshes per day if you don't go for the premium option (5k / month) or opt for the somewhat limited direct query option

• No git versioning

• M / Dax staff instead of SQL

• No proper data alerts / scheduling of reports without PowerBI premium

• Way to much logic in the report instead of the DWH, making it slow

• To much stuff is dependent on the MS eco systems in which we neither have the expertise nor are we willing to buy into (sharepoint, azure)

As most of these topics do not just apply to PowerBI as an embedded solution we would replace it completely.

What we need:

• Managed BI solution with the ability to embedded into our main application with good security and a role concept / row-level security (hashed parameters in URLs are fine)

• Good visualization library or the possibility to import d3 or use python

• Good performance (calculations in the dwh)

• Some kind of portal / folder structure where we have present the dashboards to our internal stakeholders (not for our clients)

• scheduling of mails / alerts via slack & mail

What we prefer:

• SQL > No-SQL as the entire team can write it and it's usually easier to read than some drag-n-drop stuff.

• git versioning

• As few logic as possible in the BI frontend, as we would prefer to build that stuff in the dwh (with dbt). This includes a semantic layer for self-service purposes. If the tools brings this in a way that is a good setup (think Looker) than I'm fine but we do not depend on it, as we are not a huge company and there is not a lot of self service or governance required

What we are looking into so far:

• Looker: Seems like a good fit, but it is quite expensive + user based pricing for the embedded solution makes it kind of unattractive from a price stand point. LookML seems nice, but we do not require that kind of governance layer (at least yet)

• Mode analytics: Good combination of notebooks + the reporting and the fact that the pricing for the embedded solution is a package price (instead of user based pricing). Unfortunately the out of the box visualizations and the report design are fairly limited if you don't go into HTML / JS and no self service / scheduling for embedded users.

• Metabase Pro seems potentially to be a good fit but support is quite limited and unsure whether it can compete with the bigger players in the field.

• Sisense: No versioning and user based pricing otherwise could potentially fit as well

• PowerBI: See negatives above, but would require the 5k / month premium option

• Tableau: Similar to PowerBI: No SQL, to much logic in frontend, no versioning, feels optimized for business users and pixel perfect reporting and not the analytics workflow

My questions to the community:

• Does make sense to combine the two use-cases (internal analytics and customer facing embedded analytics) in the same software? Or should we look into something like cumul.io or panintelligence (meaning an embedding focussed solution) + a BI solution.

• Did I miss any relevant vendors that might be able to offer this setup?

• What is your experience with the ones I named? Is our high level evualuation correct or should we take other providers into a PoC?

Thanks!

nvqh Sep 2024

Probably late to the game, but you can check out Holistics.io (I work here).

• Version control with Git. Support software development process (CI/CD, dev prod environment, etc)

• Nice visualizations and flexible dashboarding

• Entire platform very programmable, you can do programming stuff with it

• Cloud-hosted

• Usage-based pricing instead of user-based.

• Email/Slack report schedules

r/tableau

Posted on Apr 2023 View source

Tableau embed cost

My goal is to embed a tableau visualization I made from my tableau server onto my website and allow anyone who comes to my site to be able to view it. I have a tableau server creator's license and when I try to add the embed now, it works perfectly for me, but for everyone else, it wants a password to be able to view it.

86AMR Apr 2023

Tableau recently introduced a consumption-based licensing model for embedded analytics that is meant for external-facing use cases. I think this is what you need. If you are looking for something to truly be free then you will need to look at some of the open-source viz tools that are out there.

https://help.tableau.com/current/online/en-us/licenseproductkeys.htm

getdbt.slack.com: #bi-tools-general

Posted on Sep 2024

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hi all! curious to know what embedded analytics solutions others are currently using is it good and would you recommend it? looking to implement an embedded solution for 1,000-2,000 users would really appreciate hearing thoughts/opinions from people who are using the software rather than vendor calls if possible :sweat_smile: thanks!

S. R. Sep 2024

I'm using Metabase for embedded analytics and it's been great. It's easy to set up and has a lot of features that make it a good choice for embedded analytics.

getdbt.slack.com: #bi-tools-general

Posted on Aug 2023

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Are there any BI tools people can recommend with white label embedding at a low cost?

Jack L. Aug 2023

We have tried quite a few tools for this. In production used Tableau but ran into issues: performance, dev complexity (write in desktop, not actual code!) even security bugs (leaking other customer data in filter choices!). Now we do double approach: in parallel implement prototypes with Quicksights, and for real average end-user use dont relay on any ready BI, but build "pixel perfect UX" in code using lower level UX dev (react) components. There are opensource ones, and we have created own inhouse set on top of these. One key learning is that the BI drill-downs and other fancy things are just too complex for embedded use cases in case of beginner and average users. Advanced users anyway dont want any UX, just "can i download my data" so they will use their looker/tableau/dwh stack or whatever.

r/BusinessIntelligence

Posted on Feb 2019 View source

Looker vs Sisense for embedded analytics?

Hey all, looking for some insight from those with experience of Sisense and Looker specifically in the world of embeddeding & monetizing within an enterprise web application. Here are some key things I’m looking for:

GeneralDouglasMac Feb 2019

Of the two: Sisense has the most functionality in multi-tenancy, row level security, and embedding. It is a bit dull on it's standard package but function over form there. Another suggestion I would consider (if you're asking) is Tibco Spotfire. Row level security, excellent embedding and server, the visuals are nothing to sneeze at at first but if you enable the D3.JS repository boy are the doors thrown wide open! It has about as capable GIS capabilities as any tool I've yet to toy with. I would NOT suggest DOMO. It has fallen by the wayside in support, feature matching, and costs compared to every other tool out there.

r/BusinessIntelligence

Posted on December 2023 View source

Embeddable BI? Experts help me out

Hi all, I build and run a SaaS company. My customers want to analyze their data. I've built some charts for the most common analysis, but ideally I let the users build and manage their own visualizations and BI views. Does anyone have any recommended tools that might fit:

RomanSingele November 2023

Power BI Embedded with the A SKU (pay as you go) might be for you. You can scale up/down and even pause if you don't need it 24/7. The API let you do pretty much everything and you can play around with it in the playground.

r/BusinessIntelligence

Posted on April 2024 View source

Help choosing a BI platform

Hi! My company is exploring a BI platform, ultimately to be embedded and geared toward external users.
The background is we have a product that generates a lot of data and allows users to explore it in a very specific way (think interactive video content based on an AI/computer vision pipeline on video) but there is no way for users to explore aggregate results (they can interact with particular videos but not see how many times X happened for example over a month worth of video)

Hi! My company is exploring a BI platform, ultimately to be embedded and geared toward external users. The background is we have a product that generates a lot of data and allows users to explore it in a very specific way (think interactive video content based on an AI/computer vision pipeline on video) but there is no way for users to explore aggregate results (they can interact with particular videos but not see how many times X happened for example over a month worth of video).

We want to give users these aggregates and allow them to filter, drill-down, etc. After some user research, wireframing, etc, we realized what we want to build is basically a BI dashboard. The hypothesis is that using a BI platform we can have a much faster time-to-market or at least time-to-feedback and get the data in people's hands, quickly iterate on different dashboards, etc. And eventually when we have the product figured out, we embed it in our app and build out the frontend. Perhaps after that, if the product is stabilized, we eventually build out our own backend and move way from the BI to save costs. That last part is just a hypothetical, we'll see.

B2C enterprise software, so perhaps 100 users first year (alpha/beta) and 1000 second (MVP). We haven't decided if we will allow external users to create their own dashboards or we just iterate internally (depends on ease of use) but if not then the dashboard must be somewhat flexible (add/remove columns from a table for example depending on metrics of interest).

For context we are about 15 people in engineering but we have lots of other priorities so setting up the data model then letting product iterate on dashboards with our user research group is highly attractive. Compared to the way we usually build apps which would be design-first rather than design-last. Data is in BigQuery and our webapp we want to eventually embed in is React.

We work closely with GCP and when they heard about this project they pushed Looker quite hard and paid their local integration partner to build the LookML for us and an initial dashboard. We're still not quite convinced given the price tag and commitment they want, while not having tested any alternatives. Our current idea is to build a POC with Looker at the same time as an alternative before making a long-term commitment. So I'm looking for thoughts on what other platforms to test.

Some requirements might be

• options for embedding / external use case well-supported

• connection to BigQuery

• mature usage metrics

• basic dashboard is just going to be a table with some charts, but the specifics is where it gets complicated

• radar chart is important but not sure the default one in Looker will work (we need a differet kind of normalization) so that may need to be custom regardless of platform

• other important charts are for visualizing 2D data (think points on the screen) like heat maps, scatter plots, arrows. It's important that we can load a custom image for the chart background to plot over, replacing the default x/y axes

• it must be possible/easy for filters to filter each other. What I mean is, imagine two filters "Streaming service" and "Series". If the user first selects "Netflix" then they should only see Netflix series in the second filter. I'm not sure if this kind of thing is common or not.

• We want to put links in tooltips or elsewhere, to our video in another platform, but I think that can be done with some HTML formatting

The most important factor is time-to-MVP. If I tell the CEO one approach will save 50% annually but will take two more months to get to market then he will probably say to go for the more expensive but faster approach. We may not agree with that strategy but it is what it is... We have a head start with Looker and some budget from Google for custom development but I see many challenges ahead.

What do you think? What should be our second POC? I've looked at some alternatives but we always find a limitation. Or are we wrong for looking at BI and we should just build it with D3 or whatever?

burdenedwithpoipous April 2024

I don’t think this is asking for “a lot of free work”. This is very common and OP is smart to have realized, at least realized there’s some potential value, to buying over building. The most common, is why get in the business of maintaining the analytics, as an almost entire new product line, when your engineers are building your core product and so many vendors do BI well?

We went through this, and here is what we learned:

• never used Looker. But the LookML niche skillset and lack of resources in the market was a deal breaker for us.

• PowerBI was a cheap alternative to start. Not to different from LookML, the DAX become a bit unwelcoming but it worked for 100 users. When you scale, the feedback and requests become way more and you raised the important point of a potential future where you’ll let your customers be more self serving?

• we then turned to Tableau. While the price point was higher, it’s no doubt the easiest to use BI software. The talent market is massive, we were able to iterate quicker, and we have an upsell offering of self service analytics where we can still govern and control the data. Which is our IP. Really helped us serve more customers without bottlenecking our engineers.

You’ll have to explore the PowerBI to Big Query connection yourself. Not sure how that’s materialized recently considering the Google / MSFT competition and MSFT seems to be pushing Fabric really hard

r/BusinessIntelligence

Posted on June 2021 View source

Free / open source or affordable single sign-on embedded analytics solution

Does anybody know of an affordable solution for embedded analytics that can be authenticated through your own application via the URL in an iframe or in some way where I can use embedded analytics and only have the data linked to the current logged in app user shown on the embed?

Grovbolle June 2021

Metabase perhaps or Apache Superset

r/BusinessIntelligence

Posted on July 2020 View source

Finding hard to zero in on the right platform for embedded analytics. Which of the following would you recommend?

We are using tableau now for embedded analytics, to share with our customers. We found the following issues:

Financial_Forky July 2020

I'm a Power BI guy. I interact with Tableau reports in our organization, but I've never used it to build one. While I like Power BI because it's what I know and am good at, both platforms should be able to handle 100+ million rows of data. Most of the modeling I do is on tables ranging from a few million up to 80 million rows, although I know Power BI is used successfully on data sets over 1 billion rows, but I suspect Tableau can, as well.

However, when you get to data sets of that size, you need to put a lot of thought into how you build your model. Are you following good star schema principles? Are you writing highly efficient measures and really understand the nuances of how different functions work? Sloppy code will work just fine on a 10,000 row table, but can grind to a halt at 10's or 100's of millions of rows.

My suspicion is that your performance issues aren't Tableau's fault, but rather just an indication of a sub-optimal schema and report design. It's entirely possible that a different technology may "fix" these problems for you just by the nature of how it is designed, but it could introduce new issues, as well.

I'm not specifically advocating that you switch to Power BI; I'm just suggesting that before you drop Tableau, you confirm that your performance bottleneck is Tableau itself, and not just your level of understanding of Tableau.

locallyoptimistic.slack.com: #discussion

Posted on Sep 2023

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Katie B.

What are folks' favorite tools for building external-facing dashboards?

Dylan W. Sep 2023

If this is "whitelabel embeds" vs publicly accessible, I've found all the "name brand" BI tools do this.

Honestly, the decision criteria for choosing are basically the same as a BI tool (SQL vs modeling, pricing, viz options).

The additional criteria being

• How much does customizability of branding matter to you

• How does their auth/sso/permissioning lifecycle work, and can your dev team handle it?

I'm using Metabase for embedded analytics and it's been great. It's easy to set up and has a lot of features that make it a good choice for embedded analytics.

getdbt.slack.com: #bi-tools-general

Posted on Aug 2023

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Stephen E.

Are there any BI tools that do embedded analytics in mobile apps?

Lee August 2023

Tableau used to / still has a framework with examples embedding in mobile apps, but as above its just a web element.

There are nuances to embedding for touch devices, the BI tool has to cater for the fact that you're not using a mouse to interact so actions like click and drag or right click need to be considered by the BI tool.

10 Best Embedded Analytics Tools: An Overview

All the embedded analytics platforms in this comparison meet our criteria to some degree, which might make your evaluation process a bit muddy.

We also understand that embedded analytics isn’t the only thing on your data team’s to-do list. You might want to create board-ready reports or empower your non-technical users to build their own reports. In this section, we’ll highlight what makes each tool different besides its embedded analytical capabilities, alongside with their limitations, making it easier for you to choose one that fits your BI teams.

Disclaimer: This section is written with a few biases and preferences.

Embedded business intelligence and data analytics are fields that have been around for over 50 years, so clearly there are multiple approaches to designing embedded analytics tools.

Everyone will have their own preferences and biases. We do as well. Here are the biases that have shaped how we wrote this section:

Embedded BI should be easy to maintain programmatically. A poorly maintained BI environment kills user trust and buries BI teams in admin tasks. While many BI tools make it easy to build reports, not many of them design their software with BI maintainability in mind.
Embedded BI should be reusable. Tweaking analytics for every customer slows you down. Your BI tools should support reusable chart components so you can create similar dashboards across tenants without starting from scratch.

To keep things consistent and enjoyable, we’ll assume you’re on board with these biases for this section.

That said, we’re open to feedback! If you have any thoughts, share them with us using this form.

1. Holistics

Holistics is a BI platform that helps data teams set up self-service BIs that are reliable and easy to maintain. It offers a code-based semantic layer for managing analytics logic and a drag-and-drop interface for data exploration.

What makes Holistics different, compared to other embedded BI tools, is its programmable BI approach. For Product Engineers, this approach meant they could build a custom reporting experience and integrate it into the product without compromising software development best practices.

Besides embedded analytics, this approach also brings several unique capabilities:

Analytics as code: Every component of the analytics logic, from data models to dashboards, can be described as code, and versioned in Git, allowing for full programmatic control and customization. This is complemented by user-friendly interfaces for less technical users.
Fully customizable visualization and dashboard: A code-based canvas dashboard experience that provides users with complete control over the layout, styling, and interactions of their visualizations and dashboards.
Developer-first experience: Holistics provides a powerful IDE-like environment, enabling analysts to work with analytics code with ease, leveraging features like auto-completion, syntax highlighting, and debugging tools.
Reusable BI Components: All BI components, from visualizations and dashboards to data models and metrics, can be parameterized and reused across the organization, fostering consistency and efficiency.
Robust CI/CD and extensive API support enable powerful integration and automation that streamline BI operations while maintaining governance and consistency

Pricing: Holistics’s embedded analytics solution starts at $800/month and comes with unlimited viewers, unlimited reports created, and all functionalities included.

2. Tableau

Tableau is well-known for its powerful visualization capabilities, and its embedded analytics platform offers the same high-quality visuals people expect.

Sample Embedded Dashboard in Tableau

Notable Features:

Robust Embedding API: Integrates Tableau visualizations into applications with a REST API for user and content management.
Outstanding Visualizations: Converts complex datasets into interactive, visually stunning dashboards.
Active Community: Offers a supportive environment with AMAs, forums, and expert interactions.

Potential Limitations:

Proprietary Version Control: Tableau uses its own proprietary file formats - so there’s Git-based code version control/change process management. One person editing locally and pushing up can overwrite everyone else, making it difficult for a team to manage.
No Centralized Metric Repository: With Tableau, it’s easy to end up recreating the same metrics with different calculations in different places. As the number of reports grows, metric definitions might become more disparate and inconsistent, making it difficult for the data team to maintain accuracy across multiple reports.

3. Power BI Embedded

Power BI’s embedded analytics is rich in functionalities, allowing customers to lodge dashboards and reports into their existing applications. For enterprise companies that are already hooked to the Microsoft ecosystem, PowerBI’s embedded analytics solution is a natural choice.

Sample Embedded Dashboard in Power BI Embedded

Notable Features:

Seamless Microsoft Integration: Power BI Embedded Analytics is part of the Power BI suite and integrates smoothly with other Microsoft products, making it an excellent choice for organizations already using Microsoft tools.
Advanced Calculations with Dax: Power BI’s DAX (Data Analysis Expressions) enables advanced calculations, time intelligence, and custom measures for dynamic, interactive reports. It’s optimized for large datasets, ensuring high performance while maintaining flexibility and consistency in analytics.
Power BI’s Copilot: Power BI Copilot uses AI to assist users in building reports, generating visuals, and uncovering insights through natural language prompts, making it easier for users to explore and understand their data.

Potential Limitations:

Dependency on Microsoft Ecosystem: While it integrates well with Microsoft products, its value may be diminished for organizations using non-Microsoft technologies or requiring more advanced customization options.
No Git Version Control: PowerBI’s workflow is not designed to have code checked into Git repository, making it difficult to manage changes, perform code branching, and maintain the accuracy of analytics logic.

4. Looker

Similar to Holistics, Looker has a code-based modeling layer with self-service data exploration.

Looker’s embedded platform offers all of its core features, including:

LookML’s Modeling Layer: LookML (Looker Modeling Language) is the language that is used in Looker to create semantic data models. You can use LookML to describe dimensions, aggregates, calculations, and data relationships in your SQL database. LookML allows BI teams to centralize metric definitions, and ensures consistent, reusable datasets.
Data Models as Code: Looker allows data models to be defined as code and versioned via Git, facilitating reliable management, collaboration, and deployment of data models.

Sample Dashboard in Looker Embedded

Looker Limitations:

Steeper Learning Curve: Looker’s extensive features and customization options come with a steeper learning curve, which may be challenging for organizations without advanced data expertise.
High upfront cost: Looker’s high upfront cost might make it prohibitive for smaller organizations to scale their embedded analytics access.
Limited Visual Customization Options: While Looker provides extensive options for customization, its out-of-the-box visuals are somewhat limited. You’ll need to invest time into designing your embedded visualizations to ensure they meet your specific aesthetic and user experience requirements.

5. Explo

Unlike other BI tools in this list which offer both embedded and internal analytics, Explo specializes in creating white-labeled, embedded dashboards that seamlessly integrate into any application or website.

Sample Dashboard in Explo

Notable Features:

No-Code Setup: Explo’s no-code setup allows for rapid deployment of embedded analytics solutions, minimizing the need for extensive developer involvement and enabling teams to get up and running quickly.
No-code Interface: Explo’s no-code interface allows embedded viewers to easily interact and explore data.
AI-Assisted Report Builder: The platform features an AI-assisted report builder that suggests relevant fields and data points, helping users create reports more efficiently and tailored to their needs.

Potential Limitations:

Feature Depth: While Explo excels in simplicity and speed, it lacks the robust data modeling, complex analytical operations, and integration options offered by more comprehensive BI tools like Tableau or Looker. Organizations needing a dual-purpose tool for both internal and customer-facing analytics might find themselves relying on additional platforms to fill the gaps.
Lack of Row-Level Access Control: Explo does not include row-level access control, which may be a drawback for businesses requiring granular data security and multi-tenant environments.

6. Luzmo

Luzmo is an embedded analytics platform that puts a strong emphasis on elegant visualization and an API-first embedding approach.

Notable Features:

Luzmo IQ: Luzmo leverages AI-driven tools to provide insightful recommendations and let embedded viewers get answers to their data questions using natural language.
Embedded Dashboard Editor: Luzmo enables embedded viewers to customize dashboards directly from a template, allowing them to tailor charts, layouts, filters, and visualizations to meet specific needs.
Luzmo Flex: Luzmo Flex is a data visualization software development kit (SDK) designed for developers to create custom data products directly within their applications.

Sample Embedded Dashboard in Luzmo

Luzmo’s Limitations:

Feature Depth: Similar to Luzmo, as Explo is built for embedded analytics, it lacks the robust data modeling, complex analytical operations, and integration options offered by dual BI tools like Tableau or Looker. Organizations needing a dual-purpose tool for both internal and customer-facing analytics might find themselves relying on additional platforms to fill the gaps.
Non-Git-Based Version Control: The platform uses a non-Git version control system called Version History. While it tracks changes and allows reverts, it lacks Git’s advanced features, which might be a hassle for product engineers used to Git workflows.

7. Sigma Computing

Sigma Computing is a data analytics platform with a spreadsheet-like interface, offering embedded analytics solutions beside its core BI functionalities.

Sample Embedded Dashboard in Sigma Comuting

Notable Features:

Spreadsheet-Like Interface: Sigma Computing features an intuitive, spreadsheet-like interface, making it accessible for users familiar with Excel and easing the transition to advanced analytics.
Embedded Data Exploration: The platform allows users to explore data dynamically, perform ad-hoc analyses, and drill down into metrics directly within the embedded environment.
Granular Role-Based Access Controls: Sigma provides detailed user permissions with role-based access controls, supporting both single and multi-tenant environments. This ensures precise management of user access and dynamic role switching.

Limitations:

Customization Complexity: While Sigma allows for customization of the user interface and theme, advanced customization may require additional effort compared to other platforms with built-in features.
No Git-based Version Control: Sigma Computing doesn’t offer native “analytics as code” or direct Git version control integration. Even though Version Tagging is available to manage workbook versions proprietarily, the lack of Git’s branching, merging, and change tracking can make it harder to maintain analytics workflows.

8. Sisense

Sisense provides a flexible, code-based approach to embedded analytics. If you’ve been on the lookout for embedded analytics platforms, Sisense is a name you’ve probably come across. This is well-deserved as they’ve put a lot of thought into multi-tenancy and slicing data on different levels for access control.

Notable Features:

AI-Powered Analytics: The platform includes AI and machine learning features that provide predictive analytics and uncover hidden trends.
Efficient Data Processing: Sisense’s Elastic Data Engine is designed to handle large, complex datasets from multiple sources, making it ideal for organizations with significant data processing needs.
Customizable Embedding: Sisense offers robust embedding options with a fully customizable API and Compose SDK, allowing developers to tailor the look and feel of dashboards to match their in-app branding and user experience.

Potential Limitations:

Higher Cost: Sisense’s pricing model can be expensive, particularly for larger deployments or highly customized solutions. Sisense pricing starts from $21K per year.
Focus Shift to Embedded Analytics: Sisense’s transition from a code-first visualization tool to an embedded analytics platform has reduced its focus on SQL-first capabilities and internal BI tools. This shift has made it less suitable for users who prioritize reusable, standardized models for internal analytics and reporting.
Limited maintainability: Sisense lacks built-in features for managing analytics as code in a programmatic and version-controlled manner. Unlike tools that let you define metrics, models, or analytics entirely as code for reuse and a single source of truth, Sisense primarily operates through its GUI and embedded workflows.

9. Metabase

Metabase is an open-source BI tools, suitable for SMEs who want to quickly build embedded analytics or customer-facing data products into their applications.

Notable Features:

User-Friendly Interface: Metabase is known for its intuitive, easy-to-use interface, making it accessible for embedded viewers to create and explore dashboards.
Quick Setup: Metabase is straightforward to embed, and supports both iframe and API embedding.
Powerful Query Builder: The platform includes a robust query builder that simplifies data exploration.

Sample Embedded Dashboard in Metabase

Potential Limitations:

Limited Visualization Features: The platform struggles with displaying charts that have a lot of series, making it less suitable for visualizing complex or high-dimensional data.
No Code-based Version Control: Metabase lacks 2-way Git-based version control, making it difficult to track changes or roll back updates. This can lead to inconsistencies and errors that undermine trust in the analytics environment.

10. Domo

When it comes to embedded analytics, one platform that’s often mentioned is Domo. Domo is a feature-rich BI tool built for teams that need seamless embedded analytics and advanced data visualizations

Notable Features:

Comprehensive Data Integration: Domo stands out for its ability to easily connect with over 1,000 data sources, ranging from cloud-based applications to on-premise systems.
Collaboration Features: With built-in collaboration tools, Domo makes it easy for teams to share insights, comment on dashboards, and work together in real-time.
Mobile-First Analytics: Strong support for mobile analytics, enabling users to access dashboards, reports, and insights on the go through its mobile app.

Potential Limitations:

High Cost: While Domo offers extensive features and ease of use, it comes at a higher price point compared to other tools. The pricing structure can also become complex as your needs grow, especially with additional users or data sources.
Limited Customization for Developers: Developers might find its customization options more restrictive compared to more developer-friendly platforms like Looker or Holistics, which offer analytics as code capabilities. This can limit the flexibility needed for advanced custom analytics solutions.