The Best Embedded Analytics Tools: A Detailed Comparison Guide

Data analytics turns scattered data from different tools and systems into useful dashboards and visualizations. It works by pulling data from multiple sources, cleaning and organizing it, and then identifying patterns or connections. Embedded analytics refers to this same process, but it is integrated directly within another software product or web portal.

What is the benefit of embedded analytics?

Embedded BI and analytics bring the power of data right where your users need it — inside the tools they already use. Instead of switching between platforms or waiting on reports from the data team, users get instant insights within their everyday workflows. This makes decision-making faster, smoother, and a lot more intuitive. Plus, it’s a win for product teams, adding real value to your app without the headache of building analytics from scratch.

If you’re looking for an ideal embedded analytics solution, it’s important to consider the different types of embedded BI tools out there. To help with that, this ebook compares the top embedded analytics solutions across the analytics and BI market.

Types of embedded BI solutions

Before we can compare the best embedded BI tools, it is important to understand the different ways analytics can be embedded. For comparison purposes, here is an overview of how embedding can be achieved:

• Basic embedding via IFrame: Copy and paste HTML code into the end user’s application or web portal.
• Web Components: Behaves as a wrapper to provide simple integration of advanced analytics with minimal coding.
• Advanced embedding using SDK: Embed collections of pre-built dashboards and visualizations into end-user workflows using libraries (such as React SDK or JavaScript SDK).
• Embedding programmatically: Integrate dynamic visualizations or custom visualizations using third-party libraries and the provider's SDK – without directly accessing the provider's interface.

Other ways of customizing and embedding might rely on plugins. These enhance dashboards with extra features like images, comment sections, or charts from other BI tools. The enriched visualizations are then seamlessly integrated into end-user workflows.

To learn more about embedded analytics methods, their pros and cons, and to help you decide which to choose, check out Which Embedding Method is Right for You?

Comparing the top embedded tools

Choosing the right embedded BI tool is essential for driving business growth and ensuring both customer and vendor satisfaction. The ideal solution seamlessly integrates visualizations, dashboards, or even full user interfaces directly into the user's workflow, eliminating the need to toggle between multiple applications and keeping the experience smooth and efficient.

At-a-glance comparison of how the leading analytics tools approach embedding

To help you evaluate the options, the table below outlines the embedding methods of the best BI tools for embedding analytics currently on the market.

*Plugins are typically built by developers according to a specific need. They can easily be added to dashboards. This more flexible method can complement iFrame and full-dashboard embedding.

IFrames are a popular way to embed content and can be used with many analytics platforms. However, the above-mentioned analytics vendors also offer more advanced embedding options.

GoodData

GoodData supports Web Components — which is a step above IFrames in terms of flexibility. It ensures the seamless integration of GoodData analytics into web applications by transferring the analytics content in custom HTML tags. GoodData.UI is a React-based framework for crafting custom analytics within React applications. It offers a deeply integrated and fully tailored analytics experience (you can also embed your own or pre-built dashboard plugins).

Tableau

Tableau Embedding API allows developers to integrate Tableau's interactive reports and dashboards into web applications. Meanwhile, Tableau's JavaScript API enables the embedding experience to be customized. A list of ready-to-use dashboard plugins is also provided.

Power BI

This solution has its Power BI Embedded service, a Microsoft Azure offering that allows developers to embed interactive Power BI reports and dashboards into applications. The service supports client-side embedding with JavaScript and server-side embedding. Developers can create their own custom visuals using the Power BI Custom Visuals SDK.

Looker

Looker enables seamless integration of its dashboards into web applications. Looker Embed SDK provides extensive customization options, which offer a web-component-like experience when combined with custom elements.

Sisense

Sisenseoffers Embed SDK for customized embedding of separate visuals. SisenseJS can be used for embedding individual dashboards, widgets, and filters directly into web apps. Meanwhile, Compose SDK can create customized visualizations and other elements — with a list of Sisense plugins also available.

AWS QuickSight

This software enables interactive dashboards and visualizations to be embedded into applications via AWS QuickSight SDK and API. This makes it easy to share analytics within web and mobile applications.

Domo

Domo Everywhere is an embedding solution that allows users to integrate Domo's dashboards and cards into web applications, portals, or websites. The Domo App Store is also available for embedding custom dashboard plugins.

Qlik

This software offers embedding options with JavaScript libraries. There is also Nebula.js, a collection of libraries and components for developers that can be used to create web component-like embedding for Qlik Sense visualizations. Qlik also offers a list of dashboard plugins to be embedded in Qlik Garden.

If this embedded analytics comparison is insufficient, check out Comparing the Best BI Tools: A Comprehensive Breakdown. In addition to embedding methods, you’ll learn about other key capabilities.

Key features of a top embedded analytics platform

Embedded analytics for SaaS prioritizes fast deployment, cost efficiency, scalability, and seamless integration with your product to enhance the success of SaaS offerings. When selecting the optimal embedding BI tool, consider key features such as self-service visualizations, customizable dashboards, scalability, and competitive pricing.

Self-service visualizations

Self-service visualizations empower end users to produce captivating, interactive visuals through an intuitive drag-and-drop interface, eliminating the need for a data expert.

Embedded Analytics Provider	Ease of Creating Reports/Dashboards	AI-driven Features for Simplified Data Exploration	Self-service Support
	Low-code/No-code, user-friendly.	Yes	High
	User-friendly, easy drag-and-drop.	Yes	High
	Simple drag-and-drop but can be cluttered.	Yes	Moderate
	Less intuitive for non-technical users.	Yes	Low
	User-friendly for basic tasks, complex for advanced customization.	Yes	Moderate
	User-friendly, easy drag-and-drop.	Yes	High
	User-friendly, easy drag-and-drop.	Yes	High
	User-friendly, easy drag-and-drop, no-code.	Yes	High

Highly customizable dashboards

Customization options are crucial to embedded BI. Users need to be able to finely tailor the appearance and functionality of dashboards (or visualizations) to seamlessly integrate them into the existing interface.

Embedded Analytics Provider	Range of Visualization Types	Customization Beyond Standard Options	Overall Flexibility
	Wide range of visualizations.	Full customization available with React SDK; includes out-of-the-box visualizations.	High
	Wide range, but customization can be difficult.	Customization can be challenging; limited advanced integration options.	Moderate
	Wide, akin to Excel.	Some visuals have limited customization options.	Moderate
	Basic visualizations with lack of customization options.	Limited; primarily serves as a wrapper around iFrame.	Low
	Standard types.	Allows detailed customization through code.	High
	Standard types.	Customized dashboards are limited.	Low
	Standard types.	Less flexibility in customization due to lower prioritization by Domo.	Low
	Standard types.	Limited customization; mainly through IFrames and JavaScript libraries.	Low

Scalability

In embedded analytics, scalability ensures the system can handle growing data volumes and more users as the business expands (without compromising performance). To successfully scale, a BI solution requires seamless integration with end-user applications, ease of analytics delivery, change management, and cost-efficient resource use. All of which help to ensure analytics capabilities grow in line with business needs.

Embedded Analytics Provider	Scalability Approach	Management and Provisioning
	Shared semantic layer, workspace hierarchy, and inheritance model. Child workspaces inherit settings from the parent workspace.	Automated provisioning of child workspaces. Seamless updates across thousands of user groups, preserving customizations.
	Requires use of Tableau Cloud or Server for embedding and sharing.	Dashboards localized on devices via Tableau Desktop can lead to multiple conflicting versions of the same metrics – as various versions may exist across different user devices.
	Managed through service principals or master accounts.	Limited by a 1,000 workspace cap, which can affect scalability.
	Separate instances provisioned for scalability.	Scales efficiently for enterprise access, offering similar flexibility to GoodData.
	Sometimes dashboards created for individual customers are required.	Dashboard inheritance challenges; additional effort may be required for custom dashboards.
	Scaling requires integration with additional AWS products.	Scalability contingent on understanding and managing AWS data roles.
	Separate instances provisioned for scalability.	Similar to GoodData.
	Separate instances provisioned for scalability.	Similar to GoodData.

Pricing

An embedded BI solution’s pricing model should focus on user access and interaction within a single-user environment. Ideally, the model shouldn’t require payment for each individual user or be consumption-based. Below we outline the pricing models of the different vendors.

Embedded Analytics Provider	Pricing Model	Limitations	Cost Predictability
	Per-workspace	No restrictions on users or workspaces. Unlimited users with different permissions.	Transparent pricing, predictable for future expansions.
	Usage-based	Limited options for more advanced integration and use cases.	Costs rise with increased user interaction, making future costs unpredictable.
	Node type and number of nodes	Limited computing resources and capacity for data loaded.	Costs for additional nodes and faster data processing.
	Based on number of separate instances	Tiers include a set number of users, upgrades, and queries. Exceeding tiers incurs extra costs.	Transparent cost calculation based on number of instances.
	Customized based on use case	No public information available for this comparison.	No public information available for this comparison.
	Per-session	Costs vary between author and reader roles.	Using Amazon CloudWatch for tracking and cost management; more user interaction increases costs unpredictably.
	Consumption-based	Requires credits for platform usage.	DomoStats monitors usage; costs increase with usage, making future costs unpredictable.
	Consumption-based	Restricted by data storage and feature availability.	Future consumption and costs are unpredictable, scaling with data volume.

Top Tip: Evaluating an embedded analytics tool’s AI features

Pay close attention to the AI-powered features a tool offers as this can make or break the platform’s long-term value. Look for capabilities like natural language querying, automated insights, and predictive analytics, which empower users to explore data without needing deep technical expertise. Smart recommendations, anomaly detection, and AI-driven alerts help teams stay ahead of trends and catch issues before they escalate. Even more powerful are tools that support machine learning model integration or auto-generated dashboards tailored to specific roles or metrics. The right combination of AI features can turn your analytics into a proactive decision-making engine.

Check out our other comparisons of top GoodData alternatives

The resources below provide further analysis of how we compare to leading GoodData alternatives:

AWS QuickSight Alternative: Choosing the Right BI Tool for Your Needs

Sisense Alternative: Elevate Your Analytics Game with Dynamic Solutions

Tableau Alternative: Addressing Challenges and Solutions for Modern Analytics

Qlik Alternative: Which tool is better for your analytics?

Looker Alternative: Which BI Tool Better Fits Your Business Needs?

Domo Alternative: Discover the Right BI Tool for Your Needs

Power BI Alternative: Finding the Best Fit for Your Business

Next steps with GoodData

Still unsure whether to choose GoodData? Get a free GoodData trial for some first-hand experience. Have questions or want to see real-time use cases? Request a demo for a platform walkthrough.

*Note: The above evaluation of features is based on our best understanding of publicly available information available at the time of publishing (Apr ‘25). To understand more specific details and feature differences, readers are encouraged to perform their own research. All of the product names, logos, and brands used are for identification purposes only and remain the property of their respective owners. Use of them does not imply any affiliation with or endorsement by them.*