Best Embedded Analytics BI Tools: A Comparison Guide

Top 10 Embedded Analytics Tools: Strengths, Limitations and Pricing

All the embedded BI tools 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.

01. 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.

02. 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 for integrating Tableau data visualizations into applications, a REST API for user and content management
• Outstanding visualizations and broad accessibility. Tableau enables users to transform complex datasets into interactive, visually stunning dashboards with just a few clicks.
• Active community The Tableau community is vibrant and highly supportive, making it a standout advantage over other platforms. With resources like AMAs and interactive forums, users can connect with experts, learn new tricks, and find inspiration for their projects.

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 - often 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

Power BI Highlight 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.

Power BI 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.

5. 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.

6. Explo

What makes Explo different is its dedication to simplicity and rapid deployment, making it a good choice for businesses that need to deliver customer-facing analytics quickly. Unlike other BI tools 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.

7. 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.

8. 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.

9. 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.

10. 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.

Read more: Metabase Pros, Cons and Best Alternatives

Pricing: Metabase’s cloud licenses start from $85/month.

11. 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.

Pricing: Domo pricing is not publicly available, and you need to reach out to their sales team for a personalized quote. However, it’s been reported that Domo pricing is around $134,000 annually.