Best Embedded Analytics BI Tools: A Comparison Guide
Last updated: January 21, 2025
If you want to skip this introduction, which covers our approach to creating this embedded BI guide, feel free to jump to any section below.
If you run a SaaS product, you need embedded analytics.
It’s a strategic imperative. In-product analytics give your customers the data they need to track product usage, measure ROI, and get more value from your product. For you, it’s a chance to open new revenue streams through data monetization and add a competitive edge to your offering.
Yet, after talking to over 350 analytics teams, we learned a hard truth: There’s no perfect embedded analytics tool. (As a BI vendor, we’d love to tell you we’re the flawless solution, but that wouldn’t be honest).
There will always be trade-offs. The stakes are high: get it wrong, and you’re left with frustrated customers, overburdened developers, and an analytics feature that fails to deliver on its promise. The options can feel overwhelming. Do you go for a tool that’s highly customizable but comes with complex setup requirements? Or one that’s simple to integrate but might not scale with your growing needs?
This left you with two choices:
- Test every embedded BI tool. Learn the good and the bad, and the inevitable trade-offs. This takes months - and your users likely don’t have that kind of patience.
- Do your research online, only to get lost in a sea of buzzwords, marketing jargon and biased opinions.
This is why we want to do the heavy lifting for you. In this article, we’ll guide you through the essential features to look for in embedded analytics tools and compare the most popular embedded BI tools on the market.
We try to be as objective as possible. Here are some principles that have shaped how we build the embedded BI comparison table below.
- We prioritize hard facts over opinions.
- Every piece of information comes straight from official sources and documentation.
- We don’t push conclusions or recommendations. The comparison table is designed to help make an informed decision, faster.
The embedded analytics tools compared in this document are: Holistics, Looker, Tableau, Power BI, Metabase, Domo, Sisense.
If you find any inaccuracies or have suggestions for improvement, please let us know using this form
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.
Embedded Analytics BI Tools: A Detailed Comparison Table
Scroll to the right to see more
| Dimension |  Holistics |  Looker |  Tableau |  Power BI |  Metabase |  Domo |  Sisense |
|---|---|---|---|---|---|---|---|
| Demo Playground | |||||||
| | Not available | Available | Available | Not available | Not Available | Available | |
| Pricing Structure for Embedded 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 BI tools have pricing structure that scales with the number of embed viewers or usage of your application. Common pricing structures are:
| |||||||
| | 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 credit-based pricing for 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. |
| | Pricing Transparency Transparent for standard plans. Contact sales for embedded pricing. | Pricing Transparency Opaque. Requires speaking to sales. | Pricing Transparency Transparent for standard plans. Contact sales for embedded pricing. | Pricing Transparency Transparent for embedded pricing. | Pricing Transparency Transparent for embedded pricing. | Pricing Transparency Opaque. Requires speaking to sales. | Pricing Transparency Opaque. Requires speaking to sales. |
| 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 embedding Provide sandbox UI for generating the iFrame backend code (source) | iFrame 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) | iFrame 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) |
| | SDK embedding Not Available | 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) | SDK Embedding Not Available | SDK Embedding Supports embedding analytics with SisenseJS using a component-based approach, in beta (as of Jan 2025). (source) |
| | API embedding Not Available | Embed API Provides an Embed API that allows developers to programmatically control embedded content, such as dynamically generating embed URLs and interacting with Looker content within applications. (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) |
| | Allow users to create a "shareable link" of a dashboard, and embed it directly to websites. (source) | Public Embedding Allow users to share data visualizations publicly via embeddable URLs (source) | Public Embedding Through Tableau Public, allowing users to publish visualizations to a publicly accessible server (source) | Public Embedding Via "Publish to Web" option, which generates a publicly accessible embed URL for sharing reports without authentication. (source) | Public Embedding 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 Supports external sharing by generating public dashboard links with customizable filters. (source) |
| Permission & User Access Control 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 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) |
| | Dynamic Data Sources Allow connecting to different data sources (databases) based on different customers (tenants). (source) | Dynamic Data Sources Not supported | Dynamic Data Sources Not supported | Dynamic Data Sources Not supported | Dynamic Data Sources Not supported | Dynamic Data Sources Supports Dataset Switching that allow users to dynamically change the underlying data of embedded dashboards. (source) | Dynamic Data Sources Through Dynamic Elasticube, which dynamically determines the appropriate data source for powering a given dashboard. Supports only Windows and Linux. (source) |
| Look & Feel 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. | |||||||
| | Custom Theme and CSS Styling Allow users to create custom, reusable themes using code through canvas-based dashboard. (source) |  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 Visualizations Supports custom charts via Vega-Lite. (source) | Custom Visualizations Available through Looker marketplace. (source) | Custom Visualizations. Rich visualization options. Custom charts are supported with Viz Extensions API. (source) | Custom Visualizations Supported with Power BI visuals 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) | Custom Visualizations Supports custom visualizations through an app ecosystem. (source) | Custom Visualizations Allow users to customize widgets using Highcharts API. (source) |
| | Through canvas-based dashboard that allows more fluid dashboard design. (source) | Custom Layout 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) | Custom Layout 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) | Custom Layout 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) |
| Performance & Scalability Tools should ensures reasonable performance, and should scale well with increasing viewers and data volumes. There are typically 2 methods that affect report performance:
| |||||||
| | 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 Optimization Via Aggregate Awareness. Automatically pick the most optimal aggregated tables per each query for maximal query performance. (source) | Query Optimization Via Aggregate Awareness. Automatically pick the most optimal aggregated tables per each query for maximal query performance. (source) | Query Optimization Via its Hyper data engine for in-memory computation, query caching, and pre-aggregated and Level of Detail (LOD) calculations to optimize queries. (source) | Query Optimization Via Power BI user-defined aggregation, requiring users to manually define aggregation rules and mappings. (source) | Query Optimization Via database indexing, caching query results, and allowing users to write custom SQL for precise and efficient data retrieval. (source) | Query Optimization Enables pre-aggregation with Materialized Views, in-memory processing, cross-database joins via Elastic Data Engine, and query performance monitoring. (source) | |
| Self-service Report Creation 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:
| |||||||
| | 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) |
| | 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 Not Supported. (source) | Alert and Scheduling Supports conditional alerts based on data values and allows email scheduling for notifications (source) |
| | Embedded Report Builder WIP feature. | Embedded Report Builder Not available yet. | 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 Not available yet | 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) |
| Security & Compliance 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. | |||||||
| | SOC2, BAA, and GDPR compliant | SOC2, HIPPA, and GDPR compliant | SOC2, HIPPA and GDPR compliant | SOC2, HIPPA, and GDPR compliant | SOC2, CCPA, and GDPR compliant | HIPAA, SOC2, CCPA, ISO, GDPR compliant | HIPAA, ISO, SOC2 compliant |
| | 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) | N/A (source) | N/A | N/A (source) |
| Maintainability 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 Modeling 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 Properietary. Tableau's Logical Layer visually defines relationships and aggregation rules within workbooks or data sources. (source) | Robust Modeling Project-focused and workbook-scoped, using visual interfaces and DAX for defining relationships and calculations, with 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) |
| | Code-based definition & querying languages (AMQL) Designed natively for 'analytics as code' workflow. 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) | Not supported No code-based definition language | Supported via TMDL format An object model definition syntax for tabular data models at compatibility level 1200 or higher. (source) | Not supported. Product not designed to support code-based definition natively. Workaround using Serialization. (source) | Not supported. 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) |
| | 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) | Properietary 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) | Properietary Version Control Proprietary. Allows one-way Git-based version control through Serialization, available starting with the Pro Plan. (source) | Properietary 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) |
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.
Community Discussions
Discover what other practitioners are discussing about this topic.
r/BusinessIntelligence
Posted on Oct 2022
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.
r/BusinessIntelligence
Posted on Nov 2024
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
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
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
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?
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
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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A.
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!
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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M.
Are there any BI tools people can recommend with white label embedding at a low cost?
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
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
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
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)
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
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
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?
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?
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.
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.
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/