GoodData Alternatives for Embedded Analytics in 2025

Looking for the best GoodData alternatives for embedded analytics? Here's the definitive list of SaaS companies you can consider.

GoodData has earned its reputation as a comprehensive business intelligence platform, particularly for companies requiring extensive data governance and enterprise-level analytics. Their platform offers robust capabilities for data integration and management, with strong support for complex business metrics and calculations.

But when it comes to embedded analytics, many SaaS companies discover that GoodData wasn't built with seamless customer-facing experiences in mind. While GoodData does offer embedding capabilities, product teams often encounter significant limitations when trying to create analytics that truly feel native to their applications. The platform's design and architecture create barriers that make it difficult to deliver the modern, intuitive analytics experience today's users demand.

If you're reading this article, you've likely experienced these frustrations firsthand. You're looking for something that doesn't just function as a bolt-on analytics solution but truly integrates with your product's look, feel, and workflow.

Why GoodData Falls Short for Embedded Analytics

Before we explore the alternatives, let's examine why many product teams are moving away from GoodData for their embedded analytics needs:

• Proprietary MAQL language creates dependency: GoodData's Multidimensional Analytical Query Language requires specialized knowledge, forcing many teams to rely on GoodData consultants for customizations and creating bottlenecks in development cycles.
• Complex workspace architecture: The platform's multi-tenant analytics structure with separate workspaces complicates implementation for product teams, making it difficult to manage user permissions and data access at scale.
• Limited white-labeling capabilities: While basic customization is possible, complete branding control remains challenging, leaving customers with dashboards that look distinctly like GoodData rather than a seamless part of your product.
• Unpredictable pricing as you scale: GoodData's pricing model can become expensive as your user base grows, with costs starting around $1,500/month for embedded solutions but potentially escalating quickly based on usage and data volume.
• Complex embedding process: The reliance on iFrames for embedding creates performance issues and integration challenges, particularly for responsive design and mobile applications.

Key Features to Look for in GoodData Alternatives

When evaluating alternatives to GoodData for embedded analytics, consider these essential features:

• Full white-labeling and customization
• Native SDK integration beyond iFrames
• Predictable pricing model for scaling
• Self-service capabilities for end users
• Security and compliance
• Performance with large datasets

Top 5 GoodData alternatives for Embedded Analytics

1. Databrain

Databrain is a purpose-built embedded analytics platform designed specifically for creating highly customized user-facing analytics. Unlike traditional BI tools with embedding capabilities added as an afterthought, Databrain is developed from the ground up to integrate seamlessly with your application while providing full control over design, functionality, and user experience.

Key Features

• Fully customizable white-labeled experience:

Custom-branded analytics with your domain name, logos, color palette, typography, and UI components to create a seamless extension of your application. Supports custom CSS for pixel-perfect styling and JavaScript extensions for behavior customization, ensuring end users experience analytics as a native part of your platform rather than third-party content.

• Intuitive dashboard creation with developer flexibility:

Empower business users with drag-and-drop dashboard builders featuring pre-built visualization libraries, while developers can extend functionality with custom visualizations using JavaScript or TypeScript. This dual approach allows quick deployment of standard reports while maintaining the ability to implement complex, specialized analytics as needed.

• Native embedding beyond iFrames:

Implement analytics directly into your application's DOM using web components or JavaScript SDK integration instead of traditional iFrames, eliminating cross-domain issues and providing 3-5x faster rendering speeds. This approach enables seamless navigation, responsive layouts, and better integration with your application's state management.

• High-performance real-time analytics:

Process live data streams with sub-second latency using in-memory caching and real-time data ingestion pipelines. Maintains performance with specialized query optimization for time-series data and automated materialized views that pre-aggregate results, ensuring dashboard responsiveness even when handling millions of events per second.

• Comprehensive security infrastructure:

Implement multi-layered security with SOC2, GDPR, and HIPAA compliance. Row-level security filters data based on user attributes, while object-level security masks sensitive tables and columns entirely. Integration with identity providers enables single sign-on, multifactor authentication, and JWT-based authorization to create secure, tenant-specific analytics experiences.

• Predictable pricing model:

Unlike GoodData's complex workspace-based pricing, Databrain offers transparent, scalable pricing that doesn't penalize you for adding users or viewers, making it easier to forecast costs as your customer base grows.

What Users Say

From verified G2 reviews:

Databrain vs. GoodData

Here's a quick overview of how Databrain improves upon GoodData's limitations:

• GoodData uses a proprietary MAQL language that creates dependency on specialized knowledge, while Databrain uses standard SQL that most data teams already know. Additionally, Databrain’s AI-based SQL generator can help create SQLs even for non-technical users.
• Workspace architecture in GoodData is needlessly complex and difficult to scale. Databrain's multi-tenant architecture is designed using row-level filters that allow for a clean and secure client workspace from within the UI.
• GoodData dashboards always look like GoodData, but Databrain provides complete white-labeling, so the analytics feel native to your product
• GoodData's pricing scales unpredictably as you add users, while Databrain offers transparent, predictable pricing that's ideal for growing SaaS companies

Pro tip: Databrain's web component architecture eliminates iFrames, integrating analytics directly into your application's DOM. This technical approach delivers:

• 3-5x faster rendering speeds compared to traditional embedding
• Responsive layouts that automatically adapt to different screen sizes
• Seamless navigation between your application and embedded dashboards
• Simplified multi-tenant implementation through row-level security
• Reduced development overhead for customer-specific data filtering

2. Power BI Embedded

Power BI Embedded is Microsoft's business intelligence solution for integrating analytics into internal and external applications. Being a Microsoft product, it integrates well with the Microsoft ecosystem and provides enterprise-grade security features.

Key Features

• Row-level security (RLS): Ensures users only see the data they're authorized to access
• Data preloading: Allows dashboards to display data more quickly, though this affects data freshness
• Iframe embedding: Simplifies the integration process with copy-paste code snippets and API authentication
• Extensive data source compatibility: Seamlessly integrates with virtually all SQL and NoSQL databases on the market

What Users Say

From verified G2 reviews:

“Power BI embedded is scalable, fast, accurate, flexible and it keeps advancing with new features and capabilities.”

“It's fantastic to be able to view embedded reports in real time. It's easy for end users to access, even offline.”

Limitations

“Sometimes I feel it gets stuck when I load very large datasets in it to build a line chart or pie chart, otherwise, it works very smooth with nominal dataset.”

“The limitation to publishing your dashboard is related to paying process and the price is too high to be paid.”

Related Read: A detailed guide on Power BI Embedded Pricing

3. Looker Embedded

Looker Embedded is an enterprise-grade platform that's part of Google Cloud Core. It provides real-time, user-facing analytics with high security levels, making it ideal for organizations with complex data needs that are already integrated with Google Cloud services.

Key Features

• Secure iframe settings: Enable users to navigate dashboards embedded in your app while ensuring they only access the data they're authorized to see.
• Self-service analytics: Provides interactive dashboards allowing users to navigate, filter, and download data from reports as needed.
• Real-time loading dashboards: Delivers live data to end users, enabling them to view metrics that matter most to them in real-time.
• LookML for custom data modeling: Provides a proprietary language for tailored data modeling and more flexibility.

What Users Say

From verified G2 reviews:

“Looker is very user-friendly, even for people without technical skills. It has a lot of ready-made connectors, making it easy to pull data from different sources like BigQuery, Google Sheets, or marketing platforms.”

“An absolute rockstar tool by Google in terms of creating reports, dashboards, and models which can be utilized across teams and members.”

Limitations

“Looker can have a steep learning curve, especially with its LookML modeling language, which may be challenging for non-technical users.”

“Its data visualization options are less customizable compared to tools like Tableau or Power BI.”

4. Luzmo

Luzmo (formerly Cumul.io) is a Belgian-made embedded analytics platform specifically designed for customer-facing analytics in SaaS products. It offers a set of configurable charts with a user-friendly interface and multiple embedding options.

Key Features

• Self-service BI interface: Strikes a balance between flexibility and simplicity
• Drag-and-drop functionality: Makes the user experience friendly and easy to use
• Modern visualizations: Creates dashboards suited for user-facing applications
• Web component embedding: Offers better performance than iframe-based solutions

What Users Say

From verified G2 reviews:

“It is simple enough to use that non-technical people are easily able to be involved in the use of it.”

“Very fast to create dashboards, user-friendly, so easy to build those. Nice embedding in pages and also very nice support to help us solve our issues!”

Limitations

“I would say the only downsides for us is that we are planning to have a lot of dashboards and datasets in the future, and I think the UI is not necessarily super easy to navigate once we get to that stage.”

“Missing year-over-year comparisons and some features on some charts like limiting number of stacked categories in stacked bar chart.”

5. Sisense

Sisense is an enterprise-grade analytics platform known for its powerful data processing engine and extensive customization capabilities. It offers strong performance with large datasets and a code-first approach to embedding that gives developers significant control.

Key Features

• ElastiCube technology: Proprietary data engine optimized for complex analytics on large datasets
• Custom widget SDK: Create completely customized visualizations beyond standard charts
• Automated insights: AI-powered features that highlight key trends and anomalies
• White-labeling options: Extensive branding customization for embedded analytics

What Users Say

From verified G2 reviews:

“Sisense's ElastiCube technology has given us incredible performance even with our most complex datasets that were bringing our previous solution to its knees.”

“The widget SDK gives us the flexibility to create exactly the visualizations our customers need, without compromise.”

Limitations

“The learning curve is steep, especially when it comes to building complex ElastiCubes and customizing the platform.”

“Updates to ElastiCubes can be time-consuming and sometimes require downtime, which impacts our ability to deliver real-time analytics.”

How to Choose the Right Embedded Analytics Solution

Selecting the optimal embedded analytics solution requires a systematic evaluation of multiple factors beyond just features and pricing. Based on extensive research and industry expertise, here's a comprehensive framework to guide your decision-making process:

1. Define Your Business Needs and Goals First

Before comparing technical specifications, clearly articulate what you want to achieve with embedded analytics:

• Revenue Generation vs. Value-Add: Are you monetizing analytics as a premium feature or providing it as core product value?
• User Experience Goals: How seamlessly should analytics integrate with your existing application?
• Audience Sophistication: Will your end users need simple visualizations or complex self-service analytics?

Questions to ask yourself:

• What specific insights do our customers require most from our data?
• How will embedded analytics differentiate our product in the market?
• What metrics will determine if our embedded analytics implementation is successful?

2. Customization and Branding Requirements

The level of customization needed varies significantly based on your product strategy and brand requirements:

For full white-labeling and brand consistency:

• Databrain offers “complete control over branding, styling, and UI components” with capabilities to “match your preferred look and feel, adhering to your corporate brand with precision.”

For developer-friendly customization:

• Sisense provides a powerful SDK approach that gives technical teams granular control while handling backend complexities.

For moderate customization needs:

• Luzmo offers “basic standard options with colours/theming” that balance simplicity with customization.

3. Technical Requirements and Integration Capabilities

The technical architecture of your chosen solution will significantly impact implementation timeline, maintenance requirements, and long-term flexibility:

For seamless native integration:

• Databrain enables you to “connect to your current databases and warehouses without duplicating data or designing new models” and “embed analytics into any application using React, Web components and iFrame.”

For ecosystem-specific integration:

• Looker works best if “you're wedded to the Google Cloud Core ecosystem”
• Power BI provides optimal integration for Microsoft-centric organizations

Technical evaluation questions:

• How will the solution connect to our existing data infrastructure?
• What are the authentication and security requirements?
• How will the solution scale as our data volume and user base grows?
• Does the solution support the specific databases and data warehouses we use?

4. Performance and Speed Considerations

Speed has emerged as a critical factor in embedded analytics success, directly impacting user adoption and satisfaction:

For real-time analytics with large datasets:

• Databrain delivers “live data updates without performance degradation, even with large datasets.”
• Sisense's ElastiCube technology provides strong performance for complex queries on large datasets.

Performance evaluation questions:

• Does the platform enable your products to meet the speed and reliability standards your customers expect?
• Is the tool architected to limit trips to the data warehouse?
• How does the solution handle caching to balance data freshness with performance?
• What happens when multiple users run complex queries simultaneously?

Conclusion: Making Your Final Selection

The ideal embedded analytics solution aligns with your business objectives, technical requirements, and user needs while offering the right balance of functionality, usability, and cost-effectiveness.

Based on comprehensive evaluation across these criteria:

• Databrain stands out for organizations seeking fully customized embedded analytics with excellent user experiences, with particular strengths in branding control, ease of use, and responsive customer support.
• Power BI Embedded works well for Microsoft-centric organizations that need to leverage existing investments in the Microsoft ecosystem.
• Looker excels for Google Cloud users who require sophisticated data modeling capabilities and don't mind a steeper learning curve.
• Luzmo offers quick implementation of modern visualizations with strong support for self-service dashboard creation by end users.
• Sisense provides powerful customization options and performance for technical teams dealing with complex data needs and large datasets.

Your choice ultimately depends on your specific requirements, technical capabilities, and budget constraints. By selecting the right tool, you can deliver analytics experiences that feel like a natural extension of your product, rather than a bolted-on afterthought.

For organizations seeking the ideal balance of customization freedom, ease of use, and cost-effective scaling, Databrain offers the most comprehensive solution for embedded analytics requirements in 2025. Signup for a free trial or book a product demo to explore Databrain further.