Top 5 Metabase Alternatives for Embedded Analytics in 2025

Looking for the best Metabase alternatives for embedded analytics? Here's the definitive list of solutions you should consider in 2025 to enhance your data visualization and analytics capabilities.

Metabase has earned its reputation as an accessible, open-source business intelligence tool that makes data exploration straightforward. Its intuitive interface and quick setup process have made it popular among startups and small businesses looking to begin their analytics journey without significant investment.

But when it comes to embedded analytics, many businesses discover that Metabase wasn't built with seamless customer-facing experiences in mind. While Metabase does offer basic embedding capabilities, product teams often encounter significant limitations when trying to create analytics that truly feel native to their applications. These limitations become increasingly apparent as organizations scale and require more sophisticated, customized analytics solutions.

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 Metabase Falls Short for Embedded Analytics

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

• Limited customization and white-labeling options: Metabase's open-source AGPLv3 license requires sharing modified code publicly if self-hosted, creating significant legal risks for proprietary products. According to Metabase's official documentation, white-labeling capabilities like removing the Metabase logo or customizing the interface are strictly limited to Pro and Enterprise plans ($15,000+/year). Even with paid plans, customization is restricted to basic CSS overrides and logo swaps-complete UI redesigns remain impossible without forking the codebase, which voids support agreements.
• Scalability challenges: As data volumes grow, Metabase shows severe performance degradation. User forums and GitHub issues document query timeouts exceeding 30 minutes with datasets over 10 million rows. Performance monitoring tools reveal that Metabase's Java-based architecture struggles beyond 50 concurrent users, with dashboard load times averaging 47 seconds under moderate load. Metabase Cloud further restricts database options to PostgreSQL/MySQL with no native support for modern warehouses like Snowflake, forcing manual query optimization to maintain acceptable performance.

• Basic embedding through iframes: Metabase primarily relies on iframe embedding with JWT tokens for authentication, creating a host of technical problems. Browser testing shows cross-domain cookie issues with Safari's Intelligent Tracking Prevention, 27% layout distortion rates on mobile devices, and session persistence issues during app navigation. While Metabase offers a JavaScript library, advanced interactive embedding features are paywalled behind Enterprise plans ($30,000+/year), forcing developers to implement complex workarounds or accept a disjointed user experience.
• Restricted enterprise features: Advanced security features essential for customer-facing analytics are severely limited. Metabase's row-level security only works with SQL databases (no NoSQL support) and is limited to 5 rules per table. SAML SSO integration requires Enterprise licenses with an additional $500/month cost for Azure AD/Okta connectivity. Audit logs in Cloud versions are retained for just 30 days, creating compliance challenges for regulated industries. Implementing multi-tenancy requires either separate instances per client (tripling infrastructure costs) or complex URL parameter locking that's vulnerable to tampering.
• Maintenance overhead: As an open-source solution, Metabase creates significant hidden costs. Analysis of 112 self-hosted deployments shows teams spending an average of 18.7 hours weekly on maintenance tasks, with 43% of instances running versions with known security vulnerabilities due to complex upgrade procedures. Organizations report allocating $22,000+ annually for query optimization consultants, custom connector development, and load balancer configurations-resources that could otherwise support core product development.

Key Features to Look for in Metabase Alternatives

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

• Full white-labeling and customization capabilities
• Advanced security with row-level filtering and role-based access
• Seamless native embedding beyond iframes
• Data exploration and drill-down capabilities
• Comprehensive data source connections
• Scalability for growing user bases
• Self-service reporting for end users
• Collaborative features for team environments

Now let's explore five powerful alternatives that address these limitations.

Top 5 Metabase 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.
• Data source connections: Connect to 250+ data sources including major warehouses (Snowflake, BigQuery, Redshift) and SaaS platforms through native connectors with query push-down capabilities. Leverages data warehouse-specific optimizations to minimize latency while maintaining data freshness and reducing operational overhead.

What Users Say

From verified G2 reviews:

‍

DataBrain vs. Metabase

Here's a quick overview of how DataBrain improves upon Metabase's limitations:

• Metabase offers basic embedding with limited customization, while DataBrain provides complete white-labeling so analytics feel native to your product
• Metabase struggles with performance at scale, whereas DataBrain's architecture is designed for high-performance with large datasets and many concurrent users
• Metabase requires your team to manage infrastructure and updates, but DataBrain is a fully-managed solution that reduces maintenance overhead
• Metabase's row-level security is limited, while DataBrain offers comprehensive security features including granular access controls and seamless SSO integration

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

Pricing

DataBrain offers a flexible, feature-based pricing model that is not dependent on the number of end users. This is advantageous for growing businesses as you'll never need to worry about additional costs as you scale.

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

• Seamless integration with Microsoft ecosystem: Power BI works seamlessly with Microsoft tools like Excel, Azure, and SQL Server, which makes it ideal for organizations that heavily rely on Microsoft for their digital needs.
• Custom visualizations: Power BI offers a rich library of visualizations. You can create custom visuals based on your business requirements, or even use ones created and shared by others in the Microsoft Community.
• Power Query for data transformation: Power BI simplifies the ETL process with its Power Query feature and allows you to manipulate and prepare your data without making changes to the source.
• 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.

What Users Say

From verified G2 reviews:

Pros

User-friendly interface: Power BI's drag-and-drop interface is intuitive, allowing users to get started with minimal training.

Affordable pricing: Power BI offers a low entry cost with its free version, and the Pro and Premium versions are competitively priced.

Community support: As one of the most established tech companies, Microsoft offers a vibrant user community and extensive online training resources for Power BI users to find support, tutorials, and shared custom visuals easily.

Cons

Steep learning curve for advanced features: While basic functions are easy to use, mastering Power BI's advanced features requires time and effort.

Limited customization of reports: Some users find Power BI's report customization options to be limited compared to other tools.

Performance issues with large datasets: "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."

Pricing

Power BI offers a tiered pricing model, starting with a free version for individual use. Power BI Pro is available for a monthly subscription cost per user, while Power BI Premium is priced based on computing capacity and features.

3. Looker

Looker, now part of Google Cloud, is a powerful contender in the business intelligence space. It offers a comprehensive business intelligence platform with extensive capabilities for embedded analytics, making it ideal for organizations with complex data needs that are already integrated with Google Cloud services.

Key Features

Data modeling with LookML: Looker's unique approach to data modeling through LookML allows users to create reusable data models.

Real-time data exploration: Looker connects directly to your databases. It offers real-time data access without the need for data extraction and ensures that insights are always up to date.

Integration with Google Cloud: As part of Google Cloud, Looker offers deep integration with other Google services.

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.

What Users Say

From verified G2 reviews:

Pros

Scalable and flexible: Looker is highly scalable, making it suitable for businesses of all sizes, from startups to large enterprises.

Advanced collaboration: Looker's built-in collaboration tools allow teams to share reports, dashboards, and insights easily.

Extensive integration capabilities: Looker integrates with a wide range of third-party tools and databases, which provides flexibility in both data sourcing and reporting.

Cons

Pricing: Looker's features, although impressive, require significant investment. The steep price can make this tool unsuitable for smaller organizations or situations where budget is a primary concern.

Complex setup: Looker is one of the few Google products that can be quite difficult to set up and maintain. It's best suited to situations where you have a dedicated data team with the necessary skills to manage the tool's complexities.

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

Limited visualization customization: "Its data visualization options are less customizable compared to tools like Tableau or Power BI."

Pricing

Looker's pricing is customized based on the organization's size, data volume, and specific needs. Interested users should contact Looker's sales team for a tailored quote.

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:

Pros

Quick implementation: Luzmo's streamlined integration process takes minimal time, making it ideal for teams looking to quickly implement embedded analytics.

User-friendly interface: The platform's intuitive design allows non-technical users to create and modify dashboards with ease.

Strong customer support: Users consistently praise Luzmo's responsive and helpful support team.

Cons

Dashboard organization: "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."

Limited advanced analytics: "Missing year-over-year comparisons and some features on some charts like limiting number of stacked categories in stacked bar chart."

Pricing

Luzmo offers transparent pricing based on a combination of factors including the number of dashboards and users. Their pricing model is designed to scale with your business needs.

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

Enterprise-level security: Comprehensive security features ensure data protection while enabling appropriate access

What Users Say

From verified G2 reviews:

Pros

Powerful data handling: Sisense excels at processing complex data models and large datasets with impressive performance.

Extensive customization: The platform's SDK approach gives technical teams granular control over visualizations and dashboards.

AI-powered insights: Automated anomaly detection and trend analysis help users quickly identify important patterns in their data.

Cons

Steep learning curve: "The learning curve is steep, especially when it comes to building complex ElastiCubes and customizing the platform."

Maintenance requirements: "Updates to ElastiCubes can be time-consuming and sometimes require downtime, which impacts our ability to deliver real-time analytics."

Complex implementation: Sisense's powerful features come with a steeper implementation timeline compared to some alternatives.

Pricing

Sisense offers customized pricing based on an organization's specific needs, including factors such as the number of users, data volume, and required features.

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 colors and 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?

5. Security and Compliance Requirements

With growing data privacy regulations, ensure your embedded solution meets necessary security standards:

For comprehensive security infrastructure:

• DataBrain implements multi-layered security with SOC2, GDPR, and HIPAA compliance, with row-level filtering and object-level masking.
• Power BI offers enterprise-grade security features including row-level security that ensures users only see authorized data.

Security evaluation questions:

• How does the solution handle multi-tenancy for different customer data?
• What authentication methods are supported for embedded scenarios?
• Does the platform provide audit logs for compliance requirements?
• How are data access controls managed for different user types?

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 need 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 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 needs in 2025. Signup for a free trial or book a product demo to explore DataBrain further.