Understanding the embedded analytics landscape
The embedded analytics market has matured significantly over the past few years, evolving from simple dashboard embedding solutions to sophisticated analytics platforms designed specifically for customer-facing applications. Today's platforms offer everything from drag-and-drop dashboard builders to enterprise-grade multi-tenant architectures.
What makes embedded analytics different?
Unlike traditional BI tools designed for internal use, embedded analytics platforms prioritize customer-facing requirements: white-labeling, multi-tenancy, scalable architectures, and seamless integration with existing applications. They focus on the end-user experience rather than data exploration capabilities.
Platform categories by use case
🚀 Modern embedded-first platforms
Built from the ground up for embedding in SaaS applications.
- Fast implementation (days to weeks)
- White-label ready
- Developer-friendly APIs
- Predictable pricing
🏢 Enterprise BI with embedding
Traditional BI platforms that added embedding capabilities.
- Powerful data modeling
- Advanced governance
- Complex implementation
- Enterprise pricing
☁️ Cloud-native analytics
Modern platforms built for cloud data warehouses.
- Direct warehouse connectivity
- Usage-based pricing
- Real-time data processing
- Scalable compute
🔧 Open-source solutions
Self-hosted options with customization flexibility.
- Full control and customization
- Lower licensing costs
- Development resources required
- Maintenance overhead
Key selection criteria for embedded analytics
- Integration complexity and time-to-market
- White-labeling and customization capabilities
- Multi-tenancy and data isolation
- Performance and scalability
- Pricing model alignment with growth
- Developer experience and API quality
- Support for your data architecture
- Security and compliance features
Modern embedded-first platforms
These platforms were designed specifically for embedding analytics into SaaS applications. They prioritize ease of integration, white-labeling, and developer experience over complex data modeling capabilities.
Sumboard is designed specifically for SaaS companies who want to add professional customer-facing analytics without complex development. Their drag-and-drop dashboard builder allows non-technical team members to create beautiful, white-labeled dashboards that integrate seamlessly with any application.
✅ Key strengths
- 10-minute integration with any tech stack
- No-code dashboard creation
- Complete white-labeling and customization
- Fixed pricing with unlimited users
- PDF exports and email scheduling
- Multi-language support
- REST API for data integration
🎯 Best suited for
- SaaS companies wanting fast implementation
- Teams without dedicated data engineers
- Customer-facing analytics use cases
- Predictable cost requirements
- European companies needing EU hosting
Getting started: A generous free tier is available with everything you need to get started and test the platform.
Explo
Explo is a customer-facing analytics platform that focuses on embedding dashboards into SaaS applications. They offer an report builder and emphasize ease of use for both developers and end users.
✅ Key strengths
- White-label embedding
- React and REST API integration
- Pre-built connectors for common databases
- Tiered pricing for growing businesses
🎯 Best suited for
- SaaS companies needing rapid deployment
- Mid-market businesses
- Customer-facing dashboard use cases
Luzmo
Luzmo (formerly Cumul.io) is a Belgium-based embedded analytics platform that focuses on API-first integrations and multi-tenant architectures. They excel at handling complex data sources and offer strong European data residency options.
✅ Key strengths
- Strong API-first architecture
- Multi-tenant data isolation
- European data residency
- Flexible integration options
- Good developer documentation
🎯 Best suited for
- European companies requiring EU hosting
- API-heavy data integrations
- Multi-tenant applications
- Complex data source requirements
Embeddable
Embeddable takes a unique approach by allowing you to use your own visualizations and custom components within their platform. This gives developers maximum flexibility while still providing embedding infrastructure.
✅ Key strengths
- Use your own visualizations
- Custom component support
- Developer-centric approach
- Full customization capabilities
- Strong TypeScript support
🎯 Best suited for
- Teams with specific visualization needs
- Companies with custom design systems
- Developers wanting full control
- Advanced customization requirements
Enterprise BI platforms with embedding
These are traditional business intelligence platforms that have added embedding capabilities. They offer powerful data modeling and governance features but typically require more complex implementation and higher costs.
Looker (Google Cloud)
Looker is Google's enterprise-grade BI platform with powerful LookML modeling and governance features. Now part of Google Cloud, it offers robust embedding capabilities but with enterprise-level complexity and pricing.
✅ Key strengths
- Powerful LookML semantic modeling
- Enterprise-grade governance
- Strong Google Cloud integration
- Advanced data modeling capabilities
- Comprehensive API coverage
- Large enterprise customer base
🎯 Best suited for
- Large enterprises with complex data needs
- Google Cloud ecosystem organizations
- Advanced governance requirements
- Teams with dedicated data engineers
- Complex multi-source data modeling
Consider: High implementation complexity and cost. Typical projects take 3-6 months and cost $150k-300k+ annually.
Tableau Embedded
Tableau Embedded brings Salesforce's powerful visualization capabilities to embedded use cases. Known for advanced charting and data exploration capabilities, though with traditional BI complexity.
✅ Key strengths
- Industry-leading visualization capabilities
- Advanced data exploration features
- Strong Salesforce ecosystem integration
- Extensive connector library
- Large community and resources
🎯 Best suited for
- Organizations already using Tableau
- Complex analytical requirements
- Salesforce ecosystem companies
- Advanced visualization needs
Consider: High per-user costs and complex licensing. Better for internal analytics than customer-facing use cases.
Microsoft Power BI Embedded
Microsoft Power BI Embedded provides capacity-based pricing and integrates seamlessly with Azure services. Best suited for organizations already using Microsoft technologies and Azure infrastructure.
✅ Key strengths
- Deep Azure and Microsoft 365 integration
- Capacity-based pricing with pause capability
- Strong enterprise security features
- Familiar Microsoft interface
- Good Excel integration
🎯 Best suited for
- Microsoft ecosystem organizations
- Azure-integrated environments
- Variable workload patterns
- Teams already using Power BI
Qlik Sense
Qlik Sense offers associative analytics with flexible data exploration capabilities. Their unique associative model allows users to explore data relationships intuitively, though with enterprise-level complexity and pricing.
✅ Key strengths
- Unique associative analytics engine
- Flexible data exploration
- Strong self-service capabilities
- Good mobile support
- Powerful data integration
🎯 Best suited for
- Enterprise analytics deployments
- Complex data exploration needs
- Self-service analytics requirements
- Organizations with BI expertise
Cloud-native analytics platforms
These platforms are built specifically for modern cloud data warehouses and infrastructure. They emphasize real-time data processing, scalable compute, and usage-based pricing models.
Sisense
Sisense combines in-chip analytics with cloud-native architecture. Originally focused on traditional BI, they've pivoted toward embedded analytics with emphasis on complex data processing and AI-driven insights.
✅ Key strengths
- Complex data processing capabilities
- AI and machine learning features
- Scalable cloud architecture
- Strong data preparation tools
- White-labeling capabilities
🎯 Best suited for
- Large enterprise deployments
- Complex data analysis requirements
- Organizations with substantial BI budgets
- Multi-source data integration needs
ThoughtSpot
ThoughtSpot pioneered search-driven analytics, allowing users to query data using natural language. Their platform emphasizes self-service capabilities and AI-powered insights for business users.
✅ Key strengths
- Search-driven interface
- AI-powered analytics capabilities
- Strong self-service features
- Real-time data processing
- Modern cloud architecture
🎯 Best suited for
- AI-driven analytics needs
- Search-based data exploration
- Large enterprise deployments
- Advanced analytics requirements
Sigma Computing
Sigma Computing is a cloud-native analytics platform that queries data warehouses directly. They offer spreadsheet-like interfaces with powerful cloud data warehouse integration.
✅ Key strengths
- Live cloud data warehouse queries
- Spreadsheet-like user interface
- Real-time data processing
- Strong cloud data integration
- No-code analytics creation
🎯 Best suited for
- Cloud data warehouse users
- Real-time analytics requirements
- Teams familiar with spreadsheets
- Live data querying needs
Omni
Created by former Looker team members, Omni represents the next generation of cloud warehouse analytics. They emphasize modern metrics modeling and self-service analytics capabilities built for modern data stacks.
✅ Key strengths
- Modern metrics modeling approach
- Cloud-native architecture
- Strong data warehouse integration
- Self-service analytics focus
- Built by experienced Looker team
🎯 Best suited for
- Modern data stack environments
- Cloud data warehouses
- Self-service analytics teams
- Companies outgrowing Looker
Specialized and niche platforms
These platforms focus on specific use cases, industries, or technical approaches. They often excel in particular areas but may have limitations in broader applications.
Qrvey
Qrvey offers a comprehensive end-to-end analytics platform built specifically for enterprise SaaS companies. Their flat-rate pricing model includes unlimited users and data processing, making it attractive for organizations with complex multi-tenancy requirements.
✅ Key strengths
- End-to-end analytics platform
- AWS-native architecture
- Advanced data collection and processing
- Multi-tenant isolation
- Unlimited users and data model
🎯 Best suited for
- Enterprise SaaS applications
- Complex multi-tenant setups
- AWS-native architectures
- Large data volumes and processing needs
RevealBI
RevealBI, from Infragistics, specializes in .NET and Microsoft ecosystem integrations. Their SDK-first approach makes it particularly appealing for Microsoft-centric development teams who want embedded analytics with native controls.
✅ Key strengths
- Native .NET controls and SDK
- Strong Microsoft ecosystem integration
- Desktop and web application support
- Comprehensive component library
- Developer-focused approach
🎯 Best suited for
- .NET applications and developers
- Microsoft ecosystem companies
- Desktop and enterprise web apps
- Teams needing native controls
ToucanToco
ToucanToco is a French-based platform that emphasizes data storytelling and narrative-driven analytics. They focus on creating compelling, story-driven analytics experiences rather than traditional dashboards.
✅ Key strengths
- Data storytelling focus
- Narrative-driven analytics
- No-code dashboard builder
- Multi-language support
- European data residency
🎯 Best suited for
- Marketing and communications teams
- Executive reporting requirements
- Narrative-driven analytics needs
- European market applications
Yellowfin
Yellowfin offers OEM embedding with revenue-share pricing models alongside traditional options. They focus on data storytelling and collaborative analytics, with pricing structures designed for organizations embedding analytics into customer-facing applications.
✅ Key strengths
- OEM embedding specialization
- Revenue-share pricing options
- Collaborative analytics features
- Data storytelling capabilities
- Flexible licensing models
🎯 Best suited for
- OEM embedding applications
- Revenue-share business models
- Collaborative analytics needs
- ISV and software vendors
Open-source and self-hosted solutions
Open-source platforms offer maximum customization and control but require development resources and infrastructure management. They can be cost-effective for organizations with technical teams.
Metabase
Metabase combines open-source roots with commercial embedding capabilities. Their platform offers both self-hosted and cloud options, with embedding features available in their commercial plans.
✅ Key strengths
- Open-source foundation
- Simple setup and configuration
- Good SQL support
- Active community
- Self-hosted options available
🎯 Best suited for
- Small to medium teams
- SQL-savvy organizations
- Basic embedding needs
- Budget-conscious teams
Note: White-label embedding requires commercial plan. Open-source version has limited embedding capabilities.
Preset (Apache Superset)
Preset is a cloud-hosted version of Apache Superset, the popular open-source BI platform. Their embedded dashboards are available on Professional or Enterprise plans with additional viewer licensing.
✅ Key strengths
- Based on proven Apache Superset
- Large visualization library
- Strong community support
- Flexible data connections
- Self-hosted option available
🎯 Best suited for
- Teams familiar with Superset
- Open-source preference
- Limited embedded requirements
- Python/technical teams
Consider: Embedding features require Professional plan. Self-hosted Superset has limited white-labeling.
Holistics BI
Holistics is a business intelligence platform that focuses on data modeling and self-service analytics. Their embedded analytics offering combines SQL-based data transformation with customer-facing dashboards.
✅ Key strengths
- SQL-based data modeling
- Self-service analytics focus
- Git-based version control
- Strong data transformation
- Developer-friendly approach
🎯 Best suited for
- Data teams with SQL expertise
- Custom data transformation needs
- Self-service analytics requirements
- Version-controlled data models
How to choose the right embedded analytics platform
Selecting the right embedded analytics platform depends on multiple factors including your technical requirements, team capabilities, budget, and growth trajectory. Here's a structured approach to making the decision.
Decision framework by company stage
🚀 Early-stage SaaS (Seed to Series A)
Priorities:
- Fast time-to-market
- Predictable costs
- Minimal development effort
- Focus on core product
Recommended platforms:
- Sumboard - Fixed pricing, fast setup
- Explo - AI-powered, tiered pricing
- Metabase - Budget-friendly option
📈 Growth-stage SaaS (Series A to C)
Priorities:
- Scalable architecture
- Advanced customization
- Multi-tenancy support
- Performance optimization
Recommended platforms:
- Sumboard - Scalable fixed pricing
- Luzmo - API-first architecture
- Qrvey - Enterprise multi-tenancy
- Explo - Feature-rich platform
🏢 Enterprise SaaS (Series C+)
Priorities:
- Enterprise governance
- Complex data modeling
- Advanced security
- Global scalability
Recommended platforms:
- Looker - Enterprise features
- Qrvey - End-to-end platform
- Sisense - Complex analytics
- ThoughtSpot - AI-driven insights
Technical requirements checklist
Integration requirements
- Data source compatibility
- API quality and documentation
- Framework support (React, Vue, etc.)
- Authentication integration (SSO)
- Deployment options (cloud/self-hosted)
Feature requirements
- Chart types and visualization options
- White-labeling and customization
- Export capabilities (PDF, Excel)
- Mobile responsiveness
- Multi-language support
Performance requirements
- Real-time data processing
- Large dataset handling
- Caching and optimization
- Load time requirements
- Concurrent user support
Security and compliance
- Data residency requirements
- GDPR/CCPA compliance
- SOC 2 certification
- Role-based access control
- Data encryption standards
Evaluation process recommendations
🎯 Practical evaluation steps
- Proof of concept (2-3 platforms): Build a simple dashboard with your actual data to test integration complexity and developer experience.
- User experience testing: Have actual end users (your customers) test the embedded dashboards for usability and performance.
- Performance testing: Test with realistic data volumes and concurrent users to ensure acceptable performance.
- Total cost calculation: Include implementation time, ongoing development, and scaling costs over 2-3 years.
- Vendor evaluation: Assess support quality, roadmap alignment, and long-term viability.
✅ Success factors
- Start with customer needs: Focus on the end-user experience rather than internal requirements
- Plan for growth: Choose platforms that scale with your success, not against it
- Prioritize speed to market: Customer feedback is more valuable than perfect features
- Consider maintenance costs: Factor in ongoing development and operational overhead
- Test with real data: Demos with sample data don't reveal real-world limitations
⚠️ Common pitfalls
- Choosing internal BI for external use: Different requirements need different tools
- Underestimating scaling costs: Per-user pricing can become expensive quickly
- Ignoring white-labeling quality: Poor customization reflects on your brand
- Vendor lock-in risks: Ensure data portability and migration options
- Feature overkill: Complex platforms delay time-to-market unnecessarily
Conclusion: Navigating the embedded analytics landscape
The embedded analytics landscape in 2025 offers unprecedented choice and capability. From modern embedded-first platforms like Sumboard to enterprise giants like Looker, there's a solution for every use case, budget, and technical requirement.
The key insight
Success in embedded analytics isn't about choosing the most powerful platform—it's about selecting the platform that best aligns with your customer needs, technical capabilities, and growth trajectory. The best platform is the one that gets you to market fastest with a great user experience.
The embedded analytics market will continue evolving rapidly, but the fundamental principles remain constant: focus on user experience, choose platforms that align with your capabilities, and prioritize time-to-market over feature completeness. The platform that gets you shipping analytics to customers fastest will likely be the right choice for your organization.
