Why embedded analytics pricing is so complex
The frustrating reality
Most embedded analytics vendors hide their pricing behind lengthy sales calls, demos, and "contact us" forms. You can spend weeks just trying to get basic cost information, making it nearly impossible to budget effectively or compare options.
Unlike traditional SaaS products with transparent pricing pages, embedded analytics pricing depends on numerous variables: number of users, data volume, customization requirements, white-labeling needs, and deployment preferences. This complexity makes vendor selection unnecessarily difficult.
Common pricing variables that affect costs
- Number of end users (viewers)
- Number of internal users (builders)
- Data volume and query complexity
- Number of dashboards or reports
- White-labeling and customization level
- Self-hosting vs. cloud hosting
- Support level and SLA requirements
- Integration complexity and custom features
This guide cuts through the sales noise to give you real pricing data from 15+ embedded analytics platforms. We've analyzed publicly available pricing, gathered quotes from vendors, and customers to provide the most comprehensive pricing comparison available.
Embedded analytics pricing models explained
The most common pricing models for embedded analytics platforms.
Understanding different pricing approaches helps you identify which vendors align with your business model and growth plans. Each model has distinct advantages and potential pitfalls.
1. Fixed/Flat-rate pricing
Best for: Growing SaaS companies
Fixed pricing offers a straightforward approach where you pay a set monthly or annual fee regardless of usage or user count. This model provides predictable costs, making it easy to budget and forecast without worrying about scaling surprises. It encourages broader customer adoption since there's no per-user cost, and simplifies pricing conversations with your customers. While you might overpay if your usage is low, and fewer vendors currently offer this model, it's particularly valuable for companies anticipating growth who want to avoid usage-based cost spikes.
✅ Advantages
- Predictable monthly costs
- No surprises as you scale
- Easy to budget and forecast
- Encourages customer adoption
- Simple pricing conversations
❌ Limitations
- May overpay with low usage
- Fewer vendors offer this model
2. Per-user pricing
Best for: Small teams with predictable user base
Per-user pricing scales with the number of users accessing analytics. Often separates internal users (creators) from external users (viewers) with different price points. This model is particularly useful for SaaS companies with a stable user base who want to avoid overpaying for unused features. It also encourages customer adoption since there's no fixed cost, and simplifies pricing conversations with your customers. However, it can become expensive quickly if your user base grows rapidly, and requires careful management of viewer and creator roles.
✅ Advantages
- Scales with actual usage
- Lower costs for small teams
- Familiar pricing model
- Can be cost-effective initially
❌ Limitations
- Costs explode with growth
- Discourages customer adoption
- Complex viewer management
- Unpredictable monthly costs
3. Usage-based pricing
Best for: Variable workloads and data processing
Usage-based pricing aligns costs with actual platform utilization, charging based on metrics like API calls, data volume processed, compute capacity, or query counts. This model shines for applications with variable workloads or seasonal usage patterns, as you only pay for what you consume. It's particularly attractive for companies starting small who want to avoid large upfront costs. However, usage-based pricing can make monthly costs unpredictable and requires careful monitoring to avoid unexpected spikes. Most cloud-native platforms prefer this model, often offering reserved capacity options for more predictable workloads.
✅ Advantages
- Pay only for actual usage
- Low entry costs
- Scales down when idle
- Flexible capacity options
❌ Limitations
- Unpredictable monthly costs
- Complex usage monitoring
- Can be expensive at scale
4. Feature-based/Tiered pricing
Best for: Organizations with specific feature requirements
Feature-based pricing structures analytics costs around functionality tiers rather than usage or user counts. Starting with basic visualization capabilities at lower tiers, platforms progressively unlock advanced features like white-labeling, custom themes, or AI-powered analytics at higher price points. This model appeals to companies who want to start simple and expand capabilities as their needs grow. While it offers clear upgrade paths and helps control costs by paying only for needed features, the tiered approach can create friction when essential features are locked behind higher pricing tiers. Most modern embedded analytics platforms use this model in combination with other pricing factors.
✅ Advantages
- Start small and scale up
- Pay only for needed features
- Clear upgrade path
- Predictable pricing per tier
❌ Limitations
- Essential features may be gated
- Can outgrow tiers quickly
- Less pricing flexibility
Complete vendor pricing comparison
Here's the comprehensive breakdown of pricing for 15+ embedded analytics platforms, organized by pricing model and target market.
Fixed/Flat-rate pricing platforms
The most transparent pricing in embedded analytics. Fixed monthly cost with unlimited users and viewers. Built specifically for SaaS companies who want professional customer-facing analytics without the complexity.
Core plan: €199/month
- ✅ Unlimited end users
- ✅ Unlimited editors
- ✅ 10 embedded dashboards
- ✅ All chart types
- ✅ White-label customization
- ✅ PDF exports
- ✅ Email scheduling
- ✅ Multi-language support
- ✅ 10-minute integration
Business: €499/month
- ✅ Everything in Core
- ✅ Custom PDF layout builder
- ✅ Compare with feature
- ✅ Dashboard localisation and translation features
- ✅ Versioning
- ✅ Priority support
Best for: SaaS companies wanting fast implementation, predictable costs, and professional results
Total cost example: €2,388/year for unlimited users vs. competitors charging €10k-50k annually
More details on the pricing model can be found here.
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.
Platform details
- No public pricing
- Unlimited users and tenants
- Full white-labeling
- Advanced data collection and processing
Best suited for
- Enterprise SaaS applications
- Complex multi-tenant setups
- AWS-native architectures
- Large data volumes
RevealBI
RevealBI, from Infragistics, specializes in .NET and Microsoft ecosystem integrations. Their SDK-first approach and flat-rate pricing make it particularly appealing for Microsoft-centric development teams. The platform offers native controls for popular frameworks like Angular and React.
Platform details
- No public pricing. Tipically €30,000-50,000/year
- Unlimited embedding
- Native .NET controls
Best suited for
- .NET applications
- Microsoft ecosystem
- Desktop and web apps
Omni
Created by former Looker team members, Omni represents the next generation of cloud warehouse analytics. Their platform emphasizes modern metrics modeling and self-service analytics capabilities.
Platform details
- No publicly available but estimated at €1,000-2,000/month
- Modern metrics modeling
- Cloud-native architecture
Best suited for
- Cloud data warehouses
- Modern data stacks
- Self-service analytics
Embeddable
Embeddable is a platform that allows you to embed your own visualizations in your own application.
Platform details
- No public pricing
- Unlimited users and viewers
- Full white-labeling
- Use your own visualizations
Best suited for
- SaaS companies
- Small to medium businesses
- Self-service analytics
Seat-based or user-based pricing platforms
Tableau Embedded
Tableau Embedded brings their powerful visualization capabilities to embedded use cases.
Platform details
- No public pricing
- Creator licenses approximately: $70/month
- Viewer licenses approximately: $420/month
- No free trial
Metabase
Metabase combines open-source roots with commercial embedding capabilities. Their platform have a base platform fee plus per-user costs.
Platform details
- Base platform: €500/month
- All licenses: €10/user/month
- White-label embedding
- SSO integration
Best suited for
- Small to medium teams
- SQL-savvy organizations
- Basic embedding needs
Luzmo
Luzmo is a Belgium-based embedded analytics platform that focuses on API-first integrations and multi-tenant architectures. Their tiered pricing model scales with monthly active viewers and feature requirements.
Platform details
- Whitelabel starting at €2,050/month for 100 MAV
- Viewer-based pricing tiers
- Limited white-labeling options
Best suited for
- European companies
- API-heavy data sources
- Multi-tenant applications
Sigma Computing
Sigma Computing is a cloud-native analytics platform that offer embedded analytics with no-code options and iframe embedding.
Platform details
- Custom enterprise pricing (no public pricing)
- Live cloud data warehouse queries
- No-code iframe embedding
Holistics BI
Holistics is a business intelligence platform that focuses on data modeling and self-service analytics. Their embedded analytics offering combines per-user pricing with platform fees, targeting companies that need SQL-based data transformation capabilities alongside customer-facing dashboards.
Platform details
- Pricing for embedded analytics is not public
- SQL-based data modeling
Best suited for
- Data teams with SQL expertise
- Custom data transformation needs
- Self-service analytics requirements
Preset
Preset is a cloud-hosted version of Apache Superset that offers embedded analytics. Their embedded dashboards are only available on Professional or Enterprise plans and require purchasing additional viewer licenses.
Platform details
- Professional/Enterprise plan required
- Embedded viewers: $500/month for 50 licenses
- 14-day free trial for embedded features
Best suited for
- Teams familiar with Superset
- Limited embedded requirements
Usage-based pricing platforms
Usage-based pricing platforms charge based on consumption metrics like data processed, compute capacity, or query volume. This model works well for variable workloads but can become expensive at scale.
Microsoft Power BI Embedded
Microsoft Power BI Embedded provides per-second billing and integrates seamlessly with Azure services. Best suited for organizations already using Microsoft technologies.
Platform details
- Starting at €735/month
- Per-second billing with pause capability
- No free trial
Best suited for
- Microsoft ecosystem organizations
- Variable workload patterns
- Azure-integrated environments
Sisense
Sisense combines usage-based and user-based pricing in a hybrid model designed for enterprise deployments. Originally focused on traditional BI, Sisense has pivoted toward embedded analytics but maintains enterprise-level pricing that can reach mid-six figures annually for large implementations.
Platform details
- Starting: €10,000/year minimum
- Complex data processing capabilities
Best suited for
- Large enterprise deployments
- Complex data analysis requirements
- Organizations with substantial BI budgets
- Multi-source data integration needs
Domo
Domo operates on a consumption-based credit system where organizations purchase annual credits that are consumed based on data processing, transformation, and storage activities. This model provides flexibility but requires careful monitoring to avoid unexpected costs.
Platform details
- Consumption-based credit system
- Typical range: €20,000-75,000/year
- Credits consumed by data operations
- 30-day free trial available
Best suited for
- Medium to large enterprises
- Variable data processing needs
GoodData
GoodData focuses on OEM and multi-tenant embedded analytics with usage-based pricing that scales with client count and data volume. Their platform is specifically designed for companies building analytics into their products for external customers.
Platform details
- No public pricing
- Scales with client count and data volume
- Multi-tenant architecture
Best suited for
- OEM and ISV companies
- Multi-tenant SaaS applications
Qlik Sense
Qlik Sense combines base licensing fees with data throughput add-ons, making it suitable for enterprise analytics with heavy data processing requirements. Their associative model allows for flexible data exploration but comes with significant licensing costs.
Platform details
- Starts at $200/month for 25GB of data analysis and 10 users.
- Additional data throughput fees
Best suited for
- Enterprise analytics deployments
- Heavy data processing needs
ThoughtSpot
ThoughtSpot has transitioned from unlimited flat-rate pricing to a usage-based model that charges based on search queries and data volume.
Platform details
- Usage-based search pricing
- Previously €25,000/month unlimited
- AI-powered analytics capabilities
- Search-driven interface
Best suited for
- AI-driven analytics needs
- Search-based data exploration
- Large enterprise deployments
- Advanced analytics requirements
Yellowfin
Yellowfin offers OEM embedding with revenue-share pricing models alongside traditional usage-based options. They focus on data storytelling and collaborative analytics, with pricing structures designed for organizations embedding analytics into customer-facing applications.
Platform details
- Not available on the website
- Revenue-share options available
- OEM embedding focus
Best suited for
- OEM embedding applications
- Revenue-share business models
Feature-based/Tiered pricing platforms
Feature-based pricing platforms offer different tiers based on functionality rather than user count or usage. This model works well for organizations that want to start with basic features and scale up capabilities as their needs grow.
Looker
Google's Looker is an enterprise-grade BI platform with powerful LookML modeling and governance features. Now part of Google Cloud, Looker offers three main editions with custom pricing based on features, users, and API usage requirements.
Platform details
- Standard: €35,000-60,000/year
- Enterprise: €60,000-150,000+/year
- Embed: €150,000-300,000+/year
- LookML semantic modeling
- Enterprise-grade governance
Best suited for
- Large enterprises
- Complex data modeling needs
- Google Cloud ecosystem
- Advanced governance requirements
Explo
Explo is a customer-facing analytics platform designed specifically for embedding dashboards into SaaS applications. Their tiered pricing model scales with the number of customer segments and available features, making it accessible for growing businesses.
Platform details
- Growth: €795/month (3 dashboards)
- Pro: €2,195/month (unlimited)
- Enterprise: Custom pricing
- White-label embedding
- AI-powered report builder
Best suited for
- SaaS companies
- Customer-facing dashboards
- Rapid deployment needs
- Mid-market businesses
ToucanToco
ToucanToco is a French-based platform that emphasizes data storytelling and narrative-driven analytics. Their pricing model combines internal builder licenses with client group limits, targeting organizations that want to create compelling, story-driven analytics experiences.
Platform details
- Starter: €890/month
- Enterprise: Six figures annually
- Data storytelling focus
- No-code dashboard builder
- Multi-language support
Best suited for
- Marketing and communications teams
- Executive reporting
- Narrative-driven analytics
- European market
Conclusion: Making the right choice for your organization
After analyzing 15+ embedded analytics platforms and their pricing models, several clear patterns emerge. The right choice depends heavily on your company stage, technical requirements, and growth trajectory rather than just upfront costs.
✅ Key success factors
- Understand total cost of ownership: Factor in implementation, scaling, and hidden fees
- Match pricing model to usage: Fixed pricing for predictable costs, per-user for stable teams
- Plan for growth: Choose platforms that won't penalize you for success
- Prioritize customer experience: Performance and white-labeling matter more than internal features
- Consider compliance early: GDPR, data residency, and security requirements
⚠️ Common pitfalls to avoid
- Underestimating scaling costs: Per-user models can become expensive quickly
- Choosing internal BI for external use: Different requirements, different tools
- Ignoring implementation time: Complex platforms can delay time-to-market
- Vendor lock-in: Always ask about data export and migration capabilities
- Feature overkill: Start simple, scale complexity as needed
🎯 Practical next steps
- 1. Define your requirements: User count, data volume, customization needs, compliance requirements
- 2. Shortlist 2-3 platforms: Based on pricing model that fits your growth trajectory
- 3. Run pilots with real data: Test performance, user experience, and integration complexity
- 4. Calculate 3-year total cost: Include implementation, scaling, and potential overages
- 5. Negotiate terms: Annual contracts, volume discounts, and migration support
The embedded analytics landscape in 2025
The market has matured significantly, with distinct tiers emerging: budget-friendly platforms for startups, growth-focused solutions for scaling SaaS companies, and enterprise platforms for complex requirements. AI capabilities, real-time processing, and improved developer experience are becoming standard rather than premium features.
The most successful implementations balance cost, functionality, and user experience. Rather than optimizing for the lowest upfront cost, consider platforms that align with your long-term strategy and won't require costly migrations as you scale.
Frequently Asked Questions
Common questions about embedded analytics pricing, implementation, and platform selection.
Why don't most embedded analytics vendors show their pricing publicly?
Embedded analytics pricing depends on numerous variables: user types, data volume, API usage, customization requirements, white-labeling needs, and deployment preferences. Many vendors use custom quotes to align pricing with specific use cases and company size. However, this opacity makes budgeting and comparison difficult for buyers.
What's the typical budget range for embedded analytics?
Small SaaS companies typically spend €2,000-10,000 annually, while mid-market companies invest €10,000-50,000 per year. Enterprise deployments often exceed €50,000-200,000+ annually. Fixed pricing models like Sumboard can significantly reduce these costs by eliminating per-user fees.
Per-user vs. fixed pricing: which is better for growing SaaS companies?
Fixed pricing is generally better for growing SaaS companies because it provides predictable costs and encourages customer adoption. Per-user pricing can become expensive quickly as your user base grows, potentially limiting your ability to offer analytics to all customers. However, very small teams might benefit from per-user pricing initially.
How long does embedded analytics implementation typically take?
Implementation time varies dramatically by platform complexity. Modern embedded-first platforms like Sumboard can be deployed in days to weeks, while traditional BI platforms (Looker, Tableau) often require months of setup. Factor in data preparation, custom development, and user training when planning timelines.
What hidden costs should I watch out for?
Common hidden costs include:
- Data warehouse query costs (especially with real-time platforms)
- API overage fees when you exceed included limits
- Implementation and training services
- Premium support plans
- Additional user licenses as you scale
- Custom development for advanced features
Should European companies consider data residency when choosing a platform?
Yes, absolutely. GDPR compliance often requires EU data residency, and European hosting typically provides better performance for European users. Platforms like Sumboard, Luzmo, and ToucanToco offer EU hosting, while US-based platforms may process data outside the EU. Additionally, EUR pricing eliminates currency risk.
Can I migrate from one embedded analytics platform to another?
Migration difficulty varies significantly by platform. Some vendors create lock-in through proprietary data models (like Looker's LookML) or custom integrations. Platforms with standard APIs and common chart types are easier to migrate from. Always ask about export capabilities and migration support before committing to a platform.
How do I calculate ROI for embedded analytics?
Track metrics like customer retention improvement (typically 5-15%), reduction in support tickets (30-50%), increased upsell conversion (10-30%), and time saved on manual reporting (hours per week). Most companies see positive ROI within 6-12 months, with the platform paying for itself through improved customer retention alone.
What's the difference between embedded analytics and traditional BI tools?
Embedded analytics is designed for external users (your customers), while traditional BI serves internal teams. Embedded platforms prioritize white-labeling, multi-tenancy, performance, and ease of integration. Traditional BI tools often struggle with customer-facing requirements and can have complex pricing models that don't scale well for external users.
