What is embedded analytics?
Quick definition
Embedded analytics is the integration of data analytics capabilities directly into your software application, making insights available to your customers within your product's interface.
Think of embedded analytics as giving your customers a window into their data without forcing them to leave your application. Instead of directing users to external reporting tools or requiring them to export data for analysis, embedded analytics brings the insights directly to where your customers already spend their time.
This isn't about creating another dashboard for your internal team. Embedded analytics is specifically designed for your customers — the people who pay for your software and generate the data that makes those analytics possible.
According to a 2024 survey by Dresner Advisory Services, 78% of organizations consider embedded analytics a critical or very important initiative.
The evolution from internal to customer-facing
For decades, business intelligence tools were built for internal consumption. Companies would analyze their data internally, then share conclusions through reports, emails, or presentations. The data insights lived in a separate world from the products that generated them.
Embedded analytics flips this model. Instead of keeping insights locked away in internal tools, it surfaces the most valuable data directly to customers as a core product feature. This transformation has become a competitive necessity for modern SaaS companies.
Why your SaaS needs embedded analytics
The question isn't whether your SaaS should offer analytics to customers — it's how quickly you can implement it. Here's why embedded analytics has become table stakes for modern software products:
1 Customer expectations have changed
Today's software users expect data transparency. They want to understand how their business is performing, what trends are emerging, and where opportunities exist. If your product collects customer data but doesn't provide insights back, you're missing a fundamental value proposition.
Common reality
Many SaaS companies I've spoken with recently still export CSV files and email them to clients, who then import the data into Power BI or Tableau to build their own dashboards. This creates a massive headache: customers constantly request data exports, someone has to manually generate and send files, then clients struggle to import and visualize the data (often hiring data analysts or external services). Imagine if Stripe sent you CSV files instead of their beautiful dashboards to understand your payment data — that's the reality for most SaaS customers today.
SaaS companies often use this image to explain their customers why it will take 2 years to answer how many customers they have.
2 Competitive differentiation
In crowded markets, embedded analytics can be the feature that wins deals. When prospects compare solutions, the one that offers comprehensive insights alongside core functionality typically has a significant advantage.
3 Higher customer retention
Analytics create stickiness. When customers rely on your product not just for transactions but for understanding their business performance, switching costs increase dramatically. You become integral to their decision-making process.
4 Premium pricing opportunities
Advanced analytics features justify higher price points. Customers will pay more for software that provides actionable insights, especially when those insights directly impact their revenue or operational efficiency.
You can read more about how to monetize embedded analytics in our Complete guide to customer-facing analytics.
Types of embedded analytics
Not all embedded analytics are created equal. Understanding the different approaches helps you choose the right strategy for your product and customer needs.
1 Operational analytics
These are real-time metrics that help customers understand current performance and take immediate action. Think of Shopify's merchant dashboard showing today's sales, traffic sources, and top-performing products.
Common operational analytics:
- Real-time performance dashboards
- Usage statistics and trends
- System health and uptime metrics
- Current period vs. previous period comparisons
2 Strategic analytics
These provide longer-term insights that inform business strategy and planning. They typically involve historical data analysis, trend identification, and predictive modeling.
Common strategic analytics examples:
- Monthly and quarterly reporting
- Cohort analysis and customer lifetime value
- Market trend analysis
- Forecasting and predictive insights
3 Self-service analytics
This empowers customers to create their own reports and explore data independently. It's the most sophisticated level but offers the highest customer value.
Self-service features:
- Custom report builders
- Drag-and-drop chart creation
- Custom date ranges and filters
- Export capabilities (PDF, Excel, etc.)
Summary
Most successful implementations start with operational analytics for immediate value, then expand to strategic insights, and finally add self-service capabilities as customers become more sophisticated. The key is beginning with what your customers need most today.
📊 Quick readiness assessment
Check how many of these signals apply to your business. The more you check, the higher priority embedded analytics should be for your SaaS.
Quick scoring: 0-2 checked = Future consideration • 3-4 checked = Good opportunity • 5+ checked = High priority
Implementation approaches
There are several ways to add embedded analytics to your product, each with different trade-offs in terms of development time, customization, and ongoing maintenance.
1 Build from Scratch
Creating a custom analytics solution gives you complete control but requires significant engineering resources. This approach makes sense for companies with unique requirements or substantial development capacity.
Building from scratch means your team will need to develop every component: data processing pipelines, visualization engines, user management, and security controls. You'll need dedicated frontend and backend teams, plus data engineers to handle performance optimization.
Companies like Stripe and Shopify chose this route because analytics is core to their product value proposition. They have large engineering teams (50+ engineers) dedicated to their analytics features and can justify the ongoing investment in maintenance and feature development.
✅ Pros
- Complete customization
- Perfect brand integration
- Full data control
- No vendor lock-in
❌ Cons
- 6-18 month development time
- High engineering costs
- Ongoing maintenance burden
- Complex scalability challenges
2 Embedded analytics platforms
Purpose-built platforms like Sumboard offer the fastest path to production-ready customer-facing analytics. They handle the complexity while allowing customization.
These platforms are specifically designed for embedding analytics into SaaS products. They provide pre-built components for everything from data connections to interactive dashboards, multi-tenancy, and white-labeling. Your engineering team only needs to handle the integration, which typically takes days instead of months.
Most successful SaaS companies choose this route because it offers the best balance of speed, cost, and functionality. You get professional analytics capabilities immediately while your engineering team stays focused on your core product features.
✅ Pros
- Days to production
- Pre-built components
- Scalable infrastructure
- Regular updates and features
❌ Cons
- Ongoing subscription costs
- Some customization limits
- Vendor dependency
3 Traditional BI tools embedding
Just imagine that you have 50 customers that want to have access to more than a CSV file and you have to setup a license for each of them.
Tools like Tableau or Power BI offer embedding capabilities, but they weren't designed primarily for customer-facing use cases. These are powerful internal business intelligence tools that have added embedding as an afterthought.
The main challenge with traditional BI tools is that they're built for data analysts, not end-users. Their interfaces are complex, requiring training to use effectively. While they offer robust analysis capabilities, the user experience often feels disconnected from your product.
Some companies try this route because they're already using these tools internally. However, the licensing costs, integration complexity, and limited white-labeling options often lead to a suboptimal customer experience and higher total cost of ownership.
✅ Pros
- Powerful analysis features
- Familiar for internal teams
- Established vendors
❌ Cons
- Not designed for customer-facing
- Complex licensing models
- Limited white-labeling
- High per-user costs
Use this to make a decision.
The build vs. buy decision
This is perhaps the most critical decision you'll make. The right choice depends on your team's capabilities, timeline, and specific requirements.
When to build
Consider building if you have a large engineering team, unique data requirements, and analytics are core to your product differentiation.
Companies like Stripe, Shopify, and HubSpot built custom analytics because their requirements were so specific and central to their value proposition that off-the-shelf solutions couldn't deliver the experience they needed.
Build if you have:
- A team of 50+ engineers with dedicated data/analytics expertise
- Unique data models that don't fit standard patterns
- Analytics as a primary product differentiator
- 12+ months to invest in development
- Ongoing resources for maintenance and scaling
When to buy
Choose a platform if you want to ship analytics quickly and focus your engineering resources on your core product.
Most SaaS companies fall into this category. Analytics are important but not the primary reason customers buy your product. You want professional, customizable dashboards without the complexity of building from scratch.
Buy if you have:
- Standard SaaS metrics and data patterns
- Limited engineering bandwidth
- Pressure to ship analytics quickly
- Standard security and compliance requirements
- A preference to focus on core product development
A quick overview of different approaches to embedded analytics implementation. Shotout to GoodData team for this video.
What to take into account when making a decision.
What to consider before choosing a solution
📄 Complete this checklist to evaluate your embedded analytics needs and readiness and prepare for the next steps.
Before diving into specific platforms and vendors, it's crucial to understand the key factors that will determine which solution works best for your specific situation. These decision criteria will help you quickly identify what might be perfect (or a complete showstopper) for your project.
1 Data source compatibility
Most tools connect to common databases (PostgreSQL, MySQL, MongoDB), but if you're using something non-standard or cutting-edge, this can quickly narrow your options.
2 Integration method
How analytics get embedded affects development speed and user experience. Modern iframe implementations offer security, fast deployment, and cross-platform compatibility. Other methods like web components or direct API integration provide more control but require significantly more development time.
3 Native look and feel
If you need analytics that perfectly match your design system and feel truly native, you have limited options: build custom or use headless analytics platforms.
4 Performance requirements
Loading speed depends on both the tool and your database. Look for configurable caching options and test with real data volumes during proof-of-concept.
5 Essential features
Beyond basic charts and dashboards, identify must-have features early to avoid surprises later in the evaluation process.
6 Security and compliance
Data sovereignty requirements can quickly eliminate cloud-only providers. Check with your compliance team early to understand hosting restrictions.
7 Pricing model alignment
Costs vary dramatically based on number of users, features, white-labeling, and support levels. Get ballpark figures early to avoid wasting time.
8 Implementation timeline
Consider both initial setup time and ongoing maintenance requirements. Some solutions are quick to start but complex to scale.
Before you start evaluating
Create a requirements checklist with your must-haves, nice-to-haves, and deal-breakers. This will help you move quickly through vendor conversations and focus on solutions that actually fit your needs rather than getting distracted by impressive demos that don't address your core requirements.
Best embedded analytics tools to consider in 2025
The embedded analytics landscape has transformed in 2025. From purpose-built platforms to traditional BI tools adding embedding capabilities, the market offers more choices than ever. But with this variety comes the challenge of finding the right fit for your specific needs.
Key market trends in 2025:
- Shift from traditional BI tool embedding to purpose-built platforms
- Focus on developer experience and rapid implementation
- Emphasis on white-labeling and native integration
- Rise of AI-powered analytics capabilities
We've analyzed and compared the top solutions across two main categories:
- Purpose-built embedded analytics platforms: Modern solutions designed specifically for customer-facing analytics, offering faster implementation and better user experience.
- Traditional BI tools with embedding: Established platforms that have added embedding capabilities, often preferred by companies already using these tools internally.
For each tool, we'll look at key factors like:
Implementation
Setup time, technical requirements, and integration methods
Features & flexibility
Customization options, white-labeling, and capabilities
Pricing model
Cost structure, scalability, and included features
Let's dive into each solution, starting with platforms specifically designed for modern embedded analytics needs.
Purpose-built embedded analytics platforms
Sumboard offers the fastest path to production-ready customer-facing analytics. Built specifically for SaaS companies who want professional dashboards without the complexity of building from scratch.
Use Sumboard if:
- You want to ship analytics in days, not months
- You need white-label customization with your branding
- You want to focus engineering resources on core product
- You need reliable, scalable infrastructure
Custom PDF builder
Branded PDF reports that match your product's design and feel completely native to your customers.
Translate and localize
Serve global customers with dashboards in their native language and time zone automatically.
Share and embed
Seamlessly embed dashboards in your product or share via secure links - your customers stay in your ecosystem.
Interactive filters
Let your customers explore and drill down into their data with intuitive filtering controls.
Email schedules
Keep your customers informed with automated report delivery on their preferred schedule.
Export in PDF and Excel
Give customers the data they need in the formats they want, with full white-label branding.
White-label
Customize chart colors, add your logo, and match your brand perfectly so analytics feel native to your product.
Compare over period
Help your customers spot trends and track progress with built-in period comparison features.
Core capabilities:
- Drag-and-drop chart builder
- Multi-tenant architecture
- Database and API data sources
- Cloud and self-hosted options
- 10-minute integration
Pricing:
- Unlimited users
- All core features
Developer-first tool with headless architecture for lightning-fast customer-facing dashboards with complete control over user experience.
Best for: Maximum customization and performance
Embedding: Web component or React SDK (no iframes)
Pricing: Not disclosed
End-to-end embedded analytics platform built specifically for multi-tenant SaaS applications.
Best for: Multi-tenant SaaS with self-service needs
Embedding: JavaScript widgets (no iframes)
Pricing: Must contact sales
Belgian-made embedded analytics platform with drag-and-drop interface and API-first approach.
Best for: API endpoint data with self-service BI
Embedding: Web component
Pricing: Starting at $995/month for 100 monthly active viewers
YC-backed startup providing cloud-only service designed for fast market entry with modern UI.
Best for: Startups wanting fast time-to-market
Embedding: iframe or web component
Pricing: Starting at $795/month for 3 embedded dashboards
Purpose-built solution that pivoted from traditional BI to customer-facing analytics.
Best for: Fixed annual pricing with fluctuating user base
Embedding: iframe or SDK
Pricing: Must contact sales
BI tools with embedding features
Traditional business intelligence tools that have added embedding capabilities. While powerful for internal use, they often have limitations when used for customer-facing scenarios.
Should you choose a BI platform with embedding features?
BI tools excel at internal decision-making but weren't built for customer-facing experiences. Common limitations include restricted customization, slower loading times, and complex multi-tenant workarounds.
Power BI Embedded
Microsoft's mature BI platform with iframe-based embedding. Enterprise-grade with high security, but requires workarounds for multi-tenancy and customer-facing use cases.
Best for: Microsoft ecosystem, internal + customer BI
Embedding: iframe only
Pricing: Starts at around $750/month for A1 capacity node.
Looker Embedded
Google Cloud's enterprise-grade BI platform with real-time capabilities. Very powerful but expensive and limited customization options.
Best for: Google Cloud ecosystem, enterprise budgets
Embedding: iframe only
Pricing: Must contact sales
Tableau Embedded
Powerful data exploration with high learning curve. Swiss army knife approach.
Pricing: Must contact sales
Metabase Embedded
Popular open-source BI with full tool embedding capability.
Pricing: $500/month + $10 per user
Sigma Embedded
Cloud-first BI with iframe embedding and live query capabilities.
Pricing: Must contact sales
GoodData Embedded
One of the few BI tools offering web component embedding vs iframe.
Pricing: Must contact sales
Qlik Embedded
Suite of analytics tools with enterprise-grade embedding features.
Pricing: $200/mo for 10 users
See how Cashpad integrated embedded analytics into their product here: here OR read this case study on how Orbility replaced their old reporting tool with an embedded analytics solution here
Ready to move from research to action?
Now that you understand the landscape of embedded analytics tools and their trade-offs, it's time to create your implementation plan. Whether you've decided to build custom, buy a platform, or explore hybrid approaches, the following roadmap will guide you from initial strategy to successful launch.
Getting started: Your implementation roadmap
Ready to add embedded analytics to your product? Here's a practical roadmap that works regardless of which approach you choose.
Step 1: Define your analytics strategy
Before touching any code or evaluating platforms, clearly define what success looks like.
Key questions to answer:
- What are the top 5 metrics/reports your customers ask about most?
- How do customers currently access this data?
- What decisions will these analytics enable?
- How will you measure the success of embedded analytics?
Step 2: Audit your data infrastructure
Understanding your current data setup determines implementation complexity and timeline.
Infrastructure checklist:
- Where is customer data currently stored?
- How clean and consistent is the data?
- What are your data security and compliance requirements?
- Do you have existing APIs for data access?
Step 3: Choose your implementation approach
Based on your requirements and resources, select build vs. buy and specific platforms.
Fast track (2-4 weeks)
Use purpose-built platform like Sumboard
Custom build (6-12 months)
Chart libraries + custom backend
Hybrid (2-4 months)
Headless platform + custom frontend
Step 4: Start with a pilot
Launch analytics for a subset of customers or features to validate your approach.
Pilot strategy:
- Select 10-20 engaged customers for beta testing
- Focus on 3-5 core metrics/reports initially
- Gather feedback before broader rollout
- Measure usage patterns and customer satisfaction
Step 5: Scale and optimize
Based on pilot results, expand features and roll out to your entire customer base.
Scaling considerations:
- Performance optimization for larger datasets
- Advanced features based on customer requests
- Integration with customer workflows
- Training and onboarding processes
Step 6: Measure and iterate
Continuously improve based on usage data and customer feedback.
Success metrics to track:
- Analytics feature adoption rates
- Time spent in analytics sections
- Customer satisfaction scores
- Support ticket reduction
- Revenue impact (upsells, retention)
