Best Embedded Analytics Tools for 2025

You’re moving fast, but the competition is moving faster. In the world of SaaS, every feature launch, every analytics update, and every customer expectation feels like a race against time.

You could build analytics from scratch—spend months on development, strain your engineering resources, and sink money into maintaining it. Or you could embed analytics tools built specifically to handle those challenges.

The right embedded analytics tool isn’t just a dashboard—it’s a shortcut to faster time-to-market, smarter build-vs-buy decisions, and analytics that don’t just support your product but actively drive revenue.

But with so many platforms out there, how do you pick the right one?

In this guide, we’ll break down the 12 best embedded analytics tools for 2025, helping you identify the one that fits your product, your roadmap, and your growth goals.

Key Recap:

Tool	Best for:
Qrvey	Best for SaaS companies needing a scalable, multi-tenant analytics solution with seamless white labeling, cloud integration and unlimited licensing.
Yellowfin BI	Best for organizations focused on operational reporting and automated data storytelling for internal use cases.
Sisense	Best for teams requiring customizable dashboards and developer SDKs for complex, internal analytics workflows.
Sigma	Best for companies leveraging a spreadsheet-style interface for cloud data analysis, especially with Snowflake integration.
Luzmo	Best for smaller SaaS companies looking for lightweight, quick-to-deploy embedded dashboards.
Reveal BI	Best for businesses requiring self-hosted analytics solutions with customizable SDK dashboards.
Looker (Google Cloud)	Best for enterprises deeply integrated into Google Cloud, needing robust data modeling via LookML.
Tableau Embedded Analytics	Best for Salesforce ecosystem users requiring advanced, interactive visualizations.
AWS QuickSight	Best for AWS-native businesses requiring embedded analytics with machine learning insights.
Power BI Embedded	Best for organizations already invested in the Microsoft ecosystem requiring API-heavy dashboard customization.
ThoughtSpot Embedded	Best for search-driven analytics and AI-powered anomaly detection for large datasets.
Domo	Best for real-time business intelligence with customizable visualizations and scalable deployment options.

Top 12 Embedded Analytics Tools for 2025

Embedded analytics tools empower SaaS companies to offer customizable and self-service analytics directly within SaaS applications. Below, we’ve compiled a list of the 12 best embedded analytics tools for 2025, evaluating their capabilities, strengths, and areas where they shine.

1. Qrvey

Qrvey specializes in providing software development teams with a robust multi-tenant analytics solution. With a focus on addressing the unique needs of SaaS companies, Qrvey helps teams streamline their analytics capabilities, reduce development overhead, and deliver value faster.

Key Features

• Native data lake for enterprise-scale analytics, enhancing data readiness
• Unified data pipeline that connects to any data source, regardless of data type
• Seamless integration into multi-tenant SaaS platforms
• Native semantic layer for mapping permissions on the fly
• Suite of APIs supporting advanced use cases beyond traditional analytics
• Unlimited users, dashboards, and environment deployments with flat-rate pricing

Pros

• Built specifically for multi-tenant SaaS environments
• Embedding and customization is off-the-charts flexible and powerful
• Highly scalable with strong support for diverse data sources
• Comprehensive API suite for advanced analytics workflows
• Unlimited licensing model reduces cost unpredictability

Cons

• May not be suitable for non-SaaS or single-tenant use cases
• Ideal solution for external-facing analytics use cases, not for internal BI

Pricing: 

Qrvey’s pricing is tailored to your specific needs and chosen features. Contact Qrvey for a personalized quote.

BONUS: Get a glimpse of Qrvey’s powerful customization options in this guided demo below.

2. Yellowfin BI

Best for: organizations focused on operational reporting and automated data storytelling for internal use cases.

Source: yellowfinbi.com

Yellowfin BI is a business analytics platform from Australia that helps organizations use data for operational reporting. Core features include automating data analysis, interactive dashboards, and storytelling capabilities. However, Yellowfin primarily focuses on internal analytics.

Key Features:

• Strong support for chart-level customization
• Automation of data insights (available as an add-on)
• Interactive dashboards for operational reporting
• Custom data storytelling features

Pros:

• Good support for chart-level customization
• Strong data storytelling capabilities
• Automated data insights available as an add-on

Cons:

• Direct connection to databases only; scaling query performance is left to the customer
• iFrame embedding on a separate subdomain, not within the SaaS application itself
• Server-based hosting relies on server clustering for scaling

Pricing: 

Pricing details are not published. Options may include revenue share models, server core-based pricing, or a price-per-unit utility model.

Read our in-depth comparison here.

3. Sisense

Best for: teams requiring customizable dashboards and developer SDKs for complex, internal analytics workflows.

Source: sisense.com

Sisense offer business analytics software aimed at helping companies turn data into actionable insights. Sisense is primarily used for internal analytics

Sisense offers business analytics software aimed at helping companies turn data into actionable insights, and Sisense reviews often highlight its strength in internal analytics rather than embedded SaaS solutions.

Key Features:

• Developer SDK for dashboard customization
• Range of UI customization options
• Data visualization and reporting tools

Pros:

• Strong support for developer SDKs for dashboards
• Wide range of UI customization options

Cons:

• Expensive server-based deployment
• Integration with multi-tenant security models is challenging
• Relational data model requires third-party ETL tools for data ingestion

Pricing: 

Pricing details are not published. Historically, Sisense charges for user licenses and individual server licenses. If you’re looking for Sisense alternatives, some other providers may offer more flexible pricing models.

Read our in-depth comparison here.

4. Sigma

Best for: companies leveraging a spreadsheet-style interface for cloud data analysis, especially with Snowflake integration.

Source: Sigmacomputing.com

Sigma is a cloud analytics platform known for its spreadsheet-style interface, giving internal users access to analyze data from their cloud data warehouses.

Key Features:

• Familiar spreadsheet-like user interface
• Supports Snowflake data warehouse integration
• UI customization features

Pros:

• Familiar Excel-like interface for ease of use
• Strong integration with Snowflake
• Good UI customization capabilities

Cons:

• Relies heavily on iFrames for embedding dashboards
• No native data management; data preparation is left to external tools
• High costs for concurrency with Snowflake usage

Pricing: 

Base platform fee starts at $30k annually, with $1,000 annual user fees for dashboard creation and limited viewer licenses.

5. Luzmo

Best for: smaller SaaS companies looking for lightweight, quick-to-deploy SaaS embedded analytics dashboards.

Source: Luzmo.com

Formerly known as Cumul.io, Luzmo is a Belgium-based embedded analytics platform offering dashboard-building capabilities.

Key Features:

• Embedded dashboard builder
• Quick deployment for testing
• Support for customizable dashboard layouts

Pros:

• Quick to set up and test dashboards
• Embedded dashboard-building capabilities

Cons:

• Pricing becomes expensive for larger SaaS deployments
• No native data management
• Limited support for multi-tenant user security features

Pricing: 

Base package starts at $12k per year for 1 dashboard creator and 100 viewers. Elite package costs $37k per year for 30 creators and 1,000 viewers.

6. Reveal BI

Best for: businesses requiring self-hosted analytics solutions with customizable SDK dashboards.

Source: Revealbi.io

Reveal BI, owned by Infragistics, is a self-hosted BI solution focused on embedded analytics.

Key Features:

• Embedded SDK for analytics
• Customizable data visualizations

Pros:

• Embedded SDK for advanced analytics workflows
• Good customization options for data visualizations

Cons:

• No native data management
• iFrame dependency for dashboards

Pricing: 

Pricing details are not published.

7. Looker (Google Cloud)

Best for: enterprises deeply integrated into Google Cloud, needing robust data modeling via LookML.

Source: Google.com

Looker, part of Google Cloud, is a modern analytics and business intelligence platform known for its robust data modeling capabilities.

Key Features:

• Strong data modeling with LookML
• Embedded dashboards and analytics tools
• Integration with Google Cloud services
• Flexible API support

Pros:

• Flexible data modeling capabilities for generating valuable insights across diverse datasets.
• Seamless integration with BigQuery and Google Cloud
• Extensive API support

Cons:

• Steep learning curve for LookML
• Expensive licensing model

Pricing: 

Pricing is customized based on deployment requirements.

8. Tableau Embedded Analytics

Best for: Salesforce ecosystem users requiring advanced, interactive data visualizations.

Source: Tableau.com

Tableau Embedded Analytics is part of Salesforce and offers robust data visualization tools designed for embedding dashboards into SaaS applications.

Key Features:

• Interactive data visualizations
• Embedded analytics APIs
• Integration with Salesforce ecosystem
• Custom dashboard configurations

Pros:

• Advanced data visualization capabilities
• Strong Salesforce integration

Cons:

• Expensive licensing costs
• Steep learning curve

Pricing: 

Pricing varies based on deployment and usage.

9. AWS QuickSight

Best for: AWS-native businesses requiring embedded analytics with machine learning insights.

Source: aws.amazon.com

AWS QuickSight is Amazon’s cloud-native business intelligence service, offering embedded analytics capabilities directly integrated with the AWS ecosystem.

Key Features:

• Integration with AWS data sources
• Embedded dashboard capabilities
• Machine learning and artificial-intelligence powered insights
• Interactive dashboards and reporting

Pros:

• Seamless integration with AWS cloud infrastructure
• Machine learning insights for predictive analytics
• Pay-per-session pricing model

Cons:

• Limited advanced customization options for embedded dashboards
• UI and user experience can feel less polished compared to competitors

Pricing: 

Pricing is based on a pay-per-session model, starting at $0.30 per session.

10. Power BI Embedded

Best for: organizations already invested in the Microsoft ecosystem requiring API-heavy dashboard customization.

Source: azure.microsoft.com

Power BI Embedded, part of Microsoft’s Power BI suite, allows organizations to integrate interactive reports and dashboards into their applications.

Key Features:

• Embedded dashboards and reports
• Integration with Microsoft Azure services
• Custom data visualization capabilities
• Strong developer API support

Pros:

• Integration with the Microsoft ecosystem
• Extensive API support for customization
• Flexible deployment options

Cons:

• Complex licensing model
• Can become expensive at scale

Pricing: 

Pricing varies based on capacity and usage tiers, starting from Azure-based licensing.

11. ThoughtSpot Embedded

Best for: search-driven analytics and AI-powered anomaly detection for large datasets.

Source: thoughtspot.com

ThoughtSpot Embedded focuses on providing search-driven analytics and AI-powered insights for SaaS applications.

Key Features:

• Search-driven analytics interface
• AI-powered insights and anomaly detection
• Embedded visualizations and reports
• Integration with major cloud data warehouses

Pros:

• Strong search-driven analytics capabilities combined with collaboration features to enable team-wide data exploration
• AI-powered insights for actionable intelligence
• Integration with cloud data platforms

Cons:

• Steeper learning curve for advanced features
• High licensing costs for larger deployments

Pricing: 

Pricing is customized based on deployment requirements and usage.

12. Domo

Best for: real-time business intelligence with customizable visualizations and scalable deployment options.

Source: domo.com

Domo offers a cloud-based analytics platform focused on delivering embedded analytics, visualizations, and reporting tools for business applications.

Key Features:

• Fully embeddable analytics dashboards
• Real-time data integration
• Customizable data visualizations
• Strong API support

Pros:

• User-friendly dashboards and visualizations
• Real-time data integration for real-time insights, ensuring up-to-date decision-making.
• Scalable deployment for growing datasets

Cons:

• Expensive licensing and subscription costs
• Performance issues with very large datasets

Pricing: 

Pricing is customized and typically tailored to enterprise requirements.

What is Embedded Analytics Software?

Embedded analytics software empowers SaaS companies to offer customizable and self-service analytics directly within SaaS applications.

It enables SaaS companies to offer more than one-size-fits-all static reporting. Customers want their reports to be unique to their use case. Data exploration on their terms.

It fuels competitive differentiation by providing data-driven insights that guide smarter business strategies. This holds true no matter what industry you’re in.

Embedded analytics platforms, however, are not all the same. Most focus on how many colors a bar chart can handle, but the reality is that it starts with data. Companies understand that without a scalable, performant, multi-tenant data layer, the charting library almost doesn’t matter.

Multi-tenant analytics software requires extra layers of application logic to enforce tenant and user data security before anyone explores data.

How Do Embedded Analytics Tools Compare for Multi-Tenant Analytics?

Most solution providers think embedded analytics is only about the charts. However, embedding within multi-tenant SaaS environments requires more than internal analytics.

When choosing an embedded analytics tool, let’s look at areas like:

• How complete are they for embedding within a multi-tenant SaaS application?
• How cost-effective are they to scale concurrent queries?
• How well do they support personal analytics experiences?

Watch a demo now to see for yourself.

Essential Features to Look For When Choosing Embedded Analytics Tools

When evaluating embedded analytics tools, it’s important to consider the following essential features:

Embeddability

Embedded analytics shouldn’t just be an add-on—it should feel like an organic part of your application. A seamless, integrated experience ensures that users stay engaged without feeling like they’ve been transported to another platform.

What matters most:

• Seamless Integration: Whether through APIs, SDKs, or iframe embedding, analytics tools should integrate smoothly into your SaaS platform. Tools like Qrvey’s native API suite ensure flexibility and ease in embedding across environments.
• Customization: Analytics components must match your brand identity, from colors and fonts to button styles and dashboard layouts. Qrvey’s white-labeling capabilities guarantee a cohesive user experience.
• Native User Experience: Dashboards, charts, and data workflows should match the navigation and responsiveness of your core product.
• Environment Flexibility: Whether in staging, testing, or production environments, the tool should behave predictably and require minimal configuration.
• Scalability: As your user base grows, your embedded analytics must handle increased concurrent queries and complex datasets without lag.

When embeddability is done right, analytics become a core feature, not an afterthought, seamlessly enhancing user experience and delivering lasting value.

White Labeling

White labeling isn’t just about hiding third-party logos—it’s about making analytics feel like they belong in your product. For SaaS applications, analytics tools must integrate so seamlessly that users don’t notice a difference.

What matters most:

• Brand Consistency: Colors, typography, and UI components must align with your product’s design language. With Qrvey’s customizable dashboards, every detail can reflect your branding.
• Seamless Transitions: Moving between core product features and analytics dashboards should feel natural, without jarring transitions.
• Customization Flexibility: Tailor dashboards, reports, and visual elements to meet your customers’ unique requirements. Qrvey’s granular personalization controls ensure each tenant’s experience is unique.
• No Third-Party Branding: Analytics should look and feel like a native feature, with no traces of external vendor logos.
• Tenant-Level Personalization: In multi-tenant setups, every tenant should be able to experience analytics that feels tailored to their business needs.

When analytics looks, feels, and behaves like a native feature, it becomes a trust-builder, enhancing engagement and brand loyalty.

Scalability for Concurrency

Analytics tools are often judged by how well they scale under pressure. As your user base grows, dashboards must remain fast, responsive, and reliable, regardless of the load.

What matters most:

• High-Concurrency Support: The tool must efficiently handle thousands of concurrent users without slowing down.
• Optimized Query Performance: Every query, dashboard refresh, and user interaction should deliver results instantly. Qrvey’s native data pipeline ensures query efficiency even under heavy loads.
• Elastic Infrastructure: Analytics should scale horizontally to meet demand without manual intervention.
• Cost-Efficient Scaling: Tools with predictable pricing models, like Qrvey’s flat-rate pricing, allow growth without runaway costs.
• Load Management: The system should intelligently manage concurrent data requests to prevent bottlenecks.

A scalable analytics tool isn’t just about handling growth—it’s about ensuring consistent performance at every stage of your product’s journey.

Granular Data Security & Access Controls

In multi-tenant SaaS environments, data security isn’t optional—it’s mandatory. Every user must see only the data they’re authorized to view, without compromise.

What matters most:

• Row- and Column-Level Security: Fine-tuned controls ensure data visibility is restricted based on user roles and permissions.
• Parent Application Inheritance: Permissions should align seamlessly with your core application’s security protocols. With Qrvey’s native multi-tenant security model, this integration is built-in.
• Role-Based Access Controls: Admins, analysts, and end-users must have clearly defined access levels without manual configuration.
• Token-Based Authentication: JWT tokens or SSO (Single Sign-On) should be supported for seamless authentication flows.
• Audit Trails: Detailed logs of user activity ensure accountability and security compliance.

When access controls are built with multi-tenancy in mind, security becomes proactive, not reactive.

Deployability to SaaS Platforms

For SaaS companies, deploying analytics across environments shouldn’t feel like reinventing the wheel every time.

What matters most:

• Flexible Deployment Models: Whether cloud-hosted, self-hosted, or hybrid, deployment should align with your infrastructure. Qrvey’s AWS-native deployment offers flexibility and security.
• Seamless Environment Mapping: Moving analytics from staging to production shouldn’t require reconfiguration.
• Security Alignment: Security models should inherit permissions from your SaaS platform without requiring manual intervention.
• Version Control: Content deployments across multiple environments should be manageable and predictable.
• DevOps Integration: Analytics tools should align with CI/CD workflows for automated deployment processes.

Smooth deployment workflows free your engineering team from unnecessary maintenance, allowing them to focus on building product value.

In the end…

Choosing the right embedded analytics solution isn’t just about ticking feature boxes—it’s about addressing real concerns that can make or break your implementation.

Key obstacles many companies face include:

• Time to Market: Will the analytics tool accelerate your roadmap, or will it slow down releases with endless setup and troubleshooting?
• Engineering Overhead: Will your developers spend more time maintaining analytics than building your core product?
• Scalability and Costs: As your user base grows, will performance hold up without costs spiraling out of control?
• Data Security: Can the tool seamlessly inherit your application’s security protocols, or will it require endless custom workarounds?
• End-User Adoption: Will customers find analytics intuitive and valuable, or will they avoid using it altogether?

These aren’t small concerns—they’re make-or-break factors that separate analytics tools that deliver value from those that create headaches.

This is where Qrvey stands apart.

Qrvey addresses these challenges with a full-stack embedded analytics solution built specifically for multi-tenant SaaS environments. With:

• A native data lake to handle large and complex datasets efficiently
• No-code automation workflows to reduce engineering overhead
• Granular access controls that align seamlessly with your application’s security models
• Unlimited user licensing for predictable costs at scale
• A focus on developer flexibility with a robust suite of APIs

Qrvey doesn’t just offer charts and dashboards—it offers a scalable foundation that empowers you to deliver value faster, outcompete on features, and monetize analytics capabilities without compromising on security or user experience.

When the stakes are this high, choosing the right analytics partner isn’t optional—it’s essential.

See Qrvey in action today.