Embedded analytics has become a standard capability across modern business software. Instead of switching to a separate BI platform, users increasingly expect reports, dashboards, and data exploration to be available within the applications they already use. As adoption grows, software teams are placing greater emphasis on delivering analytics as part of the product experience rather than as an additional tool.

That shift is reflected in the market. The embedded analytics market is projected to grow from $56.89 billion in 2026 to $162 billion by 2035, while more than two-thirds of enterprises already use embedded analytics solutions to support operational decision-making and day-to-day workflows.

For organizations planning to introduce embedded analytics, one question often comes up early: should the capability be built internally or delivered through an existing platform? Building provides complete control over the analytics stack but also requires ongoing investment in data pipelines, visualization, security, scalability, and maintenance. An established platform reduces much of that engineering effort while allowing teams to focus on the parts of the product that are unique to their business.

This guide compares seven embedded analytics platforms, examining their capabilities, pricing approaches, deployment options, strengths, limitations, and customer feedback to help you evaluate which one best fits your requirements.

Best tools for embedded analytics  

• Zoho Analytics: A balanced choice for organizations that need embedded analytics, AI capabilities, and flexible deployment without the pricing typically associated with enterprise BI platforms.
• Reveal BI: Well suited to SMBs and mid-market SaaS companies looking for SDK-based embedding and greater control over the in-app analytics experience.
• ThoughtSpot: Designed for organizations that prioritize search-driven analytics, AI-assisted data exploration, and direct integration with modern cloud data warehouses.
• GoodData: A strong option for enterprises that require governed metrics, multi-tenant deployments, and consistent reporting across large customer environments.
• Qrvey: Best suited to SaaS providers running on AWS that want data ingestion, transformation, and embedded analytics within a single platform.
• Domo: Fits organizations seeking embedded analytics alongside data integration, workflow automation, and operational business intelligence.
• Yellowfin BI: A good choice for ISVs and software vendors that want to combine embedded dashboard tools with data storytelling and collaborative reporting.

Zoho Analytics  

Overview  

Zoho Analytics combines data preparation, reporting, AI-assisted analysis, and embedded analytics within a single platform. Rather than focusing only on  business intelligence dashboard embedding, it provides the data connectivity, security, administration, and customization needed to support customer-facing analytics at scale.

The platform connects to more than 500 data sources, including databases, cloud data warehouses, business applications, spreadsheets, and cloud storage services. Organizations can deploy it in cloud, on-premises, or hybrid environments, making it suitable for businesses with different infrastructure and compliance requirements.

For software vendors and SaaS providers, Zoho Analytics supports embedded deployment through APIs, SDKs, and iframe embedding, along with white-labeling, domain mapping, role-based access control, and multi-tenant architecture. These capabilities allow teams to integrate analytics into existing products while maintaining their own branding and user experience (Know more about embedded analytics for SaaS or industry specific deployments like embedded analytics for fintech.).=

Core features  

• 500+ data connectors for databases, cloud warehouses, business applications, files, and web services, supported by built-in data preparation and transformation tools.

• Zia AI assistant for natural-language querying, automated insights, anomaly detection, forecasting, and what-if analysis.

• Interactive report and dashboard builder with more than 50 visualization types, filters, drill-down analysis, alerts, and scheduled reporting.

• Security and governance including row-level security, SSO/SAML, audit logging, version history, and role-based permissions for multi-tenant deployments.

• Flexible embedding options through iframes, REST APIs, JavaScript SDKs, white-labeling, and custom domains(Know more about Embed API).

• Scalable deployment architecture supporting cloud, on-premises, and hybrid implementations as reporting requirements grow.

Pros and cons  

 Pricing  

Zoho Analytics publishes its pricing, making it easier for organizations to compare plans before engaging with the sales team. Subscription plans scale based on the number of users, rows, and workspaces rather than credit-based consumption models. Embedded and OEM pricing is quoted separately according to deployment model and end-user volume, but deployments are generally priced below the six-figure contracts often associated with enterprise-focused platforms such as ThoughtSpot, Domo, and GoodData. A free trial is available for teams that want to evaluate the platform before making a purchase decision.

What users say  

Customer reviews frequently mention three themes: ease of implementation, flexible embedding options, and overall value compared with other embedded analytics platforms.

Todd Penny, Founder and CEO of GoCodes, described his experience after integrating Zoho Analytics into the company's customer-facing product:

"We have been using Zoho Analytics for about 2 years now. We were so impressed with the flexibility, ease of use and low cost that we decided in integrating it with our customer solution. The integration was straightforward and therefore we opted a white label solution. Now our customers also love Zoho Analytics equally. Zoho Analytics has been highly reliable, secure and any queries have always been promptly addressed."

The combination of broad connectivity, embedded deployment options, and transparent pricing makes Zoho Analytics a practical choice for organizations building customer-facing reporting, including teams implementing embedded sales analytics within CRM and revenue platforms.

Reveal BI

Overview  

Unlike many business intelligence platforms that later introduced embedded analytics, Reveal BI was developed with embedded deployment as a primary use case. Its architecture centers on native SDKs that integrate directly with applications across frameworks such as .NET Core, Java, Node.js, React, Angular, and Vue, giving development teams greater control over how analytics fits into the surrounding product.

Alongside its embedding capabilities, Reveal BI includes a drag-and-drop dashboard builder, data preparation tools, predictive analytics, and conversational AI features that allow users to explore data through natural-language queries. The platform supports both cloud and on-premises deployments, making it suitable for organizations with different infrastructure requirements.

Core features  

• Native SDK embedding across major frontend and backend frameworks, allowing analytics to integrate directly with the host application.
• Interactive dashboard builder with data blending, calculated fields, predictive analytics, dashboard linking, and statistical functions.
• Conversational analytics that enables users to query data using natural language alongside traditional dashboards.
• White-label support included as part of the standard subscription.
• Cloud and on-premises deployment with support for major cloud providers and server operating systems.
• 30-day SDK trial for evaluating integration and deployment before licensing.

Pros and cons  

Pricing  

Reveal BI uses a fixed annual licensing model rather than charging by user or query volume, which provides more predictable costs as customer adoption grows. Pricing is available through custom quotations based on deployment requirements and is not published publicly.

The vendor also compares this model with the cost of building an embedded analytics platform internally, estimating that an in-house implementation can take up to seven months and cost as much as $350,000[estimated value]. Separate per-user subscriptions are available for organizations using Reveal BI as an internal analytics platform.

What users say  

User reviews on Capterra and SelectHub frequently highlight Reveal BI's ease of use, particularly for teams building embedded analytics without extensive front-end development. Several reviewers also mention that SDK-based embedding provides a more integrated experience than iframe-based deployments.

The most common criticism relates to documentation, particularly for development teams working outside the platform's primary supported languages. Some reviewers also note that conversational analytics requires a short learning period for users who are more familiar with traditional dashboard navigation.

ThoughtSpot

Overview  

Search has always been central to ThoughtSpot's approach to analytics. Rather than navigating folders or pre-built dashboards, users can ask questions in natural language and receive charts, tables, or visual summaries generated from their data. More recently, the platform has expanded that search-first model with Spotter, a collection of AI agents that assist with data exploration, automated insights, and dashboard creation.

ThoughtSpot also integrates directly with major cloud data warehouses, including Snowflake, BigQuery, Databricks, Amazon Redshift, and Azure Synapse. This architecture makes it particularly relevant for organizations that already rely on a modern cloud data stack and want analytics to operate directly against warehouse data.

Core features  

• Search-driven analytics that returns visual answers to natural-language questions without requiring SQL.
• Spotter AI agents for automated insights, dashboard generation, and AI-assisted data exploration.
• Liveboards that provide continuously refreshed dashboards for operational monitoring.
• Direct cloud warehouse querying without duplicating data into a separate analytics store.
• Embedded Analytics SDK and REST APIs for integrating search and dashboards into customer-facing applications.
• Enterprise security including row-level security, embedded SSO, and multi-tenant deployment support on higher-tier plans.

Pros and cons  

Pricing  

ThoughtSpot separates its pricing into ThoughtSpot Analytics for internal business intelligence and ThoughtSpot Embedded for customer-facing deployments. Analytics plans start at approximately $25–$50 per user per month when billed annually.

Embedded pricing follows a consumption-based model and is available through custom quotations. Costs vary according to dashboard usage and query volume, making long-term budgeting more dependent on customer adoption than fixed-license platforms. Third-party buyer data from Vendr reports average annual contracts of around $137,000, with embedded deployments commonly beginning near $200,000 (according to third-party estimates).

What users say  

Reviews on Reddit and G2 consistently praise ThoughtSpot's search experience and the speed with which users can explore large datasets. Spotter's AI capabilities and Liveboards also receive positive feedback from organizations that rely on real-time reporting.

The most common concerns relate to pricing and usability. Several users mention that natural-language search becomes more effective after learning the platform's preferred query patterns, while others note that consumption-based pricing can make costs harder to predict as dashboard usage increases.

GoodData

Overview  

At the center of GoodData's platform is a governed semantic layer that defines business metrics, calculations, and KPI logic once, then applies those definitions consistently across dashboards, embedded applications, APIs, and AI-powered workflows. This approach helps organizations maintain consistent reporting across multiple teams, products, or customer environments without recreating business logic for each deployment.

The platform is designed for embedded analytics and OEM deployments where governance, tenant isolation, and standardized metrics are operational requirements. GoodData extends the same semantic layer to its AI Building Blocks including Agents, Assistants, Copilots, and Autopilots, allowing AI features to work from governed business definitions instead of directly querying raw data.

Core features  

• Governed semantic layer that maintains consistent metric definitions across dashboards, APIs, embedded components, and AI capabilities.
• Multi-tenant workspace architecture that isolates customer environments while sharing the underlying platform infrastructure.
• AI Building Blocks supporting Agents, Assistants, Copilots, and Autopilots through a bring-your-own-LLM architecture.
• Developer SDKs and REST APIs, including React SDKs and IDE extensions, for embedded analytics implementations.
• Flexible deployment options across managed cloud and self-hosted environments to support different security and compliance requirements.
• Model Context Protocol (MCP) Server for exposing governed analytics capabilities to external AI applications. 

Pros and cons  

Pricing  

GoodData does not publish standard pricing. Its current licensing model is based on two editions: Professional and Enterprise, with pricing calculated per workspace rather than per user. In this model, each customer environment or tenant is typically treated as an individual workspace while allowing unlimited internal users.

Third-party estimates suggest entry pricing begins at approximately $1,500 per month (according to third-party estimates), with costs increasing based on deployment model, compliance requirements, and the number of workspaces. Trial access is available, although detailed pricing generally requires a discussion with the sales team.

What users say  

Across G2, Gartner Peer Insights, and TrustRadius, reviewers frequently highlight GoodData's semantic layer and multi-tenant architecture as distinguishing characteristics, particularly for organizations managing large numbers of customer workspaces.

The most common concerns relate to implementation complexity. Users often mention the learning curve associated with MAQL, GoodData's proprietary query language, as well as the time required to become familiar with the platform. Some reviewers also note that the absence of publicly available pricing makes early vendor comparisons more difficult.

Qrvey

Overview  

Qrvey combines data ingestion, transformation, storage, and embedded analytics within a single platform. Alongside dashboarding and visualization, it includes a built-in data lake and data pipeline, allowing organizations to prepare, manage, and deliver customer-facing analytics without relying on a separate warehouse for every deployment.

The platform is designed primarily for AWS environments and supports multi-tenant SaaS applications that need embedded dashboards, customer-level data isolation, and centralized administration. By bringing data processing and analytics together, Qrvey reduces the number of components teams need to integrate and maintain across their reporting stack.

This architecture is particularly relevant for organizations building new embedded analytics capabilities on AWS. Teams that already operate a mature analytics stack on Azure, Google Cloud, or an existing enterprise warehouse may instead evaluate how Qrvey fits alongside their current infrastructure.

Core features  

• Built-in multi-tenant data lake (Elasticsearch-backed) that combines data ingestion, transformation, and visualization within a unified platform.
• JavaScript widget embedding for integrating analytics directly into web applications without relying on iframes.
• Self-service dashboard builder with drill-down analysis, interactive filters, and configurable chart actions.
• No-code workflow automation for scheduled reports, alerts, and event-driven integrations with third-party applications.
• Generative AI capabilities supporting natural-language queries and predictive analytics within the reporting environment.
• Flat-rate licensing with unlimited tenants, helping organizations plan costs as customer deployments expand.

Pros and cons  

Pricing  

Qrvey follows an annual subscription model with flat-fee licensing instead of charging by user or query volume. Plans commonly referenced as Pro and Ultra vary according to the capabilities included, particularly the built-in data engine and transformation services.

Pricing is provided through custom quotations based on deployment requirements, and startup programs are available for qualifying organizations. Because licensing is not tied directly to end-user activity, organizations can estimate long-term costs more predictably as adoption grows.

What users say  

Reviews on Capterra frequently highlight Qrvey's embedded analytics capabilities and workflow automation, with several users noting that connecting data from multiple sources is straightforward. Organizations adopting the platform also point to the advantage of managing data preparation and an embedded reporting tool within the same environment.

The most common feedback relates to ecosystem maturity. Reviewers mention that documentation, community resources, and third-party integrations are still developing compared with longer-established BI platforms. G2 users also note that administrators without prior cloud or API experience may need additional time during the initial setup process.

Domo

Overview  

Domo combines data integration, transformation, dashboards, collaboration, workflow automation, and embedded analytics within a single business intelligence platform. While it is widely used for internal operational reporting, its Domo Everywhere offering also allows organizations to embed dashboards and analytics into customer-facing applications.

The platform includes more than 1,000 pre-built connectors, Magic ETL for visual data preparation, natural-language querying, and tools for building embedded analytics applications. This breadth makes Domo suitable for organizations looking to manage data integration and analytics from a single environment rather than assembling multiple tools for different stages of the reporting process.

For teams evaluating embedded analytics specifically, it is also worth considering whether they need the wider platform capabilities or a solution focused primarily on embedding. For a detailed comparison, see our  Zoho Analytics vs. Domo guide.

Core features  

• 1,000+ pre-built connectors supporting databases, cloud platforms, and business applications.
• Magic ETL, a visual data preparation tool for transforming data without extensive SQL development.
• Domo Everywhere and App Studio for embedding dashboards and building customer-facing analytics applications.
• Natural-language querying that allows business users to explore data without writing SQL.
• Built-in collaboration through Domo Buzz for discussions and sharing within dashboards.
• Predictive analytics and machine learning capabilities for forecasting and anomaly detection.

Pros and cons  

Pricing  

Domo uses a usage-based licensing model that combines per-user subscriptions with a credit system covering data storage, ingestion, and processing. A 30-day free trial with unlimited users is available, while production deployments are priced through custom quotations.

Third-party estimates suggest that mid-sized deployments, approximately 50 users and up to 250 million rows, often fall between $75,000 and $100,000 per year (according to third-party estimates). Several community discussions also note that forecasting costs requires understanding how credits are consumed across different workloads.

What users say  

Reviews on G2, Capterra, and SelectHub frequently highlight Domo's ease of use, extensive connector library, and collaboration features. Many users also value the ability to combine data preparation, dashboards, and embedded analytics within a single platform.

Cost is the most common concern raised in customer feedback. Reviewers regularly describe the platform as expensive, and discussions on Reddit note that the credit-based pricing model can make long-term budgeting more challenging. Some users also feel that organizations focused solely on embedded analytics may not require the full breadth of Domo's capabilities.

Yellowfin BI

Overview  

Yellowfin BI places a strong emphasis on data storytelling by combining dashboards with written narrative, annotations, and supporting context. Instead of presenting charts on their own, the platform helps teams explain what changed, why it matters, and what actions may need to follow. This approach is particularly useful when reports are shared with business users who may not regularly work with analytics.

For embedded deployments, Yellowfin provides a web-based analytics application that supports white-label branding, multi-tenant environments, and flexible deployment across cloud, containerized, and on-premises infrastructure. Its licensing options—including revenue sharing, server-based pricing, and traditional user licensing—also provide organizations with multiple ways to align pricing to their business model.

Core features  

• Data storytelling through Yellowfin Stories and Present, combining dashboards with narrative explanations, annotations, and supporting content.
• Yellowfin Signals for automated anomaly detection and notifications when significant data changes occur.
• Flexible embedded deployment across cloud, containerized, and on-premises environments with support for multi-tenant implementations.
• Collaborative BI tools including dashboard comments, task assignments, and scheduled report distribution.
• Broad connectivity to relational databases, web APIs, spreadsheets, and other business data sources.
• Natural-language querying for conversational data exploration alongside traditional dashboard navigation.

Pros and cons  

Pricing  

Yellowfin BI offers separate pricing models for Enterprise BI and Embedded Analytics deployments. Enterprise licensing is available through user-based, server-based, and tiered plans, while embedded deployments support revenue-sharing, server-based licensing, and other deployment-specific pricing models.

Some third-party sources report entry-level plans beginning at approximately $19 per month (according to third-party estimates), although most embedded deployments require a custom quotation based on implementation scope. A product demonstration and proof-of-concept are available before purchase.

What users say  

Reviews on G2 frequently highlight Yellowfin's storytelling capabilities as a distinguishing feature, particularly for organizations that need reports to communicate business context alongside data visualizations. Users also mention relatively quick deployment for embedded reporting projects.

The most common feedback relates to natural-language querying and advanced analytics. Several reviewers note that natural-language searches work best with well-structured queries, while others feel the platform offers fewer advanced analytical capabilities than products designed for more technical or data science–oriented workloads. Some users working with large datasets also mention data management and performance as areas for improvement.

How the 7 tools compare  

What features to look for in an embedded analytics tool  

Seamless embedding and UI integration  

The way analytics is embedded has a direct impact on the user experience. While iframe-based embedding is often the quickest option, it can introduce differences in navigation, styling, and responsiveness that make analytics feel separate from the rest of the application. SDKs, JavaScript components, and framework-specific integrations generally require more development effort but provide greater control over branding, layouts, and application behavior.

What to evaluate: Review the embedding options available for your technology stack and determine how much control they provide over authentication, navigation, responsive layouts, and visual customization. If analytics is customer-facing, the embedded experience should feel like a natural extension of the application.

Multi-tenant architecture and data governance  

For SaaS products, separating customer data is a core architectural requirement. Features such as row-level security, workspace isolation, tenant-specific permissions, and role-based access help ensure each customer only accesses information intended for them.

Deployment consideration: Multi-tenancy should be evaluated alongside the pricing model. Some platforms license by user, others by tenant, consumption, or infrastructure. Understanding how both the architecture and pricing scale helps avoid unexpected costs as customer numbers grow.

Security framework and compliance  

Embedded analytics platforms often process operational, financial, or customer information, making security an important evaluation criterion. Authentication methods, encryption, audit logging, and compliance certifications should be reviewed alongside reporting capabilities.

Questions to ask vendors:

• Which authentication methods are supported?
• Are row-level and column-level permissions available?
• Which compliance standards and certifications does the platform maintain?
• How are audit logs and administrative controls managed?

AI-assisted, self-service analytics  

AI capabilities now extend beyond natural-language search. Many platforms also provide automated insight generation, anomaly detection, forecasting, and AI-assisted report creation. The usefulness of these features depends not only on the models themselves but also on the quality and governance of the underlying data.

In practice: Ask how AI features interact with business rules, semantic models, and governed metrics. AI-generated answers are generally more reliable when they operate on curated business definitions rather than directly against raw datasets.

White-labeling and customization

Branding goes beyond replacing a logo. Evaluate whether the platform supports custom domains, themes, navigation, email templates, feature visibility, and user interface customization that aligns with your product.

This becomes particularly important for software vendors delivering analytics to customers, where reporting is expected to appear as part of the application rather than as a third-party service (know more about white-label reporting tool).

APIs, SDKs, and extensibility  

Embedded analytics projects rarely stop at standard dashboards. APIs, SDKs, webhooks, and developer documentation determine how easily the platform adapts to product-specific workflows and custom integrations.

Before deciding: Review the available SDKs for your preferred programming languages and frameworks, the completeness of the API documentation, versioning practices, and the level of developer support available during implementation.

Scalability and performance at data volume  

Testing with demonstration data rarely reflects production workloads. As deployments expand, platforms need to support larger datasets, additional users, concurrent queries, and more frequent refresh schedules without affecting responsiveness.

When evaluating performance, consider expected data volumes, concurrency requirements, refresh frequency, and deployment architecture rather than relying solely on benchmark demonstrations.

Vendor Support and implementation

Implementation often extends beyond embedding dashboards. Organizations may require assistance with authentication, branding, localization, API integration, security configuration, or production troubleshooting.

Review the vendor's implementation resources, support plans, documentation, training, and service-level agreements before committing. These factors can have a significant influence on deployment timelines and long-term maintenance.

Why organizations should use an embedded analytics tool  

Organizations adopt embedded analytics for different reasons, but the objective is usually the same: making data easier to access within the applications where work already happens. Whether the users are customers, employees, or partners, embedding reports into existing workflows reduces the need to switch between multiple tools before finding the information needed to make a decision.

For software vendors, embedded analytics also changes how reporting is delivered. Instead of building and maintaining a complete analytics stack internally, many organizations adopt an embedded analytics platform and focus their engineering effort on the parts of the product that are unique to their business. This can shorten implementation time while providing capabilities such as data connectivity, security, governance, AI-assisted analysis, and multi-tenant deployment.

The commercial impact extends beyond implementation. Organizations often use embedded analytics to introduce premium reporting features, improve customer engagement, and expand self-service access to business information. Industry studies also associate embedded analytics with higher product adoption, stronger customer retention, and improved operational efficiency, particularly when reporting is available as part of everyday workflows rather than through standalone BI tools.

One Zoho Analytics customer described the impact after embedding dashboards directly into their ERP system:

"Zoho Analytics seamlessly generated reports and dashboards that are embedded within our ERP system, offering robust functions that democratized access to insights across our organization."
— Ravi Kumar, Head of Finance and IT Operations, AQUAGROUP

While implementation approaches differ across organizations, the pattern is consistent: analytics becomes more accessible when it is integrated into the systems where people already spend their time.

Conclusion

Selecting an embedded analytics platform is ultimately about finding the right balance between functionality, implementation effort, deployment flexibility, and long-term ownership. The platforms compared here take different approaches to those requirements—some prioritize AI-assisted analytics, others focus on governed data models, embedded SDKs, integrated data platforms, or collaborative reporting. Evaluating those capabilities alongside your product architecture, customer requirements, and deployment model will provide a clearer basis for comparison than features alone.

Zoho Analytics brings together broad data connectivity, embedded analytics, AI-assisted insights, flexible deployment, and white-label capabilities within a single platform. Whether you're embedding dashboards into a SaaS application, modernizing customer-facing reporting, or extending analytics across enterprise applications, it provides the flexibility to support different implementation approaches without requiring separate analytics infrastructure.

If you're exploring embedded analytics for your product, you can start a free trial, schedule a personalized demo, or speak with the Zoho Analytics team to discuss your deployment requirements and evaluate the platform against your specific use case.You can also start your free trial here.