Evaluation Guide

Embedded Analytics for SaaS Companies

A practical framework for SaaS product leaders evaluating analytics platforms based on real-world experience, not vendor hype.

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⏱ 8 min read

INTRODUCTION

Why this guide exists

The SaaS application market is fiercely competitive. Vendors face immense pressure to innovate rapidly, while customers increasingly need to justify their return on investment. Embedded analytics play a crucial role in helping SaaS companies retain customers, win new business, and drive new revenue streams.

This guide is designed to help product managers, technologists, and business leaders evaluate analytics platforms based on real-world requirements—focusing on the less glamorous but far more critical platform capabilities that drive lasting success.

BEFORE YOU START

Eight guiding principles for evaluation

These principles can save you time, help you cover more ground, and avoid spending time on undifferentiated product areas.

Multi-tenancy: don’t assume feature parity

Many general-purpose BI platforms offer reduced or altered functionality in multi-tenant deployments. Validate how well a platform supports multi-tenancy in practice—not just in theory.

Avoid being misled by visual appeal

Attractive interfaces rarely determine long-term success. Most SaaS companies will customize the visual layer to match their branding. Focus on the capabilities that will make or break your deployment.

Evaluate AI: prioritize flexibility over novelty

Today’s cutting-edge AI features may become obsolete within months. Evaluate platforms by their ability to flexibly integrate new AI capabilities as they emerge—not by the novelty of current features.

Don’t overinvest in commonplace features

Capabilities like interactive dashboards and standard visualizations are ubiquitous. Spend your evaluation time on the edge capabilities that separate a functioning platform from one that fuels growth.

Use analytics to differentiate your product

The analytics platform you choose directly shapes your product’s differentiation. Prioritize platforms with distinctive features that resonate with your users and set your product apart.

Plan for evolving requirements

SaaS companies face unpredictable future needs driven by shifting markets, competition, and regulation. Prioritize flexibility—the right platform must adapt to tomorrow’s unknowns.

Value-added services also matter

The right partner can help shape your monetization strategy—identifying how analytics delivers differentiated value and uncovering new revenue opportunities. Evaluate the company, not just the product.

Prioritize proven SaaS experience

A vendor’s depth of experience in serving SaaS companies, understanding multi-tenant architectures, and history of successful deployments are strong indicators of future success.

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THE FRAMEWORK

Four critical areas for platform evaluation

These are the pillars of a resilient analytics strategy. Focus your evaluation here.

Self-service experience

Self-service capabilities—such as building dashboards, customizing visualizations, and modifying datasets—are essential for empowering customers and reducing support overhead. However, multi-tenancy introduces unique challenges. Features that work well in a single-tenant setup may not scale or function the same way across multiple tenants.

When evaluating self-service functionality, confirm that capabilities are not only present but also secure, scalable, and easy to manage in a multi-tenant deployment.

Dashboard builder capabilities
The ability for tenant users to independently build dashboards and share them with colleagues—without requiring support from your product team—is essential. Without this, customers become frustrated and your team gets overwhelmed by custom dashboard requests.

Dataset customization
The ability for tenant users to customize datasets—by renaming fields, adding new ones, or creating entirely new datasets—enables customers to tailor your application to their specific business needs.

  • Can tenant users create dashboards and deploy them to their coworkers autonomously?
  • Can tenant users create their own datasets, with custom calculations, metrics and fields—named as they wish?
  • Are these self-service capabilities available with a commingled data model?
  • Self-service requiring a separate engine per tenant — Some platforms only support self-service dashboards with one engine per tenant; look for support for commingled multi-tenant data.
  • Self-service database limitations — Some platforms only support self-service dashboards with one database per tenant; look for support for commingled multi-tenant data.
  • Limited self-service in multi-tenant environments — Some platforms do not support dashboard creation in multi-tenant deployments; look for true multi-tenant self-service.

    • Data management capabilities

    Data management is a foundational pillar—especially for SaaS companies operating in multi-tenant environments. It includes how the platform handles data loading, transformation, storage, and retrieval. These capabilities directly impact performance, scalability, and operational efficiency.

    Some platforms lack a dedicated data engine, requiring you to build and maintain your own multi-tenant analytics database. Others rely on proprietary engines that restrict access to vendor-specific tools.

    Built-in analytic database
    A built-in data engine boosts performance and controls costs. For SaaS companies, it’s essential that this engine supports both co-mingled and segregated data models and scales across tenants.

    Data transformation capabilities
    Enables raw source data to be reshaped for analytics. When paired with an analytic database, it allows high-performance analysis from virtually any data source.

    Semantic layer
    Maps technical table and column names to business-friendly terms and defines metrics, hierarchies, and calculations. Ensures a consistent, intuitive experience for end users. But a semantic layer alone isn’t enough—without a data engine, most SaaS companies can’t achieve the performance they need.

  • Does your platform include a data engine?
  • Does your platform require a third-party high-performance analytical database like Snowflake?
  • Can your platform deploy with an operational data store as a source system and still offer secure, high-performance analytics?
  • Does your data engine support both co-mingled and segregated multi-tenant data?
  • Can data stored in your engine be accessed via third-party applications using a standard interface such as SQL?
  • Can we retain the data engine and data if we switch to a different platform?
  • Is the data engine standards-based or proprietary?
  • Does your platform include data transformation capabilities?
  • Semantic-layer-only approach — Some platforms rely on external databases for transformations; look for a built-in analytics data engine.
  • Data engine limitations — Some platforms include a data engine but cannot be deployed in multi-tenant environments, limiting scalability for SaaS.
  • Proprietary data engines — Some platforms require a separate instance per tenant for self-service, increasing cost and complexity.

    • Deployment architecture and flexibility

    Deployment capabilities are critical yet often overlooked. This encompasses multi-cloud deployment support, content migration between environments, and the architecture of the deployed software.

    Some platforms operate only as SaaS and restrict you to a single cloud provider. Others deploy using outdated technologies like manually managed VMs rather than containerized models. SaaS companies also need smooth content migration between dev, staging, and production environments.

    Multi-cloud deployment support
    The ability to deploy across AWS, Azure, or Google Cloud gives SaaS companies flexibility to align analytics with their infrastructure strategy.

    Modern deployment technologies
    Serverless and Kubernetes simplify scaling and operations. Older approaches using VMs or manually managed servers increase complexity and overhead.

    Content migration
    Robust support for migrating content between development, staging, and production environments is essential for integrating analytics into existing deployment workflows.

  • Can the platform be deployed inside my cloud alongside my SaaS application?
  • Do you support multiple clouds (AWS, Azure, Google, other)?
  • Does the platform have built-in tools to migrate content between environments like development, testing and production?
  • Does the platform deploy using modern technologies like serverless or Kubernetes containers?
  • Does the platform deployment require manual management of VMs or servers?
  • SaaS-only platforms limit vendor controlled version management — Self-hosted deployment models preserve version control and provide flexibility to test and push updates on your terms.
  • Misaligned software development lifecycle (SDLC) — Hosted analytics tools introduce external release dependencies that bypass your QA, staging, and rollback processes; look for platforms that align with your SDLC.
  • Reduced security and operational control — SaaS-hosted solutions limit visibility into security practices, compliance controls, and uptime management; look for platforms that maintain full operational control.
  • Single-cloud deployment limitations — Some platforms only support one cloud provider, limiting multi-cloud flexibility; look for solutions that align with your SaaS infrastructure strategy.

    • Embedding capabilities

    Embedding involves the front-end technologies for integrating components like charts, dashboard explorers, or dashboard builders into your application, as well as backend platform APIs that facilitate integration with your SaaS application and operations.

    Modern front-end embedding technologies are critical for a seamless, white-labeled experience. Outdated methods like iframes often result in a fragmented interface and can introduce security vulnerabilities. Backend APIs enable the automation of integration, provisioning, and content migration.

    Front-end embedding technologies
    Modern platforms support embedding through web components rather than iframes, allowing charts, tables, dashboards, and even dashboard builders to be embedded directly into your application.

    Back-end API capabilities
    Comprehensive APIs should expose all major components—users, groups, datasets, semantic layers, dashboards—enabling automation of provisioning, deployment, and integration with external systems.

  • Does the platform offer front-end embedding and customization without iframes using modern web components?
  • Is every product capability exposed to tenant users embedded, or do some require operating the analytics platform directly?
  • Does the platform include an embedded dashboard builder?
  • Can every dashboard, widget, and visualization be customized in terms of appearance and content by the tenant user?
    • Iframe-only embedding — Iframes limit UX control and integration and introduce security concerns; look for JavaScript components that allow styling flexibility and seamless embeds.
    • Embedded dashboard limitations — Some platforms cannot embed dashboard creation tools; look for solutions that support fully embedded self-service analytics.
    • Limited APIs — Some platforms provide incomplete or inconsistent APIs, making deeper product integrations difficult; look for robust developer APIs.
    • Immature backend APIs — Some platforms lack mature backend APIs for automation and integration; look for platforms with comprehensive backend API support.

    QUICK REFERENCE

    Core evaluation areas at a glance

    Capability Area What to look for Why it matters
    Self-Service Tenant-level dashboard building and dataset customization Empowers users, reduces support load
    Data Management Built-in data engine, transformation, semantic layer Ensures performance, scalability, and cost control
    Deployment Multi-cloud support, modern deployment tech (e.g., Kubernetes), content migration Aligns with your infrastructure, simplifies operations
    Embedding Web component-based embedding, full-featured backend APIs Delivers seamless UX and operational automation

    WATCH OUT

    Pitfalls and strategic considerations

    Keep these in mind as you narrow your evaluation.

    Common pitfalls to avoid
    • Assuming multi-tenancy works the same across platforms
    • Overvaluing visual polish over backend capability
    • Relying on semantic layers without a data engine
    • Choosing SaaS analytics platforms that conflict with your data privacy model
    Strategic considerations
    • Flexibility: Can it evolve with your product and market?
    • Differentiation: Does it give your product a competitive edge?
    • Scalability: Will it support growth without re-architecture?
    • Control: Do you retain ownership of data and deployment?

     

    __ 

    “If your platform can’t manage your data,
    it can’t manage your analytics.”

     

    Evaluation Questions

    12 questions to ask before choosing a platform

    Use these questions during vendor evaluations to cut through marketing and get to what matters.

    Self-service dashboard building

    Can tenant users build and share dashboards independently?

    Dataset customization

    Can users customize datasets with field renaming, calculated fields, and new dataset creation?

    Secure multi-tenancy

    Does the platform support secure, scalable multi-tenancy with co-mingled and segregated data models?

    Built-in data engine

    Is there a built-in data engine and transformation layer—not just a semantic layer?

    Future flexibility

    Can the platform evolve with your product and market as requirements change?

    Modern deployment

    Does it support modern deployment models like Kubernetes and serverless?

    Multi-cloud support

    Can you deploy in your cloud(s) of choice?

    Environment management

    Are content migration and environment management built in?

    Seamless embedding

    Does it offer web components (not just iframes) with APIs that expose all key functionality?

    Product differentiation

    Will it differentiate your product in the eyes of your customers?

    Advisory services

    Does the vendor offer advisory services to help shape your analytics or monetization strategy?

    SaaS track record

    What experience does the vendor have working with SaaS companies in production environments?

           Download our 12 Questions to Ask When Evaluating Embedded Analytics Solutions PDF   

    See what our partners have to say

    Herman Haynes
    CIO @ Global K9 Protection Group

    Adding Qrvey to our business was like turning on a light switch.

    Ryan Quackenbush
    Senior PM @ JobNimbus

    We can’t speak highly enough of the stellar team at Qrvey. Within months of deploying Qrvey, JobNimbus achieved 70% adoption among large
    enterprise users.

    Dadou Jahanbani
    CTO @ Impexium

    Qrvey allowed Impexium to go to market quickly and get analytics into the hands of our customers.

    Srinivasa Sridharan
    CTO @ Setvi

    Excellent product and customer support.
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    Ready to evaluate with confidence?

    See how Qrvey’s embedded analytics platform is purpose-built for multi-tenant SaaS companies.

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