Looker vs Sisense for Embedded Analytics: A 2025 Comparison

Key takeaways:

• Looker and Sisense offer powerful analytics, but with trade-offs. Both platforms are strong, but they can be complex to implement, developer-heavy, and less flexible for fully embedded, customizable use cases.
• Choose Looker if you’re already invested in Google Cloud and need centralized data modeling, strong governance, and real-time analytics across teams.
• Choose Sisense if you want flexible embedding with customization options, and the ability to connect to a wide range of data sources.
• Qrvey is an alternative for embedded analytics purpose-built for SaaS—offering scalable multi-tenant support, no-code automation, deep customization and seamless white-labeling with less development effort.

Looker or Sisense? This is a common question for SaaS teams exploring analytics solutions because they both have an embedded offering.

Both are established names in the analytics space, each with its own approach to data integration, visualization, and scalability. 

But which one will actually work with your SaaS product? And are they the best options out there?

In this guide, we’ll break down the key differences between Looker and Sisense, highlighting where each shines—and falls short. Plus, we’ll share a smart alternative you may want to consider.

	Looker	Sisense
Best For	Enterprises using Google Cloud; teams with strong data engineering support	Mid-to-large companies needing flexible, embedded analytics across data sources
Stand Out Feature	Centralized data modeling with LookML	Developer-friendly embedding with robust SDKs and APIs
Price	Custom	Custom
Pros	✓ Deep GCP integrations✓ Strong data governance✓ Real-time data access	✓ Wide range of data connectors✓ Flexible embedding options✓ More customization features
Cons	✗ Steep learning curve with LookML✗ Limited UI flexibility✗ Higher cost for embedding	✗ Steep learning curve for non-technical users✗ Requires developer resources✗ Setup and maintenance can be complex
Customer Support	Structured through Google Cloud tiers; quality varies by plan	Generally responsive with onboarding support, live chat on higher tiers for post-implementation support
Embedding Analytics	Secure iframe-based embedding with signed URLs; basic customization	Customization with SDKs, widget embedding, and interactive dashboards
Customization & Visualization	Theming via LookML and URL parameters; limited layout control	Widget-level customization, drag-and-drop dashboards, rich plugin options
ML & AI Features	Integrates with Google Cloud AI tools, but requires additional setup	Some built-in predictive analytics and AI features are available out of the box

For Whom is Looker Useful For?

Looker is a data analytics platform from Google Cloud that offers embedded business intelligence. This allows teams to integrate dashboards and actionable insights directly into their applications. It’s best for:

• Companies with strong technical teams needing governed, consistent reporting across departments.
• Teams already using Google Cloud and looking to centralize their analytics within the same ecosystem.

For Whom is Sisense Useful For?

Sisense is an analytics platform that enables teams to build, customize, and embed analytics into internal tools or customer-facing applications. It’s best for:

• Organizations seeking interactive data experiences and complex data mashups without heavy modeling. 
• Businesses with technical resources, especially those with data engineers or developers who can handle the setup, modeling, and ongoing management of Sisense’s architecture.

Qrvey: An Alternative

Don’t identify with any of these? No worries—there are alternatives. 

While Looker and Sisense are strong options, they often come with long implementation cycles, steep learning curves, and ongoing engineering overhead. 

Qrvey, on the other hand, provides an embedded analytics platform purpose-built for SaaS companies—offering a lightweight, no-code solution that’s easier to deploy, scale, and customize. It gives teams everything they need to deliver powerful, white-labeled analytics without the struggle of heavy maintenance and long implementation times..

Unlike Sisense and Looker, Qrvey was designed at its inception for cloud-native embedded analytics. It was built for the way businesses operate in the cloud. That means no clunky workarounds, no limitations on user access, and no surprise costs as you scale. 

All in all, Qrvey delivers the flexibility SaaS teams need—without the heavy setup or reliance on third-party infrastructure.

Price

Pricing is a key consideration when choosing an analytics platform—especially if you’re scaling or embedding for external users. Let’s see where Looker and Sisense stand.

Looker 

Looker doesn’t use fixed per-user pricing. Instead, it offers custom quotes based on factors like company size, deployment setup, and usage. 

For larger or enterprise-level deployments, this can make it more expensive than many traditional tools.

Sisense

Sisense doesn’t offer upfront pricing, as it also depends on multiple factors such as company size, deployment, and users and viewers.

Verdict 

Both solutions offer highly customized pricing based on the company’s needs. However, this may come with hidden costs related to setup, add-ons, scaling, or user growth—making it harder to predict long-term expenses.

Ease of Use

When choosing the right embedded analytics solution, ease of use plays a big role—especially if you want faster implementation, smoother adoption, and less reliance on engineering. 

While both Looker and Sisense are powerful, they differ in how approachable they are for both developers and business users.

Looker

Looker offers a modern interface and strong modeling capabilities, but requires strong technical expertise to unlock its full potential.

• LookML required: To define metrics, relationships, and data structure, teams must learn and maintain LookML, Looker’s proprietary modeling language—which can get a bit complex.
• Developer-first setup: Embedding, customizing, and managing access often involves backend configuration and SDKs.
• Limited no-code options: Business users may find the interface clean, but still need help from data teams to create or adjust reports.

Sisense

Sisense promotes itself as user-friendly, but in reality, it leans heavily on technical expertise—especially when handling complex use cases that often require developer support.

• Initial setup can be complex: While it offers drag-and-drop tools, setting up data models and custom dashboards requires technical know-how.
• Custom coding for flexibility: JavaScript and REST APIs are necessary for deeper customization, making it better suited to technical teams.
• Steeper learning curve: Compared to simpler tools, new users may take time to get comfortable navigating the platform.

Verdict

Between the two, Looker is slightly easier for developers familiar with Google Cloud, while Sisense gives more flexibility through coding but both still have a steep learning curve. 

Neither is ideal for non-technical users—but if you have strong technical resources, both might work depending on your needs. 

Customer Support

When investing in robust solutions, solid customer support can make a huge difference—especially during implementation, scaling, or when issues arise. 

What to look for exactly?  Mainly—documentation, responsiveness, and access to dedicated support.

Looker

As part of Google Cloud, Looker offers structured support plans, but users have mixed experiences depending on the level of service purchased.

• Enterprise support plans: Looker offers tiered support through Google Cloud, with options for support and technical account managers—at an added cost.
• Documentation-heavy approach: The platform leans on extensive documentation and community forums for self-service troubleshooting.
• Response time varies: Some users report slow resolution times unless they’re on a premium support plan.

Sisense 

Sisense is not typically recognized for its responsive and dedicated customer support:​

• Highly responsive support in the beginning: Users have highlighted Sisense’s customer support as highly responsive and professional, often resolving issues promptly during the onboarding phase. ​Once implementation is complete, you are often ‘on your own.’ Sisense offers traditional break/fix technical support, but there is no proactive support.  
• Limited documentation: Users may find it difficult to troubleshoot issues or fully use the platform’s features due to limited or outdated documentation, leading to slower resolution and increased reliance on the Sisense community for technical troubleshooting, discussions and news.

Verdict

Both Looker and Sisense offer customer support, but the level of support required for their embedded analytics offering falls short of customer expectations. 

Therefore, Sisense may have a slight edge in support mainly due to their active Sisense community of users.

Integrations

When choosing an embedded analytics platform, integrations are key—they can either be a game changer or a dealbreaker. 

Connecting with your data sources, cloud environment, and business tools saves time, reduces engineering overhead, and improves your team’s workflow. 

Both Looker and Sisense support integrations—but they differ in depth and flexibility.

Looker

Looker integrates deeply with Google Cloud and other popular tools, but it works best if you’re already in the Google ecosystem.

• Strong GCP (Google Cloud Platform) integrations: Native support for BigQuery, Cloud Storage, Google Ads, Google Analytics, and Sheets makes it easy to analyze and act on data within GCP.
• Looker marketplace: Offers prebuilt blocks, plug-ins, and apps for common use cases like retail analytics, marketing dashboards, and finance.
• Limited native third-party integrations: Looker relies on APIs or third-party tools for deeper integrations outside Google Cloud.

Sisense 

Sisense is built for flexibility, supporting a wide range of connectors, APIs, and cloud platforms.

• Dozens of native connectors: Seamless integrations with BigQuery, Snowflake, Redshift, SQL Server, Salesforce, and more.
• Flexible data models: Once connected, Sisense lets you choose how to work with your data—whether live, cached, or a hybrid approach—so you can optimize for speed or freshness based on the use case.
• Built-in data preparation: Blend, transform, and prepare data from multiple sources inside the Sisense platform—no third-party ETL required.

Verdict 

By far, Sisense wins on integration flexibility—especially if you need to work across multiple clouds or data sources. 

That said, Looker is ideal if you’re already into the Google Cloud ecosystem—but may require more setup for external platforms. 

Embedding Capabilities

We’ve finally reached the core reason you’re likely here—embedding analytics.

Are you creating dashboards for internal users? Or maybe customizing customer-facing analytic features?  Either way, you want a solution that gives you full control over the user experience, branding, and security. 

Here’s how Looker and Sisense compare.

Looker 

Looker offers solid embedding features, especially when paired with Google Cloud, but it can be limiting for deeper customization.

• Signed embedding: Allows secure, personalized access using your own authentication system—ideal for customer-facing apps.
• White-labeling: Custom themes and UI tweaks help match Looker dashboards to your product’s look and feel. However, customization is limited. 
• Iframe-based integration: Embedding is handled through <iframe> elements, which can feel restrictive for highly interactive use cases, and often expose vulnerabilities.

See our article about “Why Iframes are Considered a Security Risk and How to Secure Them,” for an in-depth look at the cautions of using iframes for embedded analytics.

Sisense 

Sisense is built with flexible embedding in mind, offering powerful developer tools to tailor analytics inside any application.

• JavaScript SDK & Embed API: Go beyond iframes with full control over layout, interactions, and user experience.
• Custom UI and interactivity: Developers can embed individual widgets and build custom dashboards.
• Developer-heavy setup: While powerful, embedding with Sisense often requires front-end development expertise and time to properly set up.

Verdict 

Looker is great for simple, secure embedding, but it’s limited in interactivity. Sisense, on the other hand, offers deeper customization and control, though it requires more developer effort. 

How to Choose Between Looker vs Sisense

Both Looker and Sisense are powerful platforms, but the right choice depends on what matters most to your team.

Below, we compare how each tool handles key features.

Embedding Analytics

Looker handles embedding through secure iframes and signed URLs. However, customization is limited without developer work. 

Sisense offers more control, but requires a more technical setup.

Qrvey, on the other hand, was built specifically for embedded analytics—with full white-labeling, multi-tenant support, and no-code automation that makes embedding feel native and approachable.

Customization and data visualization

Sisense and Looker both offer dashboard building and theming. That said, visual customization is limited. 

Looker relies on LookML and theme parameters. And while Sisense allows more visual tweaking, it requires significant dev overhead.

Qrvey provides personalized embedded analytics with drag-and-drop dashboards, flexible UI components, and full white-label support for enhanced branded data visualization. 

Machine Learning and AI features 

Looker integrates with Google’s AI tools, but using them requires additional setup and cloud services. 

Sisense has some built-in AI capabilities, but these can be hard to surface for non-technical users and require lots of prior preparation. 

Qrvey’s built-in generative AI allows SaaS users to ask questions, generate visualizations automatically, and surface insights—with an easy user-friendly setup and maintenance.

Alternative To Looker and Sisense: Qrvey

Looking for an embedded analytics solution that doesn’t come with unexpected headaches?

Qrvey was built specifically for SaaS companies, delivering scalable, embedded analytics without the complexity or high costs of general-purpose BItools.

So, what sets Qrvey apart? Let’s break it down. 

Key features

Powerful embedded analytics experience:

This is where Qrvey stands out. It integrates fully interactive dashboards, self-service analytics, and AI-driven insights into your app or platform.

• Embedded dashboards
• End-user personalization and complete white labeling
• Embedded automation builder
• API-driven embedding for seamless integration
• Customizable UI to match your brand’s identity

No-code automation for data workflows:

Forget about heavily relying on technical teams. Qrvey boosts agility and scalability with no-code automation tools. 

• Workflow triggers to automate data collection and transformation
• No-code, pixel-perfect report builder for creating custom reports with precise design control
• Integrated data preparation tools for real-time insights
• AI-powered enrichment to surface trends automatically

Infrastructure and security:

Security is critical for embedded analytics. Qrvey is built for the cloud and offers enterprise-grade protection with strong access controls, compliance support, and scalable infrastructure.

• Native multi-tenant setup
• Serverless scalability
• One version with all features
• Native ETL service
• Unlimited environments

Pricing

Qrvey offers flexible, custom pricing tailored to your business needs. Instead of traditional per-user pricing, Qrvey’s model includes all features—scalable embedded analytics tools, multi-tenancy,automation, and deployment.

Where Qrvey Shines

• Designed solely for embedded analytics. This means every feature, workflow, and integration is optimized to live inside your SaaS product—without unnecessary extras or workarounds.
• Multi-tenant analytics solution. This allows you to securely isolate data, dashboards, and user permissions across different customers or user groups—all from a single deployment. 
• Less development effort. Qrvey’s no-code and low-code tools let your team build, customize, and deploy analytics without heavy engineering work. Everything is designed to be fast and flexible.
• Built for the cloud: Qrvey’s cloud-first approach was designed for SaaS businesses—providing elastic scalability, high performance, and complete control.  Since it’s fully deployed within your existing cloud infrastructure, companies with strict data compliance requirements will have peace of mind knowing customer data never flows in and out of the cloud with Qrvey.

Where Qrvey Falls Short

• Purpose-built for embedded use: Qrvey is ideal for companies that want to embed analytics into their own software products or platforms for external users. If you’re just looking for standalone dashboards or internal reporting, other BI tools may be a better fit.

Customer Reviews 

“Qrvey is one of the only tools out there that gives us the ability to embed a full suite of analytics into web apps. Qrvey’s embed capability goes beyond charts and dashboards, letting us integrate a full analytics authoring interface into apps”  – Dara K., verified G2 user review.

Who Qrvey is Best For

• SaaS companies requiring multi-tenant, embedded analytics.
• Businesses that want a customizable embedded analytics solution.
• High-growth software companies looking for an embedded analytics solution that cost-effectively scales with the business.
• Product teams looking for API-driven, automated analytics capabilities without the complexity.

Which One Will You Choose?

Both Looker and Sisense bring powerful features to the table. That said, they may not be the best choice when it comes to embedded analytics. 

If you’re looking for a solution that highlights flexibility, speed, and scalability, Qrvey is worth a closer look. 

It’s designed for teams who want analytics to feel like a natural part of their product, not an add-on.

Want to take a look? Book a demo and discover what embedded analytics should really look like.