DataBrain vs. Amazon QuickSight:
A No BS Comparison Guide

Every search for an embedded analytics platforms today includes a result of established players (and Reddit threads discussing foundational problems) and emerging, specialized tools like Databrain.

You've read articles, you've asked friends, you've stalked Reddit threads, and through all this it is clear that no one gets fired for choosing an established player. But as a conscientious product & technology leader, you want the best for your application. Apart from just playing safe, you need something comprehensive yet clear about which platform will actually work for your needs.

This comparison cuts through the marketing fluff to help you understand the real differences between DataBrain and Amazon QuickSight for embedded analytics in SaaS products. No unnecessary jargon or feature lists that mean nothing to you — just practical insights to guide your decision.

DataBrain vs. QuickSight: TL;DR Comparison

In case you don't want to read through a few thousand words (though we've tried to make it skimmable), here's the quick takeaway:

DataBrain Pros and Cons

• Pros: Purpose-built for SaaS embedding. Simplified multi-tenancy with database-per-tenant model. User-friendly AI features. Extensive customization options. Supports both self-hosted and cloud deployment.
• Cons: Newer platform with a smaller community. Less integrated with AWS ecosystem. May require custom implementation for complex data transformations.

Amazon QuickSight Pros and Cons

• Pros: Deep AWS integration. Advanced machine learning capabilities. Handles very large datasets efficiently. Session-based pricing works well for low-volume usage.
• Cons: Complex multi-tenancy implementation requiring significant automation. Limited customization options for embedding. API-only dashboard management. Session-based pricing can become expensive at scale.

What should you choose?

The short answer is, it depends on how you think embedded analytics should work in your SaaS product.

• If you want analytics that truly feels like part of your product with minimal developer overhead, DataBrain is your best bet.
• If you're deeply invested in the AWS ecosystem and have technical resources to handle complex implementation, QuickSight might work better.
• If predictable pricing matters for a growing product with increasing analytics usage, DataBrain's flat-rate model eliminates scaling surprises.

Every tool has its strengths and weaknesses. Analytics platforms reflect different philosophies about how data should be presented and managed. Our recommendation is to evaluate both based on your specific requirements and development resources.

Feature Comparison Table

Multi-tenancy: Why Implementation Complexity Matters

Multi-tenancy is where embedded analytics platforms really start to show their true colors. Both solutions offer multi-tenant capabilities, but with dramatically different approaches that impact your development timeline and maintenance overhead.

Amazon QuickSight: Complex Multi-Tenancy Implementation

Amazon QuickSight provides two multi-tenancy models that sound straightforward on paper: Group (for view-only scenarios) and Namespace (when users need to create and share). However, the implementation reality is quite different.

For data isolation, QuickSight offers either dedicated datasets per tenant or shared datasets with row-level security. Sounds flexible, right? The catch is that this typically requires extensive automation through Lambda functions, AWS CLI, or SDKs to manage assets across tenants.

A typical QuickSight multi-tenant implementation involves:

1. Creating automation scripts to provision datasets and dashboards for each tenant
2. Configuring complex row-level security with permission rules
3. Implementing identity federation with SAML 2.0
4. Creating multiple integration points between your application and AWS services

This complexity is rarely mentioned in the marketing materials but becomes painfully apparent during implementation. It's not insurmountable if you have AWS expertise on your team, but it definitely extends your time-to-market.

Programmatic Multi-Level Tenancy for SaaS: A Technical Deep Dive

With Databrain, you can implement scalable, multi-level tenancy through a fully programmatic approach that eliminates administrative overhead while maintaining robust data isolation. This solution stands out from competitors by automating tenant isolation through code rather than requiring manual configuration in admin interfaces.

Programmatic Tenant Isolation: No Admin UI Required

Unlike platforms such as QuickSight that require administrators to manually configure tenant settings through user interfaces, Databrain implements tenant isolation entirely through code and API calls. This creates several distinct advantages for your implementation:

Fully Programmatic Row-Level Security

Databrain's tenant isolation is implemented through SQL-based row-level policies that are automatically applied during query generation. These policies are parsed and applied as Common Table Expressions (CTEs) just before sending queries to your database. This happens seamlessly in the background without requiring any configuration steps in an admin UI.

SELECT * FROM customers WHERE client_id = :current_client_id=

The system dynamically injects the appropriate tenant identifier at runtime, ensuring each tenant only sees their authorized data.

Token-Based Authentication Without Administrative Overhead

The Guest Token system is central to Databrain's programmatic approach. Your application backend requests a token specific to each user with their userId or clientId. This identifier is then automatically injected into row-level policies, tailoring data access without requiring administrators to manually configure access for each new tenant.

Multi-Level Tenancy Architecture in Action

Building on this programmatic foundation, Databrain creates a sophisticated multi-level tenancy model that can be adapted to your specific organizational hierarchy:

SaaS Example Implementation

Consider a SaaS company with a three-tier customer structure:

• Organization (Level 1)
• Team (Level 2)
• User (Level 3)

Here's how Databrain implements this hierarchy programmatically:

1. Client Tenancy Base Layer: Each embedded dashboard instance is programmatically tied to a specific Organization through the Guest Token system. The token contains the Organization identifier that automatically filters all queries.
2. Dependent Dashboard Filters: Building on the base isolation, dashboard filters create additional hierarchical segmentation:
   
   • Filter 1 – Organization (driven by client tenancy)
   • Filter 2 – Team (dependent on the selected Organization)
   • Filter 3 – User (dependent on both Organization and Team)
3. Chained Dependency: This setup ensures that when a specific Organization is selected, only the relevant Teams appear, and once a Team is selected, the User filter updates accordingly—all maintained programmatically.

Embedding with Programmatic Tenant Control

When embedding a dashboard, you can programmatically pass values for any of these filters through the API. This means:

• You control data visibility for each embedded instance through code
• You can narrow the view to an Organization, Team, or individual User
• All security is enforced automatically at the database query level

The key difference with Databrain is that this entire process happens without requiring administrators to configure anything in a UI like QuickSight. Instead, tenant isolation is established through:

1. Setting up row-level policies using SQL for your data tables
2. Generating user-specific tokens through API calls that include tenant identifiers
3. Passing filter values programmatically during dashboard embedding

Multi-Data Source Management Without Administrative Burden

Databrain's approach also extends to managing multiple data sources across tenants. With Multi-Data Source Connections, you can:

• Connect unlimited data sources to a single workspace, provided schemas are identical
• Switch between client data sets programmatically or through a simple dropdown
• Define metrics and formulas once that work across all data sources

This capability eliminates the repetitive tasks of managing separate databases for each tenant without requiring administrators to configure connections for each new tenant.

Benefits of Databrain's Programmatic Approach

This programmatic, code-driven approach delivers significant advantages:

1. Scalability: Onboarding new tenants requires no administrative UI interaction, allowing you to scale to hundreds or thousands of clients without proportional management overhead
2. Consistency: Metrics, formulas, and visualizations remain consistent across all tenants, as they're defined once and applied programmatically
3. Security: Data isolation is enforced at the database query level, ensuring robust separation between tenants
4. Developer Control: Your development team maintains complete control through code rather than relying on administrative interfaces

By chaining these programmatic filters and passing values during embedding, you create a highly secure and personalized analytics experience that scales efficiently with your business, all without writing custom logic or requiring administrative configuration for each new tenant.

If you're already using separate databases for each customer (as many modern SaaS applications do), DataBrain's approach aligns perfectly with your architecture. This means:

• No complex automation scripts or workflows
• Automatic dashboard updates when switching between tenants
• Faster implementation, especially if you already have a database-per-tenant setup

For SaaS companies prioritizing rapid implementation and reduced development overhead, DataBrain's approach provides a much more direct path to embedded analytics.

AI Capabilities: Features focused on accessibility

Both platforms offer AI and machine learning features, but with distinctly different philosophies about who should be able to use them.

QuickSight: Sophisticated ML (If you can use it)

Amazon QuickSight delivers robust machine learning capabilities designed primarily for data professionals. The platform includes anomaly detection to identify outliers, forecasting features to project future trends, and predictive insights based on historical patterns.

With Amazon Q, QuickSight now supports natural language queries, allowing users to ask questions in plain English. The catch? Many organizations report that effectively using these capabilities requires significant data literacy and technical expertise, potentially limiting adoption among business users without specialized training.

The strength of QuickSight's approach lies in its technical sophistication—valuable for organizations with dedicated data teams but potentially challenging for broader business adoption.  

DataBrain: AI that you can actually use

DataBrain takes a refreshingly different approach to AI, designing features that are actually accessible and empower users regardless of their technical background.

Natural Language Queries:

Enable anyone to create metrics and reports by typing questions in conversational language. No SQL required—users simply ask “What's our customer retention rate by plan type?” and receive immediate visualizations.

AI-Powered Summaries:

Automatically analyzes metrics and generate insights, highlighting trends and anomalies without manual analysis. This feature helps teams quickly identify patterns that might otherwise require hours of exploration.

Custom SQL Assistance:

Provides AI-suggested query optimizations, making database work accessible to users with limited SQL expertise. This bridges the gap between business requirements and technical implementation.

For companies seeking to democratize analytics across their customer base, DataBrain's approach removes significant adoption barriers. When embedded in your product, these features allow customers of all technical levels to derive meaningful insights—increasing engagement and perceived value.

Embedding Experience: Flexible SDK vs. Limited Options

The quality of the embedding experience directly impacts how seamlessly analytics integrates with your application. Both platforms offer embedding capabilities, but with markedly different approaches to customization and flexibility.

DataBrain: Comprehensive SDK for Truly Native Experiences

DataBrain provides exceptional embedding flexibility through a comprehensive SDK designed specifically for SaaS integration. The platform's approach to embedding is built around three core strengths:

Deep Customization Options:

Developers can control virtually every aspect of the embedded experience, creating a seamless integration that matches their brand perfectly. DataBrain allows for:

• Complete white labeling capabilities to maintain brand consistency
• Customizable color themes, fonts, and CSS overrides
• Pre-built templates specifically designed for SaaS analytics
• Flexible configuration options for dashboards and metric cards

As their documentation states, DataBrain lets you "take full control over your app's appearance and functionality with robust customization options."

Flexible API-Driven Embedding:

DataBrain's SDK is built on an API-first foundation that enables developers to:

• Integrate data from existing pipelines or databases
• Add visualizations and dashboards directly into applications
• Automate workflows and trigger notifications through webhooks
• Control user access and customize visualizations programmatically

This API-centric approach allows SaaS companies to create dynamic, interactive analytics experiences that respond to user actions in real-time.

Streamlined Implementation:

DataBrain significantly reduces development complexity with:

• A straightforward NPM package installation process
• Simple three-step integration for embedding dashboards
• React/Vue component support for seamless framework integration
• Ready-to-use components that require minimal configuration

Amazon QuickSight: More Complex AWS-Integrated Approach

QuickSight offers embedding capabilities primarily focused on integration with the AWS ecosystem, with different considerations:

Multi-Step Configuration:

QuickSight requires more technical setup, including:

• Setting up IAM permissions for API calls
• Managing domain allow listing (up to 100 domains)
• Creating an asynchronous embedding context
• Configuring frameOptions and contentOptions for each experience

This multi-step process creates a steeper learning curve and longer implementation timeline.

AWS-Ecosystem Integration:

QuickSight's embedding is tightly coupled with AWS services:

• Requires IAM policies for authentication
• Depends on AWS SDK for generating embedded URLs
• Relies on AWS CloudTrail for usage auditing
• Necessitates QuickSight administrator involvement for domain management

Limited Customization Framework:

While QuickSight offers theming options, they're more constrained:

• Runtime theming is supported but requires SDK version 2.5.0 or higher
• Theme changes are limited to synchronizing with parent application themes
• Customization focuses primarily on appearance rather than functionality
• Developers must explicitly secure permissions to access and modify themes

For companies seeking an analytics solution that feels like a natural extension of their product, DataBrain's comprehensive customization capabilities and simplified integration process offer significant advantages over QuickSight's more complex, AWS-centric approach.

Amazon QuickSight: Limited Customization Within AWS

QuickSight offers embedding capabilities primarily focused on integration with the AWS ecosystem. While it allows for embedding visualizations and dashboards, customization options remain more limited.

Basic Theming:

QuickSight's customization focuses primarily on UI themes rather than functional components. While you can apply some basic branding, it doesn't provide the same level of granular control over UI elements that DataBrain offers.

AWS-Dependent Integration:

Data synchronization is primarily handled through AWS ecosystem workflows rather than direct APIs for syncing specific data sources. This creates additional complexity when you need real-time updates based on user actions.

Cloud-Only Deployment:

QuickSight is available only as a fully managed cloud service, which may not align with the requirements of SaaS applications requiring on-premise or hybrid deployment options.

For companies that want an embedded analytics experience that truly feels like part of their product, DataBrain's approach to customization and API-driven updates provides significant advantages over QuickSight's more limited options.

Pricing: Predictability vs. “It Depends On Usage”

The pricing models between DataBrain and Amazon QuickSight represent fundamentally different approaches to embedded analytics costs, with significant implications for scaling SaaS products.

DataBrain: Predictable Flat Pricing

DataBrain offers a transparent pricing structure with a flat annual fee for enterprise needs. This pricing model delivers several critical advantages:

• Unlimited Users and Sessions: No additional costs as your customer base grows
• Predictable Budgeting: Fixed expenses regardless of how heavily customers use analytics
• Full White-Labeling Included: No premium tier requirements for customization
• Bundled AI Capabilities: Advanced features included without per-query charges

For SaaS companies anticipating high analytics usage or rapid growth, this predictable pricing eliminates the risk of unexpected cost overruns as usage scales.

Amazon QuickSight: Expensive at Scale

Amazon QuickSight employs a consumption-based pricing model with several tiers. For embedded analytics at enterprise scale, costs can reach $322,000 annually for approximately 2 million sessions, broken down as:

• Base Cost: $258,000 for 1.6 million sessions
• Overage Charges: $64,000 for additional 400,000 sessions at $0.16 per session
• Additional Costs: SPICE storage at $0.38/GB and extra charges for Amazon Q

This approach presents several challenges for SaaS companies:

• Unpredictable Scaling Costs: Session-based pricing creates financial uncertainty as usage grows
• Overage Risk: Potential for significant budget overruns during high-usage periods
• Multi-Tenant Expense: Approximately $160/month per tenant for organizations supporting 100+ clients

For organizations with high-volume requirements or multiple tenants, QuickSight's pricing can create unpredictable expenses that escalate significantly with scale.

Report Scheduling and Distribution: Flexibility vs. Constraints

Effective reporting isn't just about creating insights—it's about delivering them to the right people at the right time. The platforms differ significantly in their approach to scheduled reports.

QuickSight: Limited Distribution Options

QuickSight restricts report scheduling to the dashboard view only, without options to include specific metrics or visualizations. Distribution options are similarly constrained: reports can be shared with either a single email recipient or exclusively with users who already have dashboard access.

These limitations become problematic for organizations that need to distribute targeted insights to multiple stakeholders. For SaaS companies offering embedded analytics to their customers, this constraint reduces the value of reporting features, particularly for clients that need to distribute reports across departments or teams.

DataBrain: Targeted, Multi-Recipient Reporting

DataBrain offers significantly more flexibility with its reporting capabilities. Users can select specific metrics for scheduled reports on a per-client basis, ensuring recipients receive only the most relevant information. This granular control makes reports more actionable and focused.

More importantly, reports can be scheduled and shared with multiple recipients simultaneously. This capability streamlines distribution to various stakeholders, making it easier for your customers to share insights across their organizations without manual intervention.

For companies that want to automate regular reporting for their customers, DataBrain's approach provides a more comprehensive solution that enhances the overall value of embedded analytics.

Dashboard Management: Intuitive UI vs. API Hell

The day-to-day management of analytics dashboards directly impacts both developer productivity and end-user satisfaction. The approaches to dashboard management highlight fundamental differences between the platforms.

QuickSight: APIs for Everything

QuickSight relies entirely on API calls for dashboard export and import processes, requiring technical expertise for routine administrative tasks. Exporting dashboards involves multiple programming steps:

1. Initiating an asset bundle export job via API
2. Specifying dashboard ARNs and dependencies
3. Choosing export format (QUICKSIGHT_JSON or CLOUDFORMATION_JSON)
4. Polling for completion status
5. Downloading the resulting .qs zip file containing JSON definitions

Importing follows a similarly complex process, requiring uploading files to Amazon S3 or providing base64-encoded ZIP files, then using additional API calls with specific parameters. Most notably, QuickSight doesn't support moving dashboards between workspaces, limiting organizational flexibility.

This API-only approach creates a dependency on technical resources for dashboard management, potentially creating bottlenecks in the analytics workflow.

DataBrain: Intuitive Dashboard Management

DataBrain introduces a more intuitive approach with its Workspace concept. Users can export and import dashboards directly through a straightforward user interface without requiring API calls or programming knowledge.

This accessibility makes dashboard management available to business users rather than requiring developer intervention. The simple interface reduces training requirements and administrative overhead, allowing team members to focus on creating value rather than managing technical processes.

The platform also includes a Move option for seamlessly transferring dashboards between workspaces. This feature significantly enhances organizational flexibility, particularly for growing SaaS companies managing analytics across multiple products or customer segments.

Conclusion: Which Is Right For Your SaaS Product?

When selecting between DataBrain and Amazon QuickSight for your embedded analytics needs, consider your specific requirements, technical resources, and strategic priorities.

Choose DataBrain if you need:

• A highly customizable embedded analytics experience that feels native to your application
• Simplified multi-tenancy that aligns with database-per-tenant architectures
• AI features accessible to non-technical users
• Flexible deployment options including self-hosted alternatives
• Comprehensive reporting with distribution to multiple stakeholders
• Intuitive dashboard management without API complexity
• Predictable pricing regardless of usage growth
• Faster implementation and reduced development overhead

Choose Amazon QuickSight if you prefer:

• Deep integration with existing AWS infrastructure
• Advanced machine learning capabilities for technical data analysts
• Managed cloud-only deployment through AWS
• Pay-as-you-go pricing for limited initial usage

For most SaaS companies seeking to embed analytics into their products, DataBrain offers a more accessible path to implementation with less technical overhead and more predictable costs. Its user-friendly approach to AI, streamlined multi-tenancy, and comprehensive customization options make it particularly well-suited for products serving a diverse customer base.

Ready to elevate your SaaS product with powerful embedded analytics? See how DataBrain can transform your customer experience with a personalized demo tailored to your specific use case. Contact us today to get started.