Comparing AWS QuickSight Versus Power BI

Implementing a Business Intelligence (BI) tool empowers an organization to make data-driven decisions, streamline operations, and uncover valuable insights, ultimately driving significant growth and competitive advantage. It enables organizations to enhance data analysis, make informed decisions, and maximize operational efficiency. We’ve created a detailed comparison between AWS QuickSight and Microsoft Power BI, highlighting their strengths and differences to help you decide which tool best fits your needs.

Data Modeling

QuickSight is efficient for handling large datasets using its SPICE engine, offering quick and streamlined data analysis. While it may not support advanced data modeling, its simplicity ensures that users can generate insights rapidly without complex setup. In contrast, Power BI excels in robust data modeling and managing complex relationships between tables, which can be beneficial for detailed analysis. However, this complexity often requires more time and expertise, making QuickSight a more user-friendly and efficient option for many organizations.


When it comes to scalability, AWS QuickSight and Power BI offer different strengths suited to varying organizational needs. QuickSight automatically scales based on user demand, providing optimal performance and availability without requiring manual intervention. This makes it an excellent choice for organizations looking for a hassle-free, scalable solution that adjusts seamlessly as usage grows. On the other hand, Power BI allows users to scale capacity up and down as needed, but this often involves specific manual adjustments to optimize performance. While this approach offers flexibility, it requires more hands-on management, which can be a drawback for organizations seeking a more automated solution. QuickSight’s ability to scale effortlessly based on real-time demand positions it as a highly efficient tool for dynamic and growing businesses, whereas Power BI’s customizable scalability caters to those needing fine-tuned control over their analytics environment.


In terms of performance, AWS QuickSight and Power BI each have distinct advantages that cater to different organizational needs. AWS QuickSight is optimized for speed, leveraging its SPICE (Super-fast, Parallel, In-memory Calculation Engine) to handle large datasets efficiently, enabling real-time analytics and interactive dashboards. This makes QuickSight particularly well-suited for environments where rapid data processing and quick insights are critical. Conversely, Power BI utilizes DAX (Data Analysis Expressions) for computations, which prioritizes performance but can vary significantly depending on the complexity of the data and calculations. While Power BI’s performance can be robust, it often requires careful optimization to achieve the best results. QuickSight’s consistent high-speed performance with large datasets provides a reliable solution for organizations needing immediate and actionable data insights, whereas Power BI’s variable performance offers flexibility for more complex data analysis scenarios.


When comparing AWS QuickSight and Power BI pricing, QuickSight uses a pay-per-user model, with Author licenses at $24 per month and Reader licenses at $3 per month, making it cost-effective for scaling. For advanced features, the Author Pro license is available at $50 per month, including advanced data preparation, machine learning insights, and enhanced security. Power BI offers a freemium model, with free basic features, Pro licenses at $10 per user per month, and Premium licenses at $20 per user per month, which provide higher user capacity and advanced features for larger organizations. While Power BI’s freemium model allows for initial cost savings, QuickSight’s flexible pricing structure can be more economical over the long term, especially for organizations with a large number of users.

Example Pricing Scenario – 10 Year Licensing Cost:


AWS QuickSight Cost (10 Years)

Power BI Cost (10 Years)

200 Readers, 10 Authors



Deployment and Access Permissions

QuickSight automates deployment and offers granular control, making it easier to manage across accounts and regions. Power BI requires more manual deployment efforts.


AWS QuickSight

Power BI


Automated deployment via APIs, granular control across accounts & regions

Primarily manual deployment, permissions managed within workspaces

Access Permissions

Export & import bundle files to enable deployments across accounts & regions

Permissions managed within workspaces controlling user access to reports & apps. Readers need to be premium to be able to view reports.

Ease of Use

QuickSight is fully web-based and user-friendly across devices, whereas Power BI requires a desktop app for authoring and manual effort for mobile optimization.


AWS QuickSight

Power BI


Fully web-based and accessible from both PC and Mac, focusing on simplicity

Requires a desktop app (PC-only) for authoring, no support for Mac


Automatically adapts visualizations to device screens

Offers automatic mobile layout creation, but requires manual selection by the author

Customization & Visuals

When it comes to customization and visuals, AWS QuickSight and Power BI cater to different user needs. QuickSight focuses on simplicity and ease of use, providing a range of basic visualization tools such as bar charts, line charts, scatter plots, and pie charts. However, it has limited customization options, and users cannot directly embed custom formulas within visuals; calculations must be predefined during data preparation.

In contrast, Power BI excels in offering extensive customization capabilities. It allows users to fine-tune visual elements to meet specific branding or design requirements, providing a variety of advanced visualization options and custom visualizations from the Microsoft AppSource. This flexibility makes Power BI ideal for organizations that need highly tailored visualizations and interactive reports.

Overall, while QuickSight offers a straightforward approach to data visualization, Power BI provides a more versatile and customizable solution for detailed and complex visual needs

Version Control & Development Environment

The comparison of version control and development environments between AWS QuickSight and Power BI reveals distinct approaches suited to different organizational needs. QuickSight lacks built-in functionalities for version control and development environments. Users often resort to workarounds, such as utilizing new APIs via scripts to manually separate development, testing, and production environments. This approach, while functional, can be cumbersome for organizations requiring a more structured and streamlined development process.

Conversely, Power BI offers more robust version control and development capabilities. Most authoring in Power BI happens offline on the desktop app and must be published back to the cloud service, facilitating a clear separation between development stages. Additionally, Power BI Premium features support more advanced version control setups, including environments for development, quality assurance, and production. This structured approach allows for better management of report versions and ensures a smoother transition from development to deployment.

Therefore, while AWS QuickSight provides basic version control through manual processes and APIs, Power BI’s more comprehensive and integrated approach to version control and development environments makes it a stronger choice for organizations needing a robust and systematic development framework​


Both AWS QuickSight and Power BI have their strengths. QuickSight stands out for its seamless scalability, high-speed performance, and cost-effective pricing. It’s an excellent choice for organizations looking to leverage data efficiently with minimal complexity. For those who prioritize advanced data modeling and customization, Power BI is the better fit. However, for simplicity, automated deployment, and real-time analytics, AWS QuickSight is the superior BI tool.

Summary Table of Main Takeaways:


AWS QuickSight

Power BI

Data Modeling

Efficient with large datasets (SPICE engine), lacks advanced modeling

Robust data modeling, handles complex relationships


Automatically scales based on user demand

Manual adjustments often required


Optimized with SPICE for real-time analytics

Varies with data complexity and DAX


Cost-effective pay-per-user/session model

Freemium model with paid tiers


Automated with granular control via APIs

Primarily manual deployment

Ease of Use (Authoring)

Fully web-based, accessible from PC and Mac

Requires PC desktop app, no Mac support

Ease of Use (Reading)

Automatically adapts visualizations to devices

Requires manual effort for mobile optimization

Customization & Visuals

Focuses on simplicity, limited customization

Extensive customization options

Version Control

Lacks built-in control, uses APIs for environments

Requires offline authoring and setup for version control

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