10 Most Recommended Alternatives To Power BI (2024)

Khai To -

Power BI - the trusty toolbox everyone’s heard of, but sometimes it feels like you need a manual for the manual. It’s robust, it’s feature-rich, and it’s great… until you run into a pricing model that requires a PhD in DAX to decipher and a setup process that makes you question your career choices.

For those reasons, many businesses are on the lookout for Power BI alternatives—tools that provide better flexibility, better ease of use and less dependence on Microsoft ecosystem. In this guide, we’ll break down the best Power BI alternatives and competitors, oft-recommended by BI practitioners within our circles.

Power BI At A Glance: Pros and Cons

Power BI Pros

  • Ease of Creating Visualizations and Strong Integrations: Power BI really stands out for how easily it lets you create interactive, visually engaging dashboards. With its smooth integration into Microsoft apps like Excel and SharePoint, and even Google Sheets, it’s easy to pull in data and build reports quickly. Plus, the support docs are clear and straightforward, making it easy to get started.
  • Affordable for Small Businesses: Compared to high-priced competitors like Tableau, Power BI offers a cost-effective solution, with the Pro version starting at $9.99 per user per month. This makes it especially attractive for small and medium-sized businesses. The interface is also simple, particularly for users already familiar with Microsoft Office, helping teams get started with minimal training.
  • Great for Personal Data Analysis: Power BI is also great for people who need to handle personal data analysis. On Capterra, Power BI users are happy using it to track things like finance or expenditures. The Desktop version gives you tons of flexibility, with multiple data connectors and strong data modeling tools, so you can format your data just the way you need.
  • Powerful Data Modeling and Analytics Features: With robust data modeling features, you can easily clean, transform, and work with your data in different ways. This makes it great for real-time reporting and live data connections and of course, ideal for companies that need up-to-the-minute insights. Plus, using DAX for complex calculations gives you a lot of control over how you handle and present your data.

Limitations of Power BI

  • Microsoft-Centric and Platform Limitations: Power BI’s tight integration with Microsoft’s ecosystem is a double-edged sword. It works great for organizations already using Windows, but it’s not compatible with MacOS or Linux, which limits its flexibility across diverse teams.
  • No Git Version Control: PowerBI’s workflow is not designed to have code checked into Git repository, making it difficult to manage changes, perform code branching, and maintain the accuracy of analytics logic.
  • Steep Learning Curve with DAX: While DAX is a powerful feature in Power BI, it’s also notorious for being difficult to learn. Across review platforms like Capterra, users frequently mention that mastering DAX requires a significant time investment, making it harder for non-technical users to perform advanced calculations and analysis. Compared to the simplicity of self-service bi tools like Looker or Tableau, DAX’s complexity can slow down productivity for teams that aren’t well-versed in data analysis.
  • Limited Visual Customization and Code Functionality: Customizing visuals in Power BI can be restricted. Each visual comes with predefined functionalities, limiting flexibility for more advanced customization. For instance, users on Reddit have expressed frustration over the inability to conditionally color format each series in a stacked bar chart based on DAX measures, which is a feature users often look for but can’t currently achieve in Power BI. 

Both Capterra and Reddit users have expressed frustration over the inability to conditionally format visuals based on DAX measures and difficulties with controlling basic elements like column width. /

  • Limited Flexibility for Data Discovery: Though Power BI excels at corporate reporting, users noted that it’s not ideal for teams looking for data exploration or ad hoc analysis as it focuses more on structured reports, while tools like Tableau offer more flexibility for slicing, dicing, and playing with data in real-time.

Holistics.io

Holistics presents a strong case as an alternative to Power BI, especially for teams that prioritize SQL-based data modeling and cloud access. It eliminates the need for desktop installations, unlike Power BI, which is largely desktop-centric and requires Windows to run​.

One of Holistics’ standout features is its code-based semantic modeling layer, which allows data analysts to define metrics and logic as code, and put them through a Git-based review process. This keeps the analytics process easy to maintain, accurate, and governable.

Holistics key features include:

  • Analytics as code: Every component of the analytics logic, from data models to dashboards, is stored and versioned in Git, allowing for full programmatic control and customization. This is complemented by user-friendly interfaces for less technical users.
  • Fully customizable visualization and dashboard: Holistics offers a canvas-based dashboard experience that provides users with complete control over the layout, styling, and interactions of their visualizations and dashboards.
  • Reusable BI Components: All BI components, from visualizations and dashboards to data models and metrics, can be parameterized and reused across the organization, fostering consistency and efficiency.
  • Self-Service Data Exploration: Analysts prepare and curate datasets. Using curated datasets, business users can self-serve data and build their own reports without writing code.

Tableau

Tableau is well-known as a top choice for data visualization, offering powerful tools to create interactive and visually appealing dashboards. Tableau focuses mainly on data visualization, so if you need to quickly build polished, detailed visual reports, it's an excellent option.

Tableau Dashboard Sample

But when it comes to data modeling, Power BI is often preferred.

Users on Reddit frequently mention that while Tableau excels in visualization, its data modeling capabilities are more limited compared to Power BI, which handles complex datasets better. Though for quick data exploration and time series analysis, Tableau tends to be faster and easier to use.

When it comes to handling large datasets, Tableau usually outperforms Power BI. Users have noted that it manages big data well, especially for visual-heavy tasks. Plus, Tableau works smoothly with Python and R, which makes it even more appealing for advanced analytics.

That said, Tableau is definitely more expensive, with the Creator plan running at $70 per user per month. For companies already invested in the Microsoft ecosystem, Power BI might be the more affordable option.

In short, if you need powerful, customizable dashboards, Tableau’s your best bet. But if you’re more focused on data modeling and affordability, Power BI might be the way to go.

Looker

Looker is a cloud-based BI tool focused heavily on data modeling through its LookML language, making it a great option for organizations using Google BigQuery or other SQL-based databases. Similar to Holistics, its strength lies in its ability to create reusable, structured data models, streamlining how teams manage and explore large datasets.

Image Source: https://docs.looker.com/dashboards/creating-lookml-dashboards

When comparing Looker to Power BI, the two tools serve different needs. Looker excels in data modeling and is better for users who need to manage large datasets and create structured, reusable models that can be exposed to end users for self-service. As pointed out by many users, Looker’s backend capabilities surpass Power BI’s, but its front-end visualizations don’t match Power BI's sleek, flexible dashboards​.

Under the same post, another user notes that Power BI has more data connectors, making it easier to pull data from various sources, while Looker is more limited in this regard​.

But again, Looker comes with a higher price tag. The number varies depending on the number of users and data sources.

Historically, its starting price is around $35,000 annually, and it can increase based on usage and enterprise needs​. 

Qlik Sense 

Being known for its capabilities in data exploration, especially with its associative data model, Qlik Sense lets users easily connect data from multiple sources without having to write complicated queries. This makes it perfect for businesses that need flexibility when working with complex datasets. Qlik’s associative model keeps all data views in sync while users interact with them, making it great for uncovering hidden relationships. In contrast, Power BI is more structured, offering data connectivity with cloud services like Azure and Excel.

When it comes to ease of use, Power BI tends to be easier for beginners. Over on Reddit, some users have mentioned that Qlik Sense can feel more complicated and has a steeper learning curve.

On the visualization side, Qlik Sense’s visualizations are interactive and adapt in real-time, making it perfect for exploring different datasets dynamically. But Power BI still has the edge when it comes to customization. It offers a wider range of customization options, plus cool AI-powered visuals like decomposition trees and key influencers for extra insights.

On the pricing side, Qlik Sense Business starts at $30 per user per month, with custom pricing for enterprise plans. 

Metabase

For teams looking for a user-friendly BI tool, this one stands out for its ease of use and versatility. Built for simplicity, Metabase allows users of all skill levels to explore data through interactive dashboards without needing SQL knowledge. For those who prefer more advanced control, it also supports SQL queries, making it flexible for various business needs. With its ability to connect to numerous databases like MySQL and PostgreSQL, it streamlines data analysis and reports sharing through Slack or email.

When compared to Power BI, Metabase is much easier for non-technical users to get started with, thanks to its lower learning curve. Both platforms offer interactive dashboards, but Metabase makes report creation accessible to everyone.

Metabase offers both an open-source version, which is free to use, and several premium plans for additional features like advanced governance, security, and customization. The premium plans begin at around $85 per month, making it a cost-effective solution for businesses that need more features but don't want to commit to the larger price tag of Power BI.

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Read more: Metabase Pros, Cons and Alternatives

Sigma Computing

For business users familiar with Excel, Sigma Computing provides a much more accessible way to explore and analyze data. Instead of learning complex modeling languages like DAX in Power BI, Sigma lets users work within a familiar, spreadsheet-like interface. This simplicity means less time spent on training and more time diving into data. On top of that, Sigma supports real-time collaboration, so teams can work together on the same datasets, share insights instantly, and make faster decisions.

One of the key differences between Sigma and Power BI is in the user experience. While Power BI offers robust tools for data modeling and analysis, it can be challenging for non-technical users to master. Sigma, on the other hand, focuses on making data exploration easier for business users with its intuitive, Excel-like interface. Reddit users have pointed out that while Power BI excels in handling complex data transformations, Sigma's simplicity makes it more accessible for everyday business tasks​.

In terms of cost, Sigma is on the higher end, starting at around $25,000 per year, which is significantly more than Power BI's more affordable tiers. 

Redash

Redash is a simple yet powerful tool designed to make data analysis more accessible, especially for teams that are comfortable using SQL. It's built around running SQL queries, which makes it ideal for users who want to dig into their databases directly. With Redash, you can easily write queries, create custom visualizations, and share insights across your team. It’s all about flexibility and simplicity, with support for various data sources, including cloud services, SQL databases, and APIs. Plus, if you're into open-source tools, Redash has a version you can host yourself, making it budget-friendly for tech-savvy teams.

One downside of Redash, as oft-noted on G2, is that while it provides easy setup and administration, it doesn't offer the same level of product support and feature updates as Power BI, which benefits from Microsoft's large-scale development efforts. Redash does not provide clear pricing details publicly, but it is known to offer both a paid version and an open-source version. 

Domo

When it comes to embedded analytics, one platform that's gained a lot of attention is Domo. Domo is a feature-rich BI tool built for teams that need seamless embedded analytics and advanced data visualizations

A key distinction between Domo and Power BI is Domo's user-friendly design. Power BI, while stronger in data modeling and advanced analytics, can be a bit tricky for beginners. Domo’s real advantage is how it simplifies data integration and automation, making it a top choice for teams that need real-time insights without having to tackle a steep learning curve.

On pricing, Domo tends to be on the higher end. Many companies report spending over $150,000 a year, particularly for data warehousing options according to Gartner Peer Insights. While Power BI is more affordable and integrates well with the Microsoft ecosystem, Domo’s higher price tag comes with the benefit of easy deployment and accessibility for non-technical users.

Sisense

If you're looking for a BI tool to embed analytics in your SaaS product, you've probably stumbled upon Sisense. It’s a popular choice for embedded analytics thanks to its powerful APIs and multi-tenancy. 

Sisense offers powerful APIs, plenty of flexibility for in-product analytics, and robust customizability for developers. Its drag-and-drop interface allows for easy dashboard creation, and the platform provides real-time data analytics. With its in-chip technology, Sisense optimizes query performance by reducing the need for complex data transformations.

While Sisense has several strengths, users on Reddit often highlight its limitations compared to Power BI. For example, one user expressed frustration with the amount of JavaScript required for simple tasks in Sisense, whereas Power BI is considered more user-friendly and less code-intensive.

Similarly, users also mentions that Power BI’s visualizations are significantly better, with Sisense having unnecessary whitespace and bugs. Additionally, Power BI offers a much stronger mobile app experience, making it easier to visualize data on the go.

Sisense pricing is generally more competitive than Power BI, which could be a factor in its appeal. Sisense pricing typically starts around $10,000 per year for a self-hosted option with five users, while cloud-hosted options begin at about $21,000 annually. For larger organizations, costs can escalate to between $35,000 and $60,000 per year, depending on user numbers and access levels.

Evidence.Dev

Evidence allows data teams to quickly transform raw SQL queries into actionable, real-time reports. This approach appeals to developers who prefer coding their reports without the complexity of additional tools or software layers, making it an efficient choice for SQL-savvy users. Plus, its Markdown support lets you add text and code snippets for extra context and narrative-building in your reports. As an open-source tool, it offers plenty of flexibility for teams that need a tailored reporting setup.

In terms of pricing, Evidence.dev offers a free open-source version that can be self-hosted, while the premium hosted plan starts at approximately $50 per month, which includes additional features and support.


Conclusion

All the above products have their own benefits and drawbacks. Choosing the right BI tool for your company will depend on a lot of aspects including the size of the company, your data maturity, your budget, and importantly, the use cases you want to solve.

It is not a one-day job so my recommendation is that you should involve all the related parties (data builders and data consumers) of your company to clearly list out all the requirements, narrow it down to the top 4-5 tools, and try out all the tools.

If you need a little more pointers to choosing the right tool, you might want to check out Chapter 4 in The Analytics Setup Guidebook which will discuss in detail how you can navigate the Business Intelligence tool space.