Compare Microsoft Power BI and Databricks on pricing, features, platforms, and ratings — both Business Intelligence tools, Subscription vs Usage-based…
At a Glance
Microsoft Power BI
Databricks
Primary category
Business Intelligence
Business Intelligence
Rating
Not documented
Not documented
Pricing model
Subscription
Usage-based (pay-as-you-go per Databricks Unit)
Starting price
Free (Power BI Desktop); $14/user/month for Pro
Free Community Edition available; paid usage billed per Databricks Unit (DBU) plus underlying cloud compute costs
Free plan
Yes
Yes
Free trial
Not documented
Yes
Platforms
Web, Windows
Web
Team collaboration
Not documented
Not documented
AI features
Yes
Yes
Public API
Not documented
Yes
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Microsoft Power BI
Power BI Desktop — Free N/A
Power BI Pro — $14/user/month Annual
Power BI Premium Per User — $24/user/month Annual
Databricks
Premium — Pay-as-you-go, from ~$0.07/DBU Usage-based
Enterprise — Custom (negotiated with sales) Usage-based
Pros & Cons
Microsoft Power BI
Pros
Power BI Desktop is free for individual report building
Tight integration with Excel and the broader Microsoft 365/Azure ecosystem
Used by over 350,000 organizations, indicating a mature ecosystem and community
Scales from a single Pro seat to enterprise-wide capacity or Fabric licensing
Frequent feature updates, with over 1,500 shipped since its 2015 launch
Cons
Pro and Premium Per User prices increased significantly in April 2025 ($10 to $14 and $20 to $24)
Sharing and collaborating on reports requires a paid Pro or Premium license even for viewers in some configurations
Capacity-based Premium licensing is being phased toward Fabric, adding complexity for long-term planning
DAX and data modeling have a real learning curve for non-technical users
Unified platform reduces the need for separate data warehouse, data lake, and ML tooling
Built on widely adopted open-source projects (Spark, Delta Lake, MLflow), lowering vendor lock-in risk
Scales efficiently for very large data engineering and machine learning workloads
Strong native support for generative AI and custom model development via Mosaic AI
Cons
Consumption-based DBU pricing can be complex to forecast and may escalate at scale
Requires meaningful technical expertise in Spark, SQL, and cloud infrastructure
No flat, predictable starting price for smaller teams or individual users
Cost governance and cluster configuration require dedicated platform engineering resources
Use Cases
Microsoft Power BI
Business reporting and dashboards — Analysts build interactive dashboards that let stakeholders explore sales, marketing, or operations data without needing a dedicated analyst for every question.
Enterprise data governance and self-service BI — IT and data platform teams create governed datasets that business users can safely build their own reports on top of.
Excel-integrated financial and operational analysis — Finance and operations teams already working in Excel extend their analysis into interactive Power BI reports using the same underlying data.
Databricks
Enterprise Data Engineering and ETL — Building and orchestrating large-scale batch and streaming data pipelines using Delta Lake and Databricks Workflows.
Machine Learning and Generative AI Development — Training, tracking, and deploying ML models and building custom LLM applications with Mosaic AI and MLflow.
Enterprise Analytics at Scale — Running warehouse-style SQL analytics and BI reporting directly on governed lakehouse data via Databricks SQL.