Microsoft Fabric + Power BI

Likely, yes. Fabric's entry F2 capacity starts around $263/month, well above what a standalone Power BI + PostgreSQL or Snowflake setup costs. It earns its price specifically for organizations already running on Microsoft's cloud and consolidating multiple data workloads onto one platform.

Fabric bundles a unified OneLake data lake, Spark-based data engineering, and Power BI's reporting layer into one platform with shared governance, instead of connecting Power BI to a separately-run warehouse.

Below F64, yes. On the smaller Fabric capacities, anyone who opens a shared report needs a Power BI Pro or Premium Per User license, just as without Fabric. From F64 upward, people with a free license can view reports in workspaces on that capacity, and only the people who build and publish reports need Pro. That threshold drives the math for large audiences: a few hundred viewers at Pro prices can cost as much as the step up to F64 itself, so organizations with a wide read-only audience often size capacity around it.

Because every workload in Fabric draws from the same capacity. Fabric lets a heavy Spark job or pipeline burst above the capacity's size and then spreads the cost of that burst over the following hours; if the account stays over its limit for too long, Fabric throttles, delaying interactive work such as report queries first. Schedule heavy jobs outside business hours, split development and production onto separate capacities, and watch the capacity metrics app to see which items consume the most. For development capacities billed pay-as-you-go, pause them when nobody is working, since a paused capacity isn't billed.

It depends on who writes the data. Both store tables as Delta files in OneLake, so Power BI and other Fabric tools read either one. A Lakehouse is written with Spark and Python notebooks and can hold raw files next to tables, which suits data engineers and data scientists; its SQL endpoint is for reading. A Warehouse is written and queried with T-SQL, including inserts, updates, and multi-table transactions, which suits SQL developers and teams moving from a SQL Server or Synapse warehouse. A common setup uses both: a Lakehouse for raw and cleaned data, a Warehouse for the modeled tables reports read.