Microsoft Fabric vs Azure Databricks
Cost Calculator
Model Fabric capacity and Azure Databricks separately, then compare the monthly total: compute, storage and Power BI licensing. Built on published US list rates, with every assumption shown.
Is Microsoft Fabric cheaper than Databricks?
It depends on how you use them. Fabric is a shared capacity you pay for while it runs (or a fixed reservation), and F64 and above lets free-license users view Power BI reports. OneLake storage is billed separately.
Azure Databricks charges DBUs plus separate VM costs on classic compute, but only for the hours clusters run. Steady always-on BI favors Fabric reservations; short scheduled pipelines can favor Databricks. Enter your own numbers below.
How to use this calculator
Fabric and Databricks are billed in different units (capacity units versus DBUs plus VMs), so there is no valid conversion between an F-SKU and a cluster. This tool sizes each side independently. Presets are illustrative scenarios, not equivalent hardware.
Set the Fabric SKU and how many hours per day it stays running. Then describe the Databricks compute you would actually run: node type and count for Jobs or All-Purpose, or a warehouse size for SQL. Add storage and Power BI users.
Microsoft Fabric
Azure Databricks
Steady BI workloads
F64+ can remove per-viewer Power BI licensing for eligible viewers, and a reservation gives a fixed monthly price.
Scheduled engineering
Jobs Compute has a lower DBU rate than All-Purpose, but VM configuration and runtime also drive total cost.
Bursty workloads
Fabric pay-as-you-go can be paused, and Databricks clusters auto-terminate. Compare how many hours you truly need.
Reservations
A Fabric reservation only beats pay-as-you-go above roughly 60% of hours running. Test both in the calculator.
How each platform is billed
Microsoft Fabric sells a shared pool of capacity units (CUs). Pay-as-you-go is $0.18 per CU-hour in US regions while the capacity is running, and Microsoft’s monthly figures use 730 hours (an F64 is about $8,410). A one-year reservation is a fixed price, about $5,003 for F64. OneLake storage is billed separately, and Power BI licensing depends on the SKU.
Azure Databricks on classic compute bills DBUs, whose count depends on the selected VM instances, plus the underlying VMs, disks and networking, all through your Azure account. Serverless SQL includes the compute in its DBU rate, though networking and other charges can apply.
Rates and assumptions used
| Input | Value | Type |
|---|---|---|
| Fabric pay-as-you-go | $0.18 per CU-hour, US, 730 hours/month at 24×7 | Published rate (region varies) |
| Fabric reservation | F4 $313, F8 $625, F16 $1,251, F32 $2,501, F64 $5,003, F128 $10,005 per month | Published rate (about 41% off) |
| Fabric viewer licensing | Pro $14 per user/month below F64; free viewers at F64+ | Published rule |
| Databricks DBU rates (Azure Premium) | Jobs $0.30, All-Purpose $0.55, SQL Classic $0.22, SQL Pro $0.55, Serverless SQL $0.70 per DBU-hour | Published rates (US) |
| SQL warehouse DBUs | 2X-Small 4, X-Small 6, Small 12, Medium 24, Large 40, X-Large 80, 2X-Large 144, 3X-Large 272, 4X-Large 528 per hour | Published table |
| Node DBU ratings | D4s v5 1.00, D8s v5 2.00, D16s v5 4.00, E8s v5 2.75, E16s v5 5.50 per hour | Published table (non-Photon) |
| VM prices | Approximate Linux pay-as-you-go, East US; SQL Classic/Pro VM about $0.32 per DBU | UIG planning assumption: override |
| Storage | $0.023 per GB-month hot | Published OneLake hot rate |
Formulas
Worked example (default inputs)
Fabric F8 running 24 hours a day on pay-as-you-go: 8 x $0.18 x 730 = $1,051. Add 10 TB of hot storage (10,240 GB x $0.023 = $236) and Power BI Pro for 100 viewers and 10 creators (110 x $14 = $1,540), because F8 is below F64. Total: about $2,827 per month.
Databricks Jobs Compute on 5 D8s v5 nodes for 8 hours a day (243 hours a month): DBUs 10 per hour x $0.30 x 243 = $730, VMs 5 x $0.384 x 243 = $467, plus the same storage and licences. Total: about $2,973 per month. Change any input and the answer moves, which is the point: neither platform is cheaper in general.
What this model leaves out
Photon increases DBU consumption; Delta Live Tables, serverless jobs and model serving have their own rates; Spot and Reserved VM pricing, autoscaling, disks, networking and egress all change the Databricks bill. On Fabric, throttling and smoothing affect whether a given SKU is big enough, and autoscale billing for Spark uses a separate consumption model. Regional prices and negotiated discounts apply to both. Use this tool to narrow the decision, then validate with Microsoft’s and Databricks’ own calculators.