Azure Databricks Cost Calculator

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Reviewed and Verified by David Chen, CFA, Cloud Solutions Architect.

Accurately estimate your monthly expenses for leveraging Azure Databricks. This calculator uses key variables like DBU price, VM size, and usage hours to forecast your total cloud spend, helping you optimize your data platform budget.

Azure Databricks Cost Calculator

Estimated Monthly Cost

$0.00

Azure Databricks Cost Calculator Formula

Total Monthly Cost = Cluster Size × Hours × ( (DBU Rate × DBU Consumption) + VM Rate )

Where:

  • Cluster Size is the Average Cluster Size (VMs)
  • Hours is the Total Running Hours per Month
  • DBU Rate is the DBU Price per Unit ($)
  • DBU Consumption is the DBUs Consumed per VM Hour
  • VM Rate is the Azure VM Price per Hour ($)

Formula Source: For detailed pricing models, please refer to the official Azure Databricks Pricing and Databricks Corporate Pricing documentation.

Variables Explained

  • DBU Price per Unit ($): The cost charged by Databricks for one Databricks Unit (DBU). This varies based on workload (e.g., All-Purpose, Jobs) and region.
  • Azure VM Price per Hour ($): The cost charged by Azure for the underlying Virtual Machines (Compute). This depends on VM size (vCPUs/RAM) and region.
  • DBUs Consumed per VM Hour: A factor indicating how many DBUs are typically consumed by a single VM in one hour, depending on the workload type.
  • Average Cluster Size (VMs): The number of Virtual Machines running concurrently in your cluster.
  • Total Running Hours per Month: The cumulative hours your cluster is actively running and processing data jobs in a month.

Related Calculators

What is Azure Databricks Cost Calculator?

The Azure Databricks Cost Calculator is an essential tool designed to estimate the monthly expenditure associated with running a Databricks environment on the Azure cloud platform. Since Databricks pricing is bifurcated—combining a compute cost (Azure VMs) and a software cost (Databricks Units or DBUs)—manual cost estimation can be complex. This calculator simplifies the process by consolidating the key consumption metrics into a single, predictable figure.

Understanding these costs is vital for budget planning, especially for organizations running large-scale data science, machine learning, and ETL workloads. By adjusting variables like cluster size and usage hours, users can model various scenarios to find the most cost-effective configuration for their data infrastructure needs, moving from variable cloud spending to controlled operational costs (OpEx).

How to Calculate Azure Databricks Cost (Example)

  1. Define Rates: Assume DBU Price is $0.40/unit and Azure VM Price is $0.60/hour.
  2. Determine DBU Consumption: Assume the workload consumes 2 DBUs per VM hour.
  3. Define Usage: Assume an average cluster size of 10 VMs running for 100 hours per month.
  4. Calculate DBU Component Cost: ($0.40 DBU Rate × 2 DBUs/Hour × 10 VMs × 100 Hours) = $800.00
  5. Calculate VM Component Cost: ($0.60 VM Rate × 10 VMs × 100 Hours) = $600.00
  6. Calculate Total Monthly Cost: $800.00 (DBU) + $600.00 (VM) = $1,400.00

Frequently Asked Questions (FAQ)

  • How do I find my DBU Price per Unit? DBU pricing is published by Databricks/Azure and is dependent on the workload type (e.g., All-Purpose vs. Jobs Compute) and the region where your workspace is deployed.
  • What is the difference between DBU cost and VM cost? VM cost covers the basic underlying Azure infrastructure (compute, memory). DBU cost covers the proprietary Databricks platform features, managed services, and optimizations running on top of those VMs.
  • Does this calculator include storage costs? No. This calculator primarily focuses on compute costs (VMs + DBUs). Storage (like ADLS Gen2) is typically billed separately by Azure based on volume and transactions, which is often a smaller component of the total bill.
  • What is a reasonable “DBUs Consumed per VM Hour” value? This is highly dependent on your specific usage. For general-purpose workloads, a value between 1.5 to 3 is common, but optimized jobs might consume less, and demanding interactive environments might consume more.
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