Cloud Usage and Costs with Databricks

Data, AI & Machine Learning Engineering Solution

context​

Organizations are increasingly migrating their analytics and data processing workloads to the cloud for better scalability and flexibility. However, with this comes the challenges of managing cloud resources efficiently and controlling associated costs. 

Databricks, a unified analytics platform built on Apache Spark, has emerged as a powerful solution for data engineering, data science, and Machine Learning workloads in cloud environments. As companies deploy more complex data pipelines and analytics solutions using Databricks across AWS, Azure, and Google Cloud, understanding and optimizing cloud usage and costs has become a critical concern for IT leaders and finance departments alike.

Databricks employs a cloud-based consumption model with per-second billing precision, allowing organizations to pay exclusively for the computing resources they utilize. This pay-as-you-go approach eliminates the need for upfront financial commitments or long-term contracts, providing flexibility for resources.

cloud

PAIN POINTs

The primary challenge users face with Databricks lies in the complexity and potential lack of transparency surrounding its cost structure. This complexity arises from several factors:

  • Abstract Billing Unit (DBU): The core metric, DBU (Databricks Billing Unit), requires understanding multiple variables to translate into actual cost: the specific VM instance types and count, differing rates across cloud providers (AWS, Azure, GCP), varying rates based on workload type (All-Purpose Compute, DLT, SQL Compute, etc.), and the user’s subscription plan tier. Total cost isn’t a single figure from Databricks. Users must track and sum costs from both Databricks (DBU consumption) and their cloud provider (AWS / Azure / GCP)
  • Additional Service Costs: Specialized managed services (e.g., Lakehouse Monitoring, Predictive Optimization, Model Serving) operate under separate pricing models distinct from core compute DBUs. 
  • Risk of Inefficient Resource Usage: The pay-as-you-go structure means inefficiency directly translates to higher bills. Costs can escalate if clusters are over-dimensioned, if resources aren’t automatically adjusted downwards during periods of low activity (leading to over-provisioning), or if clusters remain running and incurring charges when not actively processing data due to missed auto-termination opportunities.
  • Cost Attribution Difficulties: It can be challenging to clearly see which specific internal teams, projects, or business units generated particular Databricks usage and the related expenses. 

solution

There are several ways to manage and optimize Databricks costs:

  • Cost Estimation: Utilize Databricks’ provided DBU calculators (specific to each cloud provider) and the general Pricing Calculator to estimate costs based on configuration details.
  • Understanding Cost Components: Recognize that total cost comprises both Databricks fees (DBU consumption) and underlying cloud provider charges (VMs, storage, networking), except for Serverless compute where infrastructure costs are bundled into the DBU rate.
  • Cost Optimization: Implement proper cluster configuration by choosing appropriate sizes and types. Leverage Cluster Policies to enforce cost-saving measures like auto-scaling (to match resources to workload needs) and auto-termination (to shut down idle clusters).
  • Cost Monitoring & Attribution: Use the Dashboard usage feature to get a detailed view of DBU consumption across the account, workspace, and specific Databricks services. Implement custom tags via compute policies to accurately attribute usage and costs to different business units or teams.

benefits

  • Gain clarity on how DBU consumption translates into real monetary costs and estimate Databricks expenses using the Pricing Calculator.
  • Reduce overall cloud spending through efficient cluster management practices like appropriate sizing, auto-scaling, and auto-termination.
  • Achieve better visibility into spending patterns and identify key cost drivers using monitoring dashboards.
  • Improve cost accountability by attributing usage to specific teams or projects via tagging.
Technology Stack and Key Skills​

 

  • Core Platform: Databricks
  • Cloud Providers: AWS, Azure, GCP
  • Compute Resources: Virtual Machines (VMs) from cloud providers, Databricks Serverless Compute
  • Databricks Cost Management Features: DBU Calculators, Pricing Calculator, Dashboard usage, Cluster Policies, Custom Tags

Do you want to know more about our services? Fill in the form and schedule a meeting with our team!