Databricks Pricing: Comparing Azure and AWS Costs (2025 Guide)

Summary

This blog compares Databricks pricing on Azure and AWS, highlighting the factors that influence costs such as DBUs, compute, and storage. It offers a detailed breakdown of pricing models for both platforms and provides tips for cost optimization, such as using autoscaling and spot instances. Understanding the differences between AWS and Azure Databricks can help businesses make informed decisions to manage cloud costs effectively.

Introduction

When evaluating cloud-based data platforms, understanding the pricing structure of services like Databricks is crucial for businesses relying on data engineering solutions. Databricks offers powerful features for data analytics, machine learning, and data engineering tasks, making it a go-to platform for handling complex workflows. However, the cost can vary significantly depending on the platform. In this guide, we’ll dive into the pricing of Databricks on two of the leading cloud providers—AWS and Azure. By comparing the costs, we can help you make an informed decision on which platform delivers the best value for your business needs.

What is Databricks and How Does It Work?

Databricks is a cloud-based platform that combines the power of Apache Spark with various data management and machine learning tools. It provides an environment for developers, data scientists, and data engineers to collaboratively build and manage data pipelines, run analytics workloads, and create machine learning models. The platform is available on major cloud providers like AWS, Azure, and Google Cloud, each offering slightly different pricing models.

Understanding how Databricks pricing works is essential for businesses to control costs, but optimizing your Databricks usage goes hand-in-hand with leveraging skilled professionals. A Databricks Certified Data Engineer Associate can help optimize your workflows, ensuring that your Databricks setup is cost-effective and efficient. Learn how professional engineers can further enhance your data operations by exploring the right consulting resources for your needs, such as choosing the right data engineering consultant.

Key Features of Databricks

• Data Engineering: Databricks simplifies data engineering workflows by enabling scalable data processing and seamless integration with various data sources.
• Machine Learning: With native integration to MLflow, Databricks supports end-to-end machine learning model management, including training, testing, and deployment.
• Collaborative Notebooks: Data scientists and engineers can work in the same environment, facilitating collaboration with interactive notebooks that support multiple languages.

Importance of Understanding Databricks Pricing

Databricks has become a key tool for businesses looking to accelerate their data analytics and engineering tasks. As a unified analytics platform, it brings together data science, machine learning, and data engineering services. While Databricks’ functionality is top-tier, understanding its pricing structure across different cloud platforms is essential to ensure that your organization can maximize its investment. This comparison will explore the costs associated with Databricks on AWS and Azure, two of the most widely used cloud services.

Key Factors That Influence Databricks Pricing

Understanding how Databricks pricing works requires considering several factors that influence the final cost:

• Cloud Platform: The cost can vary significantly depending on whether you’re using AWS or Azure.
• DBUs (Databricks Units): Databricks uses DBUs as the unit of measurement for billing. The number of DBUs consumed depends on the type and size of the clusters.
• Compute and Storage: The type of virtual machines (VMs) you choose for your clusters, as well as the amount of data storage, can impact costs.
• Support and Additional Services: Databricks also offers premium support and add-ons like security features, which can affect the overall cost.

Understanding the DBU (Databricks Unit)

What is a DBU?

A Databricks Unit (DBU) represents a unit of processing capability per hour. It is used to quantify the computational resources consumed by Databricks workloads. DBUs are billed based on the virtual machines (VMs) and clusters you choose for your data engineering tasks. The cost of DBUs may vary depending on the cloud provider (AWS vs Azure) and region.

How is DBU Calculated?

DBU consumption depends on several factors:

• Type and size of clusters.
• Type of workload (e.g., interactive notebooks, jobs, or streaming workloads).
• Instance configurations and autoscaling settings.

Detailed Comparison of AWS Databricks and Azure Databricks Pricing

Both AWS and Azure offer Databricks services with their unique pricing models, but the core components of Databricks, such as DBUs, storage, and compute resources, remain the same.

Databricks on AWS

Databricks on AWS provides users with scalable compute resources, leveraging AWS services like EC2 and Amazon S3 for processing and storage. Pricing for Databricks on AWS is influenced by several key factors:

• DBU Usage:
  Databricks Units (DBUs) are the main metric for pricing. The number of DBUs consumed depends on the EC2 instance type and the specific Databricks workload.
• Compute Costs:
  AWS charges for compute resources based on per-second usage. The cost varies depending on the EC2 instance type (e.g., compute-optimized, memory-optimized, or storage-optimized), and the region in which it operates.
  Compute costs range from $0.07 to $0.70 per DBU, depending on the instance type and usage scenario.
• Storage Costs:
  Data storage on Amazon S3 incurs costs based on the volume of data stored and the frequency of read/write operations.
• Pricing Example:
  If you use 500 DBUs per hour at $0.70 per DBU, the total cost for DBUs would be $350. Adding the AWS compute charges (which range from $35 to $350), your total cost for the hour could range from $385 to $700.
• Discounts and Reserved Instances:AWS offers discounts for long-term reserved instances, which can significantly reduce costs for consistent workloads. Additionally, there are three pricing tiers and 16 different compute types to choose from, providing flexibility to optimize pricing based on usage patterns.

Databricks on Azure

Databricks on Azure is integrated seamlessly with Azure’s services, offering powerful tools like Azure Blob Storage, Azure Data Lake, and Azure Machine Learning. Pricing for Azure Databricks is influenced by several factors:

1. DBU Usage: Databricks Units (DBUs) are consumed based on the virtual machine (VM) configuration and workload, similar to AWS.
2. Compute Costs: Azure offers various VM types (e.g., Standard and Memory-optimized VMs) to suit different workload requirements. Costs will vary depending on the VM type and the region.
3. Storage Costs: Charges for data storage on Azure Blob Storage or Data Lake depend on the volume of data and retrieval frequency. Azure’s pricing is competitive with AWS, with some regional and service-specific differences.
4. Pricing Example: Azure Databricks charges for both DBUs and Azure resources, including virtual machines, managed storage, and disks. Discounts of up to 33% for one-year and 37% for three-year commitments are available, with further cost reductions possible by using spot instances.

Explore more on how businesses can optimize software development costs to make sure you’re leveraging cloud tools effectively.

Pricing Comparison Table

Key Pricing Differences Between AWS and Azure

While the core pricing models for Databricks on AWS and Azure are similar, there are some key differences:

• Compute Instance Pricing: AWS may offer more granular pricing based on instance types, whereas Azure’s pricing is often bundled with other Azure services.
• Storage: AWS’s S3 and Azure’s Blob Storage offer similar capabilities, but pricing varies depending on storage tiers and data redundancy options.
• Regional Differences: Both AWS and Azure have different pricing for various regions, which could impact your costs depending on where your data and workloads are hosted.

Cost Management and Optimization Tips for Databricks

To make the most of your Databricks investment, consider these cost management strategies:

Using Autoscaling for Cost Efficiency

Autoscaling allows you to automatically adjust the size of your clusters based on workload demands, which can save costs during periods of low usage while ensuring performance during peak times.

Selecting the Right Instance Types

Choosing the right instance type can significantly affect your pricing. Ensure you match the workload type (e.g., memory-heavy tasks, compute-intensive operations) to the appropriate instance.

Optimizing Storage and Compute Usage

Monitor your storage usage and delete unused data or optimize data formats to reduce costs. Use spot instances for non-critical workloads to further reduce compute costs.

Utilizing Spot Instances

Spot instances are unused VM capacity offered at a lower price, providing an affordable option for batch processing jobs that can tolerate interruptions.

Databricks Pricing Examples

Let’s look at a few example pricing scenarios to help understand how Databricks pricing works:

• Basic Data Engineering Workflow on AWS: A small data engineering workload on an m5.large EC2 instance with 2 DBUs could cost approximately $0.40/hour for DBUs plus EC2 instance charges.
• Machine Learning Model Training on Azure: A larger data science model on a DSv3 instance with 5 DBUs could run at a cost of $1.80/hour for DBUs and additional VM charges.

Conclusion

Databricks offers powerful tools for data science, engineering, and machine learning, and understanding its pricing structure on AWS vs Azure is crucial to making cost-effective decisions. The right choice depends on several factors including region, workload, and available services. Leverage cost optimization strategies like autoscaling, selecting the appropriate instance types, and considering spot instances to reduce your overall expenses. If you are seeking help optimizing your cloud costs, consulting with a Certified Data Engineer could provide valuable insight into best practices and advanced techniques to ensure you maximize your Databricks investment.

Frequently Asked Questions (FAQs)

What is the difference in pricing between Databricks on AWS and Azure?

While both platforms offer similar pricing models, AWS tends to provide more granular instance pricing, whereas Azure bundles more services into its pricing, which may lead to a better value for businesses already using Azure.

Does Databricks offer a free trial?

Yes, Databricks offers a free trial with limited usage to help businesses get started with their platform.

How does Databricks billing work on AWS and Azure?

Databricks is billed based on DBUs, compute time, and storage usage, with charges varying depending on the cloud platform and configuration.

Is Databricks more cost-effective than other analytics platforms?

While Databricks can be more expensive than some other platforms, its flexibility and

Can I switch between AWS and Azure for Databricks?

Yes, you can switch, but it requires setting up a new workspace, migrating data, and ensuring compatibility, as Databricks doesn’t provide direct multi-cloud interoperability.

Databricks

Nirmalsinh Rathod

Director - Mobile Technologies