Databricks, a leading lakehouse platform, provides a collaborative environment for data, analytics, and AI.
In this article, I compare Databricks deployments on two of the leading cloud providers, Azure and AWS, highlighting the key differences in integration, pricing, features, and use cases to help you make an informed decision.
When considering Databricks Azure vs AWS, it’s crucial to understand how each platform’s unique characteristics can impact your data strategy. This Databricks comparison will explore the nuances of cloud data platform differences.
Overview of Databricks on Azure
Azure Databricks is a first-party Microsoft service, co-developed with Databricks to ensure seamless integration with the Azure ecosystem. This native integration makes it a compelling choice for organizations already invested in Azure.
- Key Features: At its core, Azure Databricks is built on a lakehouse architecture, powered by the Photon engine, which delivers up to 12 times better price/performance. It offers robust support for SQL, Python, and Spark, complemented by native tools like notebooks and workflows.
- Integrations: Azure Databricks offers deep integrations with a wide range of Azure services, including Azure Active Directory (now Microsoft Entra ID), Microsoft Purview, Azure Synapse Analytics, Power BI, Azure Data Factory, Azure Machine Learning, and Event Hubs. For more on this topic, see our article on Azure Databricks vs Databricks: Key Differences.
- Availability: The service is available in over 44 Azure regions and provides enterprise-grade security with certifications like SOC 2, ISO 27001, and HIPAA compliance.
Overview of Databricks on AWS
Databricks on AWS is a unified platform available through the AWS Marketplace, providing tight integration with the extensive AWS ecosystem.
- Key Features: It leverages Amazon S3 for its lakehouse architecture with Delta Lake, ensuring ACID transactions for your data. A key advantage is its support for Graviton processors, which can offer significant price/performance benefits. It also features MLflow for end-to-end AI workflows.
- Integrations: Databricks on AWS seamlessly integrates with a host of AWS services, such as AWS Identity and Access Management (IAM), S3, AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon SageMaker, AWS Lambda, Amazon Athena, and Amazon QuickSight.
- Availability: Available in numerous AWS regions, it also holds several certifications, including SOC 2, ISO 27001, PCI DSS, and FedRAMP.
Key Differences Between Databricks on Azure and AWS
While both platforms offer the core Databricks experience, there are crucial differences to consider. For a deeper dive into the world of data platforms, you can explore our blog.
Integration and Ecosystem
- Azure: Offers deeper, native integrations with the Azure stack. For instance, it uses Azure Data Lake Storage (ADLS) Gen2, which provides hierarchical storage, and integrates seamlessly with Azure Synapse for data warehousing and Microsoft Purview for governance.
- AWS: Provides strong connections to the AWS ecosystem. It relies on Amazon S3 for object storage, AWS Glue for ETL processes, and Amazon SageMaker for machine learning.
| Feature | Azure Databricks | AWS Databricks |
|---|---|---|
| Storage | Azure Data Lake Storage (ADLS) Gen2 | Amazon S3 |
| ETL | Azure Data Factory | AWS Glue |
| ML | Azure Machine Learning | Amazon SageMaker |
Pricing and Cost Structure
- Azure: Utilizes a pay-as-you-go model with Databricks Units (DBUs), with prices ranging from approximately $0.15 to $0.60 per DBU. It often bundles costs with other Azure services and offers up to 37% discounts on 3-year reservations and the use of spot VMs. For more detailed information, you can refer to the official Azure Databricks Pricing.
- AWS: Employs a per-second billing model with DBUs, with prices ranging from about $0.10 to $0.70 per DBU. It provides more granular pricing for EC2 instances, spot instances, and Savings Plans. More information can be found on the Databricks Pricing page.
| Job Type | Azure DBU Price (approx.) | AWS DBU Price (approx.) |
|---|---|---|
| Classic Jobs | $0.15 – $0.30 | $0.15 – $0.20 |
Overall, AWS often provides more flexibility with spot pricing, while Azure can be more cost-effective for organizations with a bundled ecosystem.
Security and Compliance
- Azure: Leverages Azure Key Vault for encryption and offers column-level security. It is deeply integrated with Microsoft Entra ID for robust identity and access management.
- AWS: Uses AWS Key Management Service (KMS) and provides field-level encryption. It offers more granular control with IAM and supports advanced networking with VPC peering and AWS Transit Gateway.
Both platforms support Bring Your Own Key (BYOK) and hold similar certifications. You can read more about security in our article about the Best Data Storage Platforms for Protecting Sensitive Data in 2025.
Performance and Compute
- Azure: Utilizes VM-based clusters, including the DSv3 and Ev3 series, and offers GPU support with the NCv3 and NDv2 series.
- AWS: Employs EC2-based clusters and provides a wider range of instance types (16 vs. 9), including Graviton processors for better cost efficiency and GPU instances like the P3 and G4 series. A performance comparison can be found on the Azure Blog.
Both platforms use the Photon engine for optimized performance, but the underlying cloud-specific hardware can lead to performance differences.
On-Premises Connectivity
- Azure: Offers connectivity through ExpressRoute and VPN Gateway.
- AWS: Provides connectivity via Direct Connect and VPN.
Pros and Cons
| Platform | Pros | Cons |
|---|---|---|
| Azure | – Unified billing – First-party support – Ideal for Azure-centric organizations |
– Less granular pricing – Potential for vendor lock-in |
| AWS | – Flexible networking and security – Broader instance options – Better for multi-cloud strategies |
– Separate billing from Databricks – Region-limited serverless options |
When to Choose Databricks on Azure vs AWS
- Choose Azure if: Your organization is heavily invested in the Microsoft ecosystem. The seamless integrations and unified governance will provide a significant advantage.
- Choose AWS if: You require flexible pricing, advanced security configurations, or are running AWS-native workloads.
Ultimately, the choice should be guided by your existing infrastructure and hybrid needs.
Conclusion
In summary, both Azure and AWS offer robust and powerful platforms for Databricks. The key differences lie in their integration with their respective cloud ecosystems, pricing models, and specific features. The best choice for your organization will depend on your existing cloud strategy and specific data analytics and AI requirements.
Ready to implement Databricks on Azure or AWS? Contact Dateonic for expert consulting and tailored data platform solutions.