Azure Databricks vs AWS vs GCP: Cloud Platform Comparison

Databricks is available on three major cloud platforms—Azure, AWS, and Google Cloud Platform—with each deployment offering the core Databricks capabilities while leveraging unique cloud-specific integrations and services. Choosing between Azure Databricks, Databricks on AWS, and Databricks on GCP depends on your existing cloud infrastructure, preferred cloud services ecosystem, regional availability requirements, and specific integration needs. While the fundamental Databricks experience remains consistent across clouds, understanding the differences in integrations, features, pricing, and cloud service ecosystems helps organizations select the optimal cloud platform for their Databricks deployment. All three options provide excellent performance and capabilities, making the decision primarily about cloud alignment rather than Databricks functionality.

Databricks Core Capabilities Across All Clouds

Before examining cloud-specific differences, it's important to understand that Databricks provides consistent core functionality across Azure, AWS, and GCP:

Universal Features

• Unified workspace: Same notebook interface and collaboration features
• Apache Spark: Identical Spark runtime and optimizations
• Delta Lake: Consistent storage layer capabilities
• MLflow: Same machine learning lifecycle management
• Databricks SQL: SQL analytics engine across all platforms
• Unity Catalog: Unified governance (where available)
• APIs and SDKs: Consistent programmatic interfaces

This consistency means workloads developed on one cloud can migrate to another with minimal changes, providing flexibility in cloud strategy.

Azure Databricks: Deep Microsoft Integration

Azure Databricks represents the tightest integration between Databricks and a cloud provider, resulting from the strategic partnership between Databricks and Microsoft.

Key Advantages of Azure Databricks

Native Azure Service
Azure Databricks is a first-party Azure service, providing several benefits:

• Unified billing through Azure subscription
• Seamless integration with Azure Active Directory
• Native VNet injection for security
• Azure Monitor integration for observability
• Consistent Azure portal experience

Microsoft Ecosystem Integration
Azure Databricks excels at integrating with Microsoft's extensive ecosystem:

• Power BI: Direct connectivity for visualization and reporting
• Azure Synapse Analytics: Integration for unified analytics
• Azure Data Factory: Native orchestration capabilities
• Azure Data Lake Storage: Optimized storage integration
• Azure Key Vault: Secure secrets management
• Microsoft Purview: Extended governance capabilities

Enterprise Microsoft Shops
Organizations heavily invested in Microsoft technologies benefit from:

• Unified enterprise agreement and licensing
• Consistent identity and access management with Azure AD
• Integrated security and compliance frameworks
• Single support relationship with Microsoft

Azure Databricks Unique Features

• Table Access Control: Fine-grained permissions integrated with Azure AD
• Premium tier exclusive features: Some features launched first on Azure
• Azure-specific optimizations: Performance tuning for Azure infrastructure
• Managed VNet: Simplified networking configuration

Ideal Azure Databricks Use Cases

• Organizations primarily using Microsoft technologies
• Enterprises with Azure-first cloud strategy
• Power BI and Azure Synapse users
• Companies requiring tight Azure ecosystem integration
• Organizations leveraging Azure's compliance and security features

Databricks on AWS: Mature and Feature-Rich

As the first cloud where Databricks launched, AWS offers the most mature Databricks deployment with extensive features and integrations.

Key Advantages of Databricks on AWS

Extensive AWS Service Integration
Databricks integrates deeply with AWS's comprehensive service catalog:

• Amazon S3: Primary storage with optimized access patterns
• AWS Glue: Metadata catalog integration
• AWS IAM: Fine-grained access control
• Amazon Kinesis: Streaming data ingestion
• AWS Lambda: Event-driven processing integration
• Amazon EMR: Interoperability with existing Hadoop workloads
• AWS Lake Formation: Data lake governance integration

Maturity and Stability
AWS Databricks benefits from being the longest-running deployment:

• Most battle-tested at scale
• Extensive documentation and community knowledge
• Proven performance optimizations
• Mature operational practices

AWS Marketplace
Available through AWS Marketplace enabling:

• Unified procurement through AWS
• Consumption against AWS commitments
• Simplified purchasing for AWS-centric organizations

AWS-Specific Features

• PrivateLink: Secure connectivity to AWS services
• Instance types: Widest selection of EC2 instance types
• Regional availability: Available in most AWS regions
• Graviton support: ARM-based instances for cost optimization

Ideal AWS Databricks Use Cases

• Organizations with AWS-first strategy
• Companies heavily using AWS services ecosystem
• Workloads requiring specific EC2 instance types
• Enterprises with significant AWS commitments
• Organizations in AWS-specific regions

Databricks on GCP: Modern Architecture and Google Integration

Databricks on Google Cloud Platform is the newest deployment, offering modern architecture and integration with Google's innovative services.

Key Advantages of Databricks on GCP

Google Cloud Integration
Databricks leverages GCP's service ecosystem:

• Google Cloud Storage: Scalable object storage
• BigQuery: Integration with Google's data warehouse
• Cloud Composer: Managed Apache Airflow for orchestration
• Vertex AI: Integration with Google's AI platform
• Cloud IAM: Google's identity and access management
• Cloud Logging and Monitoring: Observability integration

Modern Architecture Benefits
Being newest enables incorporation of latest best practices:

• Clean implementation leveraging recent innovations
• Modern security architecture from ground up
• Simplified deployment and configuration
• Benefit from lessons learned on other clouds

Google's Data and AI Ecosystem
Organizations using Google's data tools benefit from:

• Integration with BigQuery for SQL analytics
• Connection to TensorFlow and Google AI tools
• Looker integration for business intelligence
• Access to Google's AI/ML research innovations

GCP-Specific Features

• Private Google Access: Secure connectivity to GCP services
• GCS integration: Optimized Google Cloud Storage access
• Google's network: Leverage Google's global fiber network
• Committed use discounts: Integration with GCP pricing models

Ideal GCP Databricks Use Cases

• Organizations with GCP-first cloud strategy
• Companies using BigQuery and Google's analytics ecosystem
• Enterprises leveraging Google AI services
• Organizations appreciating Google's network performance
• Companies preferring Google's operational model

Feature Availability Comparison

While core Databricks features are consistent, some advanced features have different availability timelines across clouds:

Generally Available Everywhere

• Databricks Runtime and Spark
• Notebooks and collaborative workspace
• Jobs scheduling and orchestration
• Delta Lake
• MLflow
• Databricks SQL
• REST APIs

Platform-Specific Timing

New features often launch first on one platform before rolling out to others:

• Azure typically receives Microsoft-related integrations first
• AWS often gets certain features first due to market maturity
• GCP receives features after validation on other clouds
• Unity Catalog availability varies by cloud

Performance and Pricing Considerations

Performance

Performance is generally comparable across clouds for equivalent workloads:

• Compute performance: Similar for equivalent instance types
• Storage performance: All clouds provide high-performance object storage
• Network performance: Varies by cloud provider's network architecture
• Regional differences: Performance may vary by specific region

Pricing Structure

Each cloud has distinct pricing characteristics:

Azure Databricks:

• DBU pricing plus Azure VM costs
• Integration with Azure reservations and enterprise agreements
• Azure-specific discounting programs

AWS Databricks:

• DBU pricing plus EC2 instance costs
• Can count against AWS commitments via Marketplace
• Access to AWS reserved instances and savings plans

GCP Databricks:

• DBU pricing plus Compute Engine costs
• GCP committed use discounts
• Sustained use discounts automatically applied

Total cost comparison requires analyzing both Databricks DBUs and underlying cloud infrastructure costs specific to your workload patterns.

Regional Availability and Compliance

Geographic Coverage

• AWS: Broadest regional availability globally
• Azure: Extensive coverage with strong presence in Microsoft-focused markets
• GCP: Growing coverage, may have fewer options in some regions

Compliance Certifications

All three clouds support major compliance frameworks, but specific certifications may vary:

• SOC 2 Type II available on all clouds
• HIPAA compliance available across platforms
• GDPR compliance supported on all clouds
• Region-specific certifications may differ
• Government cloud availability varies

Making Your Cloud Choice

Select your Databricks cloud platform based on these factors:

Choose Azure Databricks If:

• You're primarily a Microsoft shop
• You use Power BI, Azure Synapse, or other Azure data services
• You have Azure enterprise agreements
• Your organization has Azure-first cloud policy
• You need tight Azure Active Directory integration

Choose AWS Databricks If:

• Your infrastructure is primarily on AWS
• You use extensive AWS services ecosystem
• You value the maturity and stability of longest-running deployment
• You need specific EC2 instance types
• You have AWS commitments to consume

Choose GCP Databricks If:

• You're committed to Google Cloud Platform
• You use BigQuery, Vertex AI, or Google's data ecosystem
• You prefer Google's operational approach
• You value modern architecture implementations
• Your data strategy involves Google tools

Multi-Cloud Considerations

Some organizations deploy Databricks on multiple clouds:

• Different clouds for different business units
• Regional requirements necessitating multiple clouds
• Risk mitigation through multi-cloud strategy
• Customer-specific deployment requirements

Databricks' consistent experience across clouds makes multi-cloud deployments feasible.

Conclusion

Azure Databricks, Databricks on AWS, and Databricks on GCP all provide excellent platforms for unified data analytics and machine learning. Azure Databricks excels for Microsoft-centric organizations with its deep Azure integration and first-party service status. Databricks on AWS offers the most mature deployment with extensive AWS ecosystem integration and the broadest regional availability. Databricks on GCP provides modern architecture and strong integration with Google's innovative data and AI services.

Your choice should align with your existing cloud investments, preferred cloud ecosystem, team expertise, and specific integration requirements. All three clouds deliver the powerful lakehouse capabilities, machine learning features, and performance that make Databricks valuable. The consistency of the Databricks experience across clouds means you're choosing your cloud platform context more than different Databricks capabilities. Organizations can feel confident that regardless of cloud choice, they'll access Databricks' comprehensive data and AI platform with the specific cloud integrations that best fit their technology strategy and operational preferences.