Databricks on Azure vs. AWS vs. GCP: What Actually Differs

The core Databricks experience - Spark runtime, Delta Lake, notebooks, MLflow, SQL analytics - is the same on all three clouds. What differs is the integration layer: how Databricks connects to each cloud's identity system, storage, networking, billing, and native services.

Most organizations choose their Databricks cloud based on where they already run infrastructure, not on Databricks-specific features. An enterprise with 200 Azure subscriptions, Microsoft Entra ID for identity, and Power BI for reporting is not going to deploy Databricks on GCP to save 3% on compute. Cloud selection is an infrastructure decision, and this guide focuses on the differences that actually affect day-to-day operations, cost, and governance.

What Stays the Same Across Clouds

The Spark runtime, Delta Lake storage format, Unity Catalog, MLflow, Databricks SQL, REST APIs, and notebook experience are the same on every cloud, although individual features can reach clouds and regions at different times. A PySpark job developed on Azure will run on AWS with minor config changes (swap ADLS paths for S3 paths, and Azure storage credentials for IAM roles). Teams running multi-cloud or considering migration can rely on this portability - the Databricks platform layer abstracts most of the cloud-specific storage and compute differences underneath.

Azure Databricks: First-Party Microsoft Integration

Azure Databricks is the only one of the three where Databricks is a first-party Azure service. You provision it through the Azure portal, it appears on your Azure bill, and it signs users in with Microsoft Entra ID for SSO and access control out of the box.

The Microsoft ecosystem integration runs deep. Power BI DirectQuery connects to Databricks SQL warehouses natively. Azure Data Factory runs Azure Databricks jobs from its pipelines. ADLS Gen2 is the storage layer for Unity Catalog tables and volumes. Azure Key Vault can back Databricks secret scopes. Microsoft Purview can scan Unity Catalog metadata and lineage.

For an enterprise running Databricks, Power BI, and Purview under a single Azure Enterprise Agreement, the result is one bill, one identity system, and one support relationship. That operational simplicity is the strongest argument for Azure Databricks, and it is why many Microsoft-centric organizations start their Databricks evaluation there.

Databricks on AWS: Maturity and Instance Variety

AWS was the first cloud Databricks launched on, and it has the longest track record. The ecosystem integrates with S3 for storage, IAM for access control, Glue for legacy Hive metastore compatibility (Unity Catalog is the recommended path), and AWS PrivateLink for private front-end and back-end connectivity.

The practical advantage is instance variety. AWS offers a broad catalog of EC2 instance types, including Graviton ARM-based instances, which AWS says have the best price-performance on EC2. Databricks supports them with some exceptions, including Databricks SQL and AWS GovCloud. For teams running large-scale data engineering, the ability to pick the compute profile - memory-optimized for joins, storage-optimized for shuffles, GPU instances for ML training - is a real advantage.

Databricks on AWS can be paid through an AWS Marketplace account, so the charges land on the AWS bill. Whether that spend counts toward an existing AWS commitment depends on the terms of your enterprise agreement with AWS, so confirm it before you size the deal.

Databricks on GCP: BigQuery and Vertex AI Ecosystem

GCP is the newest Databricks deployment. It integrates with Google Cloud Storage, Cloud IAM, Cloud Composer (managed Airflow), and Google's networking, including Private Service Connect for private connectivity.

The draw is proximity to BigQuery. With Lakehouse Federation, teams can run queries in Databricks that join Delta Lake tables with BigQuery datasets without copying the data first. Federation to BigQuery is not exclusive to Google Cloud, but running Databricks on GCP keeps those queries inside one cloud and avoids cross-cloud data transfer. For organizations already invested in BigQuery for SQL analytics, adding Databricks for Spark workloads and ML training creates a complementary stack rather than a replacement.

Vertex AI is the other neighbor. A team that already serves models on Vertex AI can train and track a model with MLflow in Databricks, export it, and deploy it to Vertex AI endpoints - a hand-off the team builds and maintains, not a built-in integration. Databricks Model Serving is the alternative that keeps training and serving of AI models on one platform.

GCP also applies sustained use discounts automatically to eligible machine series such as N1 and N2. There is no need to commit upfront; Google reduces the effective rate as monthly usage of those machines increases. Series such as E2 are not on the eligible list, so check which machine types your clusters use.

Pricing Differences by Cloud

DBU rates are not identical across clouds. On the Premium tier, Databricks list prices for Jobs Compute are $0.15 per DBU on AWS and GCP and $0.30 on Azure, while All-Purpose Compute lists at $0.55 per DBU on all three (October 2026). The Standard tier is retired (AWS and GCP on 1 October 2025, Azure on 1 October 2026), so Premium is the baseline. On top of DBUs, each cloud provider charges for the underlying VMs, storage, and network egress.

VM prices for comparable instance types - AWS m5.xlarge vs. Azure Standard_D4s_v3 vs. GCP n2-standard-4 - vary by region and commitment level, so price the exact instance in each provider's calculator rather than relying on a rule of thumb. Storage works the same way: Amazon S3 Standard lists $0.023 per GB-month for the first 50 TB in US East (N. Virginia), and the equivalent ADLS and GCS tiers should be checked for your region.

The real cost differentiator is billing integration. Azure Databricks DBUs are billed through Azure alongside the infrastructure, and Azure offers Databricks Commit Unit pre-purchase plans with up to 37% savings over pay-as-you-go DBU prices. AWS customers can pay through AWS Marketplace, and Databricks on Google Cloud is subscribed through Google Cloud Marketplace and tied to a Google billing account. GCP's sustained use discounts apply automatically to eligible VMs. The cheapest cloud depends less on list prices and more on which provider your organization already has a commercial relationship with and what that agreement covers.

Network egress costs deserve attention for multi-region or hybrid architectures. Each provider prices cross-region and internet egress differently, and Databricks adds its own networking charges in some cases - on Azure, for example, when serverless workloads connect to customer resources. If your architecture moves large volumes of data between Databricks and other services, model the egress costs explicitly - they can exceed the compute costs for data-intensive workloads.

Feature Rollout and Regional Availability

Feature availability differs by cloud and by region, so do not assume that a capability you saw in one cloud's documentation exists in your region. Each cloud's Databricks documentation keeps a list of features with limited regional availability. On Azure, for example, serverless compute for notebooks, jobs, pipelines, and SQL warehouses is available only in a subset of Azure Databricks regions. Check that list for every capability you depend on before choosing a region.

Regional coverage differs too. As of October 2026, the documentation lists 45 Azure Databricks regions (including two Azure Government and four Azure China regions), 16 commercial AWS regions plus AWS GovCloud, and 15 Google Cloud regions.

For organizations in regulated industries, government cloud support may be decisive. Azure Databricks is in scope for FedRAMP High, DoD IL4 and DoD IL5 (with workload isolation) in Azure Government, although Microsoft's feature-availability page (October 2026) states that the Azure Government regions do not support Databricks SQL or Unity Catalog. Databricks on AWS GovCloud offers separate environments for FedRAMP High and for DoD IL5 requirements. On Google Cloud, the compliance security profile covers C5, HIPAA, HITRUST, PCI-DSS, and TISAX, and Databricks documents no government-cloud deployment there.

Governance Across Clouds with Dawiso

Unity Catalog governs data across the workspaces of a Databricks account, with one metastore per region. It handles access control, lineage, and metadata for Delta tables and ML models, and through Lakehouse Federation it can govern queries to external systems such as Snowflake or BigQuery. But many organizations run Databricks on more than one cloud, which means separate Databricks accounts and metastores, alongside Snowflake, SaaS tools, and on-premises systems. A single business view across those boundaries is where a cross-platform catalog helps.

Dawiso addresses this by bringing the metadata of each connected Databricks account into a single data catalog, next to the other sources you connect. The Databricks connector reads Unity Catalog objects and column-level lineage from the system.access.column_lineage system table. A business glossary term like "monthly active users" can be linked to the Databricks table that computes it, the Snowflake table it is replicated to, and the Power BI report that shows it. Lineage depth depends on the source: column level for Databricks, object level for Power BI, and the Snowflake connector does not read native column lineage.

Through the Model Context Protocol (MCP), the Dawiso MCP server lets AI agents search the catalog, read object details, trace lineage, and preview data samples for the connected sources. An analyst asking an AI assistant "where is our customer churn data?" can get the Databricks table, its description, and the downstream reports that depend on it in one answer.

Conclusion

The Databricks platform is cloud-agnostic by design. The differences between Azure, AWS, and GCP deployments are real but bounded: they come down to identity integration, native service connectors, instance type selection, DBU pricing and billing mechanics, region coverage, and compliance options. Pick the cloud where your organization already operates, and focus your evaluation on the integration points that matter for your specific architecture. For multi-cloud environments, the governance gap is the harder problem - and it is easier to close with a cross-platform catalog alongside Unity Catalog.

Sources

• Microsoft Learn - What is Azure Databricks?
• Microsoft Azure - Azure Databricks: Differentiated synergy
• Microsoft Azure - Azure Databricks pricing
• Databricks - Databricks pricing
• Databricks - End of life for Standard tier workspaces on Databricks on Google Cloud
• Microsoft Learn - End of life for Standard tier workspaces on Azure Databricks
• Microsoft Learn - Transform data with Databricks Job
• Microsoft Learn - Connect to and manage Azure Databricks Unity Catalog in Microsoft Purview
• Databricks - Networking (Databricks on AWS)
• Databricks - Compute configuration reference: AWS Graviton instance types
• Databricks - Manage your subscription and billing (Databricks on AWS)
• Databricks - Networking (Databricks on Google Cloud)
• Databricks - Connect to external databases and catalogs (Lakehouse Federation)
• Google Cloud - Sustained use discounts
• Databricks - Manage your subscription (Databricks on Google Cloud)
• AWS - Amazon S3 pricing
• Microsoft Learn - Networking - Azure Databricks
• Microsoft Learn - Features with limited regional availability - Azure Databricks
• Microsoft Learn - Azure Databricks regions
• Databricks - Databricks clouds and regions (Databricks on AWS)
• Databricks - Databricks clouds and regions (Databricks on Google Cloud)
• Microsoft Learn - Azure Government services by audit scope
• Databricks - Databricks on AWS GovCloud
• Databricks - Compliance (Databricks on Google Cloud)
• Databricks - Create a Unity Catalog metastore
• Dawiso Help - Databricks connector
• Dawiso Help - Power BI connector
• Dawiso Help - Snowflake connector
• Dawiso Help - Dawiso Model Context Protocol (MCP)