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, Active Directory 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 identical regardless of cloud. A PySpark job developed on Azure will run on AWS with minor config changes (swap ADLS paths for S3 paths, swap AAD tokens for IAM roles). Teams running multi-cloud or considering migration can rely on this portability — the Databricks platform layer abstracts the cloud-specific storage and compute differences underneath.

Azure Databricks: First-Party Microsoft Integration

Azure Databricks is the only cloud where Databricks is a first-party native service. You provision it through the Azure portal, it appears on your Azure bill, and it integrates with Azure Active Directory for SSO and role-based access control out of the box.

The Microsoft ecosystem integration runs deep. Power BI DirectQuery connects to Databricks SQL warehouses natively. Azure Data Factory orchestrates dbt and Spark jobs. ADLS Gen2 serves as the default storage layer with fine-grained ACLs. Azure Key Vault manages secrets. Microsoft Purview provides catalog metadata — though with scope limited to the Azure ecosystem.

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's why most Microsoft-heavy shops don't seriously evaluate the other clouds for their Databricks deployment.

Databricks on AWS: Maturity and Instance Variety

AWS was the first cloud Databricks launched on, and it remains the most battle-tested deployment. The ecosystem integrates with S3 for storage, IAM for access control, Glue for metastore compatibility (though Unity Catalog is replacing this), and AWS PrivateLink for secure network connectivity.

The practical advantage is instance variety. AWS offers the widest selection of EC2 instance types, including Graviton ARM-based instances that deliver roughly 20% compute cost savings on Spark workloads compared to equivalent x86 instances. For teams running large-scale data engineering, the ability to pick exactly the right compute profile — memory-optimized for joins, storage-optimized for shuffles, GPU instances for ML training — gives AWS a meaningful edge.

AWS Databricks is available through the AWS Marketplace, which means consumption can count against existing AWS commit spend. For organizations with large AWS enterprise discount programs, this can make Databricks effectively cheaper on AWS than the same DBU rate on Azure or GCP.

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 infrastructure.

The differentiator is BigQuery federation. Teams can run queries in Databricks that join Delta Lake tables with BigQuery datasets without moving data between systems. 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 integration connects Databricks-trained models to Google's inference infrastructure. A team can train a model in Databricks MLflow, export it, and deploy it to Vertex AI endpoints for serving — keeping training in the Databricks ecosystem and inference in Google's managed platform.

GCP also applies sustained-use discounts automatically. There's no need to purchase reserved instances upfront; Google reduces the per-hour rate as cumulative monthly usage increases. This benefits workloads with variable but consistent usage patterns.

Pricing Differences by Cloud

Databricks charges the same DBU rates across all three clouds. The cost difference comes from the underlying VM prices, storage costs, and network egress fees charged by each cloud provider.

For equivalent instance types — AWS m5.xlarge vs. Azure Standard_D4s_v3 vs. GCP n2-standard-4 — the price difference is typically 5-15%, varying by region and commitment level. Storage costs across S3, ADLS, and GCS are within a few percent of each other for standard tiers.

The real cost differentiator is billing integration. Azure customers with Enterprise Agreements get negotiated rates that apply to both Azure infrastructure and Databricks DBUs. AWS customers can route Databricks through the Marketplace to draw down existing committed spend. GCP's sustained-use discounts apply automatically without upfront commitment. The cheapest cloud depends less on list prices and more on which provider your organization already has a commercial relationship with.

Network egress costs deserve attention for multi-region or hybrid architectures. GCP tends to be cheapest for intra-region traffic. AWS and Azure charge similar rates for cross-region and internet egress. 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

Historically, AWS gets new Databricks features first, Azure follows within weeks to months, and GCP trails further behind. Unity Catalog, serverless compute, and Model Serving all launched on AWS before reaching the other clouds. The gap has narrowed, but teams deploying on GCP should check the Databricks feature availability matrix for any capability they depend on.

Regional coverage follows a similar pattern. AWS offers Databricks in the most regions globally. Azure has strong coverage in markets with significant Microsoft enterprise presence, including government clouds (Azure Government, Azure China). GCP's Databricks regions are fewer but growing steadily.

For organizations in regulated industries, government cloud support may be decisive. Azure Government and AWS GovCloud both support Databricks for FedRAMP and DoD workloads. GCP does not currently offer an equivalent government-certified Databricks deployment.

Governance Across Clouds with Dawiso

Unity Catalog governs data within a single Databricks workspace or account. It handles access control, lineage, and metadata for Delta tables and ML models. But many organizations run Databricks on multiple clouds — or alongside Snowflake, SaaS tools, and on-premises systems. Unity Catalog does not extend to those boundaries.

Dawiso addresses this by indexing metadata from Databricks workspaces on any cloud into a single data catalog. A business glossary term like "monthly active users" maps to its definition in the Azure Databricks workspace, the Snowflake table it's replicated to, and the Power BI dashboard that visualizes it. Lineage traces the full path across platforms, not just within one Databricks account.

Through the Model Context Protocol (MCP), AI agents can discover data assets regardless of which cloud they reside on. An analyst asking an AI copilot "where is our customer churn data?" gets a single answer that includes the Databricks table, its cloud location, its freshness status, and which downstream reports depend on it — whether the data lives on Azure, AWS, or GCP.

Conclusion

The Databricks platform is cloud-agnostic by design. The differences between Azure, AWS, and GCP deployments are real but narrow: they come down to identity integration, native service connectors, instance type selection, and billing mechanics. 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 requires a cross-platform catalog that Unity Catalog alone does not provide.