A fast-growing startup hits a wall. Data pipelines break, dashboards lag, and the data science team waits hours to train models. Whether you’re running a lean team or managing millions in cloud spend, picking the right platform affects the entire business. And, the debate of Azure Databricks vs. Snowflake fits into that narrative.
For organizations running on Microsoft Azure , the decision often comes down to Azure Databricks vs Snowflake. Both platforms can run on Azure, but they approach data and AI differently. Azure Databricks is a Microsoft first-party service with native integrations across the Azure ecosystem. Snowflake is a multi-cloud platform available on Azure, AWS, and GCP, with its own set of Microsoft integrations.
Here’s the current state: Databricks crossed a $5.4 billion revenue run-rate in early 2026, growing over 65% year-over-year. The company is valued at $134 billion and serves more than 60% of the Fortune 500. In June 2025, Databricks and Microsoft extended their strategic partnership, deepening integrations between Azure Databricks, Azure AI Foundry, and Microsoft Power Platform.
Snowflake generated $3.5 billion in product revenue for fiscal year 2025 and has more than 11,000 customers. Snowflake has also expanded its Microsoft partnership, integrating OpenAI models via Azure AI Foundry and bringing Cortex Agents to Microsoft 365 Copilot and Teams.
This post breaks down the key differences for organizations evaluating these platforms on Azure.
TL;DR Azure Databricks and Snowflake both run on Azure, but they serve different primary use cases. Azure Databricks is a Microsoft first-party service with deep Azure integration, built for data engineering, ML, and complex pipelines. Snowflake is a multi-cloud platform that also runs on Azure, optimized for SQL analytics and BI workloads. For organizations already invested in the Microsoft ecosystem, Azure Databricks often provides tighter integration—but the choice ultimately depends on your workloads.
What is Azure Databricks? Azure Databricks is the Azure-hosted version of the Databricks Data Intelligence Platform. It’s a Microsoft first-party service, jointly developed by Microsoft and Databricks, meaning it’s billed through Azure, integrated with Azure identity and security, and receives Azure-specific features.
Built on Apache Spark, Azure Databricks combines data engineering, machine learning, and analytics in a unified environment. Data sits in Azure storage (Blob Storage or Azure Data Lake Storage) in open formats like Delta Lake or Apache Iceberg.
Key features Native Azure integration Microsoft 365 and SharePoint connectors for data ingestionMicrosoft Entra ID (formerly Azure AD) for authentication and identity managementAzure Key Vault for secrets managementAzure Data Factory for orchestrationPower BI for visualization with optimized connectorsMicrosoft Fabric for interoperability via OneLake shortcutsAzure AI Foundry for AI model integrationPower Automate for workflow automationSource: Azure Blog
Serverless workspaces (GA January 2026)
Lakehouse architecture
AI and ML capabilities Mosaic AI Model Serving supports Anthropic Claude models (Opus 4.5, Haiku 4.5) and OpenAI models as Databricks-hosted foundation modelsAgent Bricks enables building production-grade AI agents with Knowledge Assistant now available across multiple regionsLakebase provides PostgreSQL-compatible OLTP alongside analytical workloadsAI/BI Genie allows natural language queries integrated with Copilot StudioDatabricks One (GA January 2026) provides a simplified interface for business usersExplore how Kanerika’s strategic partnership with Databricks is reshaping data intelligence , unlocking smarter solutions and driving innovation for businesses worldwide.
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Collaborative notebooks Teams work together using notebooks that support Python, SQL, R, and Scala. The Databricks Assistant Agent Mode automates multi-step tasks—retrieving assets, generating code, fixing errors, and visualizing results.
Who uses Azure Databricks? Azure Databricks works well for organizations already invested in Azure who need data engineering, ML workflows, or complex pipelines. It’s widely used across tech, finance, healthcare, and manufacturing—anywhere real-time insights or advanced analytics are critical
What is Snowflake? Snowflake is a cloud-based data platform built to store, process, and analyze large amounts of structured and semi-structured data. It runs entirely on public cloud services like AWS, Azure, and Google Cloud. Known for its simplicity and performance, Snowflake helps businesses handle data without managing complex infrastructure.
Key features Cloud-native data warehouse
SQL-first experience
Azure integrations
Azure Blob Storage and ADLS for external stagesPower BI with native connectorsAzure Functions for external functions and API integrationMicrosoft Entra ID for authenticationAzure OpenAI Service in Cortex AI via Azure AI FoundryMicrosoft 365 Copilot and Teams integration for Cortex Agents (preview, Azure US East 2)OneLake interoperability through Apache Iceberg supportAI and ML capabilities (Cortex AI) Snowflake has substantially expanded its AI offering:
Cortex Analyst : Natural language to SQL with high accuracyCortex Search : RAG capabilities over unstructured dataCortex Code : AI coding agent for data engineering (launched February 2026)Snowflake Intelligence : Agentic AI framework for conversational analyticsModel access : OpenAI (via Azure AI Foundry), Anthropic, DeepSeek, Meta, Mistral, and Snowflake ArcticMore than 7,300 customers now use Snowflake’s AI and ML technology weekly.
Secure data sharing
Who uses Snowflake? Snowflake is popular with analysts, BI teams, and data engineers focused on SQL analytics and reporting. Organizations that need multi-cloud flexibility or want a simpler managed experience often choose Snowflake over Azure-native options.
Who Uses Snowflake? Snowflake is ideal for analysts, business intelligence teams, and data engineers who focus on structured data and reporting. It’s widely used in retail, media, finance, and other industries that depend on fast, reliable analytics.
Azure Databricks vs Snowflake: What Are the Major Differences? 1. Platform positioning on Azure The differentiator in the Azure Databricks vs Snowflake debate is platform positioning.
Azure Databricks is a Microsoft first-party service. It’s billed through Azure, appears in the Azure portal, integrates with Azure RBAC, and receives joint engineering investment from Microsoft and Databricks. This matters for enterprises with Azure Enterprise Agreements, existing Azure credits, or strict procurement requirements.
Snowflake runs on Azure but is billed separately through Snowflake. It’s a multi-cloud platform—the same Snowflake experience across Azure, AWS, and GCP. This matters for organizations running across multiple clouds or who want vendor independence.
2. Architecture Comparison The fundamental difference between these platforms lies in their architectural philosophy. Databricks follows the lakehouse architecture, which brings data management capabilities like data cataloging to data lakes , while Snowflake replaces legacy data warehouses and supports ELT processing.
Azure Databricks operates on a lakehouse model that combines the flexibility of data lakes with the structure of data warehouses. It uses the open-source Apache Spark framework to create data lakehouses, allowing you to store both structured and unstructured data in one location. This approach eliminates the need for separate systems and reduces data movement.
Snowflake , on the other hand, maintains a traditional data warehouse architecture but with cloud-native enhancements. Snowflake now supports data lakes by allowing data teams to work with a variety of data types, including semi-structured and unstructured data. However, it still requires you to load data into its proprietary format before analysis.
Key Architectural Differences:
Storage approach : Databricks stores data in open formats (Delta Lake), while Snowflake uses proprietary storageProcessing engine : Databricks uses Apache Spark for distributed processing, Snowflake uses its own SQL engineData organization : Databricks maintains raw data in place, Snowflake requires data ingestion and transformation3. Microsoft ecosystem integration The differentiator in the Azure Databricks vs Snowflake lies in the depth they merge with the Microsoft ecosystem.
Azure Databricks offers tighter native integration:
Genie spaces connect directly to Azure AI Foundry and Copilot Studio Power Automate can trigger and interact with Databricks jobs SharePoint connector ingests data directly Microsoft Entra ID groups can share Unity Catalog assets Serverless compute uses Azure Network Security Perimeter Azure-specific compliance (CCCS Medium/Protected B, TISAX) Snowflake has expanded Microsoft integration:
Cortex Agents work in Microsoft 365 Copilot and Teams (preview) OpenAI models available via Azure AI Foundry integration OneLake interoperability through Iceberg Power BI connectivity with native connectors Azure Blob Storage for external stages For organizations deeply embedded in Microsoft 365 and Azure, Azure Databricks generally offers more seamless integration. Snowflake has caught up in key areas (especially AI model access), but some integrations are still in preview.
4. Data processing and analytics Azure Databricks handles large-scale ETL, streaming, and complex transformations well. Spark distributes processing across clusters. Structured Streaming provides native real-time capabilities. Lakeflow Declarative Pipelines (formerly Delta Live Tables) offers managed ETL with autoscaling.
Snowflake performs strongly for SQL analytics and BI reporting. High-concurrency scenarios—hundreds of analysts running dashboards—are a strength. Snowpipe Streaming has improved real-time capabilities, but Snowflake still relies more on batch-oriented ELT.
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5. Machine learning and AI The biggest differentiator in the Azure Databricks vs Snowflake debate is in the AI/ML domain. Though, Snowflake is closer to bridging the gap.
Azure Databricks provides comprehensive ML:
MLflow for experiment tracking, model versioning, deployment Native support for TensorFlow, PyTorch, scikit-learn Serverless GPU compute for training and fine-tuning Agent Bricks for building AI agents Model serving for real-time inference Foundation models (OpenAI, Anthropic) accessible via Mosaic AI Snowflake offers accessible AI via Cortex:
Pre-built LLM access (OpenAI via Azure, Anthropic, Mistral, etc.) Cortex Analyst for natural language SQL Cortex Code for automated data engineering Snowflake ML for common model training scenarios Snowpark for custom Python/Scala code The difference: Azure Databricks lets you train custom models, fine-tune foundation models, and deploy arbitrary ML code. Snowflake primarily runs managed models and provides easier access for non-ML teams.
6. SQL and business intelligence Snowflake uses standard SQL. The learning curve is minimal. BI tool integration—Tableau, Power BI, Looker—is mature with native optimizations. Dynamic Tables provide declarative incremental transformations without procedural code.
Azure Databricks supports SQL through Databricks SQL. It’s built on Spark SQL, which occasionally requires different syntax. The serverless SQL warehouses are competitive for BI workloads, and AI/BI dashboards with Genie provide natural language querying. However, the platform’s DNA favors data engineers.
For SQL-centric teams, Snowflake remains easier to adopt.
7. Pricing Azure Databricks uses a Databricks Unit (DBU) model. Costs vary by workload type—jobs compute is cheaper than interactive or serverless SQL. You also pay Azure separately for VMs, storage, and networking. This dual billing makes cost estimation harder but allows Azure credits and enterprise discounts to apply.
Snowflake uses a credit-based model ($1.50-4.00 per credit depending on edition and commitment). Storage costs around $23/TB/month. Pricing is more predictable for steady SQL workloads but managed entirely outside Azure billing.
Aspect Azure Databricks Snowflake Billing Azure + DBU Snowflake credits Azure credits apply Yes No Cost predictability Lower (dual billing) Higher
8. Governance and compliance Both offer enterprise-grade security.
Azure Databricks :
Unity Catalog for centralized governance, lineage, audit Microsoft Entra ID integration Azure Network Security Perimeter support HIPAA, SOC 2, GDPR, FedRAMP, CCCS Medium, TISAX compliance Delta Sharing for cross-platform data sharing Snowflake :
Native RBAC, dynamic data masking, row access policies Zero-copy data sharing (within Snowflake ecosystem) Data Clean Rooms for multi-party analytics Horizon Catalog for governance PCI DSS, FedRAMP, ISO, HIPAA compliance Snowflake’s data sharing within its ecosystem remains a strength. Azure Databricks offers more flexible cross-platform sharing via Delta Sharing.
When to choose Azure Databricks You’re already invested in Azure
Machine learning and data science
Complex data engineering
Microsoft ecosystem depth
When to choose Snowflake SQL analytics and BI
Multi-cloud requirements
Simpler managed experience
Data sharing and collaboration
Many organizations use Azure Databricks and Snowflake together. Azure Databricks handles data preparation, ML, and complex pipelines; Snowflake serves analytics, BI, and governed reporting.
Apache Iceberg support on both platforms—plus OneLake interoperability—makes this pattern more practical. Data can flow between systems without duplication when using open formats.
The decision doesn’t have to be either/or. Consider your primary workloads, team skills, and Azure investment level.
Kanerika: Azure data and AI consulting Kanerika helps businesses build modern data platforms on Azure using AI, machine learning, and strong data governance . We work with organizations across manufacturing, retail, finance, and healthcare to implement data solutions that drive decisions.
As a Databricks partner and Microsoft Solutions Partner for Data & AI, we help enterprises evaluate platforms, migrate data, and build ML pipelines on Azure. Whether you’re choosing between Azure Databricks and Snowflake or implementing both, our team can guide you through the process.
FAQs What's the difference between Azure Databricks and regular Databricks?What's the difference between Azure Databricks and regular Databricks? Azure Databricks is the Azure-hosted version of Databricks, jointly developed with Microsoft. It’s a first-party Azure service with native integrations (Entra ID, Key Vault, Azure portal), billed through Azure, and eligible for Azure credits. The core Databricks features are the same across clouds, but Azure Databricks has Azure-specific optimizations and compliance certifications.
Can I use both Azure Databricks and Snowflake together? Yes. Many enterprises use Azure Databricks for data engineering and ML, then serve analytics from Snowflake. Apache Iceberg support and OneLake interoperability make data sharing between platforms practical.
Which platform is better for Power BI? Both integrate with Power BI. Snowflake has mature, optimized connectors that work well for standard BI. Azure Databricks also supports Power BI with optimized connectivity, plus deeper integration with the broader Microsoft ecosystem (Copilot Studio, Power Automate). For pure BI, either works; for end-to-end Microsoft stack, Azure Databricks may fit better.
Is Snowflake a first-party Azure service? No. Snowflake runs on Azure infrastructure but is billed and managed through Snowflake. Azure Databricks is a first-party service, meaning it’s billed through Azure and integrated into the Azure portal.
Which is more cost-effective on Azure? It depends on workloads. Azure Databricks allows Azure credits and enterprise discounts to apply, but has dual billing (Azure + DBUs). Snowflake has simpler, more predictable pricing for SQL workloads but can’t use Azure credits. Run representative workloads to compare.
How do the AI capabilities compare in 2026? Azure Databricks offers more flexibility—custom model training, MLflow, serverless GPUs, and Agent Bricks. Snowflake provides easier access to pre-built models via Cortex AI, including OpenAI through Azure AI Foundry. Azure Databricks suits ML teams; Snowflake suits business users who want AI without deep ML expertise.ShareArtifactsDownload allAzure databricks vs snowflake 2026Document · MD Databricks vs snowflake 2026 correctedDocument · MD
Is Databricks Azure or AWS? Databricks isn’t tied exclusively to Azure or AWS. It’s a lakehouse platform that operates across multiple cloud providers, including Azure, AWS, and GCP. You choose your preferred cloud environment when setting up your Databricks workspace. Think of it as a software application, not a cloud provider itself.
