As enterprises increasingly integrate AI-driven automation and data platforms with Salesforce, understanding about the right underlying data infrastructure becomes crucial for the overall Enterprise Architecture. Snowflake and Databricks are two leading platforms, each offering unique advantages for AI, data engineering, and analytics. We at Megnity based on our experience and expertise, provide an unbiased comprehensive point of view outlining when to use Snowflake versus Databricks for Salesforce integration, backed by real-world case studies and industry expert perspectives. 

Enhancing AI and Data Capabilities in Salesforce 

The Role of Snowflake and Databricks 

• Snowflake excels in structured data storage, high-performance analytics, and seamless SQL-based querying. 

• Databricks is optimized for large-scale AI/ML workloads, data lake architecture, and real-time data processing. 
  
  

How Integration Enhances AI Agents and Vertical AI in Salesforce 

• AI Agents & Agentic AI: Both platforms enhance AI-driven automation, but Databricks provides deeper ML capabilities. 

• Vertical AI Agents: Snowflake’s superior data governance and native connectivity to BI tools make it ideal for industry-specific AI applications. 
  
  

Feature-by-Feature Comparison 

Feature	Snowflake	Databricks
Pricing Model	Compute + Storage (separate charges)	DBU-based (compute + storage combined)
ETL Workload Costs	Higher (up to 9x more expensive)	Lower (optimized for real-time ETL)
Real-Time Analytics	Expensive for streaming workloads	Cost-efficient with Delta Lake
Operational Overhead	Low (simple setup)	Higher (requires expertise)
Scalability	Scales well for structured data	Scales efficiently across all data types
AI/ML Workloads	Limited support; higher cost	Optimized; cost-effective
Ease of Use	High (user-friendly SaaS platform)	Moderate (requires technical expertise)
Primary Focus	SQL-based analytics and BI	AI/ML workloads and real-time analytics
Data Sharing	Built-in Data Sharing	Delta Sharing (open-source protocol)
Scaling	Automatic scaling	Requires optimization
Cost Efficiency	Better for batch processing and BI	Better for real-time AI/ML workloads
Learning Curve	Low	High

Megnity Experiences 

Case Study 1: Snowflake for Salesforce AI-Driven Analytics 

A global financial services firm leveraged Snowflake for predictive customer analytics in Salesforce, reducing query times by 60% and improving decision-making. 

Case Study 2: Databricks for AI Model Training in Salesforce 

A healthcare provider used Databricks for real-time patient risk assessment AI models, integrating seamlessly with Salesforce’s Health Cloud. 

Case Study 3: Databricks for AI Model Training in Salesforce 

A retailer integrated Salesforce Sales and Marketing Clouds with Databricks to build a product recommendation model. By leveraging Customer 360 data, the model predicted individual customer preferences, improving conversion rates and customer satisfaction. 

Salesforce Integration: Databricks vs. Snowflake 

Feature	Salesforce-Databricks	Salesforce-Snowflake
Purpose	AI, advanced analytics, ML workloads	Scalable data warehousing, real-time analytics
Architecture	Zero-ETL data federation	ETL tools or connectors like Salesforce Connect
Data Management	Unified data access and governance	Centralized data storage and governance
Analytics	AI-driven predictions, real-time personalization	Scalable analytics and customer insights
Scalability	Optimized for big data and AI workloads	Elastic scalability for growing data volumes
Integration Complexity	Simplified zero-ETL integration	Requires ETL tools, increasing complexity
Use Cases	Hyper-personalization, predictive lead scoring, AI-driven customer support	Sales analytics, customer 360 view, real-time reporting

Salesforce Integration Use Cases (Example Scenario) 

Use Case	Snowflake Cost Drivers	Databricks Cost Drivers
Real-time CDP Analytics	Compute credits for continuous pipelines	DBU costs for streaming clusters
Batch ML Training	Storage costs for historical data	GPU cluster costs + ML runtime fees
AI Agent Deployment	Third-party ML platform costs	Native model serving infrastructure
Total Cost	Averages 15-20% lower for pure analytics	30% cost advantage in ML workloads

Estimated Pricing Breakdown (Example Scenario) 

Workload	Snowflake Estimated Cost	Databricks Estimated Cost
ETL Processing	$9 per query	$1 per query
BI Reporting	$5 per query	$7 per query
AI Model Training	$20 per model	$10 per model
Real-Time Streaming	Expensive	Cost-efficient

While both Snowflake and Databricks enhance Salesforce capabilities, they serve different purposes: 

• Snowflake focuses on scalable data warehousing and real-time analytics with traditional ETL methods.  

• Databricks specializes in AI-driven insights, advanced analytics, and zero-ETL simplicity. 
  
  

According to Industry Analysts, 78% of AI-first enterprises prefer Databricks for agentic AI development, while 62% of CRM-centric organizations standardized on Snowflake. 

Megnity Recommendation 

Optimal platform depends on project requirements and team expertise: 

Choose Snowflake If: 

• Organizations prioritizing structured data analytics, reporting, and BI. 

• Enterprises seeking tight Salesforce integration for data warehousing. 

• The use case prioritizes SQL-based BI, reporting, or lightweight analytics. 

• Companies requiring low learning curve and SQL-based data manipulation. 

• Teams lack Spark/Python expertise but require quick time-to-insight. 

• Pre-built SaaS integrations and external data sourcing are critical. 
  
  

Choose Databricks If: 

• Companies focusing on large-scale AI/ML and unstructured data processing. 

• Organizations requiring real-time data processing and complex transformations. 

• Businesses needing advanced analytics and AI-driven automation. 

• Projects involve advanced ML, unstructured data, or real-time streaming. 

• Technical teams can manage Spark clusters and prefer open-source flexibility. 

• Unified governance across data lakes and warehouses is essential. 
  
  

Hybrid Approach: 

• Leverage the Databricks-Snowflake connector to combine strengths—use Snowflake for BI and Databricks for AI. This architecture suits organizations transitioning from warehouses to lakehouses.
  
   

Organizations should align platform choice with their AI maturity and based on data strategy, expertise, and long-term goals. Snowflake for data-first CRM enhancement, Databricks for building intelligent agent systems. For hybrid needs, the Databricks-Snowflake connector can offer the best of both worlds.