DataComparison
Snowflake and Databricks are the two dominant enterprise data platforms — and their rivalry defines the modern data stack debate. Snowflake started as the premier cloud data warehouse and has expanded into AI and data applications. Databricks started as the Apache Spark and ML platform and has expanded into SQL analytics and data warehousing. Today they compete across virtually every data use case.
Metric
8.6
Overall Score
8.5
Overall Score
7-Dimension Scorecard
Core Capability
9.5
Best enterprise data warehouse — separated compute and storage, near-infinite scalability, and multi-cloud support are industry-defining.
9.5
Industry-leading unified analytics — Spark-based data engineering, collaborative notebooks, MLflow, and Delta Lake form the best integrated data + ML platform.
Ease of Use
8.0Better
SQL-standard interface. Good Snowsight web UI. VirtualWarehouses concept takes some learning.
6.5
Requires data engineering expertise. Not accessible to non-technical users. Complex to configure for first-timers.
Integrations
9.5Better
Integrates with virtually every data tool. Data Marketplace for cross-organization data sharing is unique.
9.0
Deep integrations with all major cloud providers, data warehouses, BI tools, and ML frameworks.
Pricing Value
6.5Better
Credit-based pricing. Enterprise spends typically $50K–$500K+/year. Expensive but justified for the scale.
6.0
DBU (Databricks Unit) based pricing can be expensive and opaque. Most enterprise teams spend $50K–$500K+ annually.
AI Sophistication
8.0
Cortex AI for ML models and LLM features directly in the warehouse. Snowpark for Python/ML workloads.
9.5Better
Mosaic AI, MLflow, Vector Search, and LLM fine-tuning make Databricks the enterprise AI development platform of choice.
Community & Support
9.0
Strong community, Snowflake Summit conference, extensive partner ecosystem.
9.0
Massive community, extensive documentation, Databricks Academy, and annual Data + AI Summit.
Scalability
10.0
Petabyte-scale by design. Compute scales instantly up or down without migration.
10.0
Virtually unlimited scale — designed for petabyte-scale data processing and enterprise AI development.
Verdict
Snowflake wins for analytics engineering teams that need the best data warehouse with strong SQL tooling, data sharing, and broad BI integration. Databricks wins for data science and ML teams that need integrated notebook-based development, MLflow for model management, and large-scale data engineering with Spark.
Choose Snowflake
Use Snowflake if your primary use case is analytics engineering, SQL-based reporting, and data sharing — Snowflake's query performance, data marketplace, and BI tool integrations are best-in-class.
Full ScorecardChoose Databricks
Use Databricks if your team does data engineering + machine learning + AI model development — the unified platform with Spark, MLflow, and Mosaic AI is unmatched for data science workflows.
Full ScorecardPricing
Snowflake
StandardFrom $2/credit (pay-as-you-go)
EnterpriseCustom — typically $50K+/year
Databricks
Pay-as-you-goDBU-based (from ~$0.07/DBU)
EnterpriseCustom — typically $50K+/year
Frequently Asked Questions
Can you run dbt on both Snowflake and Databricks?
Yes — dbt has excellent native connectors for both Snowflake and Databricks. Many companies run Fivetran → dbt → Snowflake or Fivetran → dbt → Databricks SQL as their analytics stack. dbt works equally well with both warehouses.
Which is cheaper?
Pricing is complex and use-case dependent. For pure SQL analytics, Snowflake's credit pricing is often more predictable. For large-scale Spark jobs, Databricks' DBU pricing can be cheaper than running equivalent workloads on Snowflake. Most enterprises spend comparably on both at scale.
Are Snowflake and Databricks converging?
Yes — Snowflake has added Snowpark for Python and Cortex AI for ML. Databricks added Databricks SQL for analytics. Both are building toward the same unified data + AI platform. The distinction between warehouse and lakehouse is blurring significantly.
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