Databricks vs Snowflake: Complete Pricing and Feature Comparison
The defining data platform comparison. Databricks is a unified analytics platform built on Apache Spark. Snowflake is a cloud data warehouse optimized for SQL. Different architectures, different pricing models, different strengths.
Quick Verdict
Choose Databricks for data engineering + ML workloads. Choose Snowflake for SQL analytics + BI. Many enterprise organizations use both.
Pricing Model Comparison
Databricks uses DBUs (Databricks Units) priced from $0.07 to $0.70 per DBU depending on workload type, plus separate cloud infrastructure costs. Snowflake uses credits priced from $2 to $4 per credit depending on edition (Standard, Enterprise, Business Critical). One Snowflake credit equals roughly one hour of an X-Small warehouse.
The fundamental difference: Snowflake bundles compute into its credit price. Databricks separates platform cost from infrastructure cost. Snowflake is easier to budget. Databricks is more transparent about where money goes and offers more optimization levers.
| Dimension | Databricks | Snowflake |
|---|---|---|
| Pricing unit | DBU ($0.07-$0.70) | Credit ($2-$4) |
| Billing structure | Two bills (platform + cloud) | One bill (bundled) |
| Compute management | You manage clusters | Fully managed warehouses |
| Spot/preemptible savings | 60-70% cloud savings | Not available |
| Instance type control | Full control | No control (abstracted) |
| Storage pricing | Cloud provider rates | $23-$40/TB/mo (compressed) |
| Committed discounts | 20-40% (1-3 year) | 25-35% (1-3 year) |
| Cost predictability | Lower (variable) | Higher (credits are fixed) |
Cost Comparison by Workload
| Workload | Databricks Est. | Snowflake Est. | Better Value |
|---|---|---|---|
| ETL pipelines (daily, 8hr) | $400-$800/mo | $800-$1,500/mo | Databricks |
| SQL analytics (10 users) | $2,000-$4,000/mo | $1,500-$3,500/mo | Comparable |
| ML model training | $3,000-$12,000/mo | Not practical | Databricks |
| Real-time streaming | $500-$2,000/mo | Limited (Snowpipe) | Databricks |
| BI dashboard queries | $1,000-$3,000/mo | $800-$2,500/mo | Snowflake |
| Data sharing (cross-org) | Delta Sharing (free) | Native (free) | Snowflake |
Feature Comparison Matrix
| Feature | Databricks | Snowflake |
|---|---|---|
| SQL analytics | Good | Excellent |
| Data engineering (ETL) | Excellent | Limited |
| Machine learning | Excellent | Basic (Snowpark ML) |
| Real-time streaming | Excellent | Limited (Snowpipe) |
| Notebook experience | Excellent | Basic (Snowsight) |
| BI tool integration | Good (JDBC/ODBC) | Excellent (native) |
| Data sharing | Delta Sharing | Excellent (native) |
| Data marketplace | Limited | Snowflake Marketplace |
| Multi-cloud | AWS, Azure, GCP | AWS, Azure, GCP |
| Governance | Unity Catalog | Native + Horizon |
| Semi-structured data | Good (JSON, Avro) | Excellent (VARIANT) |
| Geospatial | Limited | Good |
| Time travel | Delta Lake (30 days) | Up to 90 days |
| Serverless option | SQL + Compute | Always serverless |
| Infrastructure control | Full (instance types, spot) | None (abstracted) |
| Open source foundation | Apache Spark, Delta Lake | Proprietary |
| Python/Scala support | Native | Snowpark (limited) |
| CI/CD integration | Databricks Asset Bundles | Snowflake CLI, dbt |
| Job scheduling | Workflows (native) | Tasks (basic) |
| Cost optimization levers | Many (spot, sizing, etc.) | Few (warehouse size) |
Where Databricks Wins
- Data engineering. Spark-based ETL, Delta Live Tables, and Structured Streaming are purpose-built for data engineering at scale. Jobs Compute at $0.15/DBU with spot instances makes production pipelines cost-effective.
- Machine learning. GPU clusters, MLflow, model serving endpoints, and feature stores. Training a deep learning model on Snowflake is not practical. If ML is core to your business, Databricks is the clear choice.
- Infrastructure control. Choose instance types, use spot instances (60-70% savings), configure auto-scaling, and optimize at the hardware level. More operational work, but more cost optimization opportunities.
- Open source. Built on Apache Spark and Delta Lake. No vendor lock-in on the data format. Your Delta tables are Parquet files on your cloud storage, readable by any engine.
Where Snowflake Wins
- SQL analytics. Built from the ground up as a SQL warehouse. Query optimization, concurrency handling, and BI tool integration are industry-leading. Business analysts connect and query with zero friction.
- Simplicity. No clusters, no instance types, no infrastructure. Create a warehouse, set the size, query. For teams without data engineering staff, Snowflake is dramatically simpler to operate.
- Data sharing. Snowflake Data Marketplace and cross-account sharing are unmatched. Share live data between organizations without copying. No Databricks feature competes here.
- Cost predictability. Credit-based pricing bundles compute into one number. Buy credits, monitor consumption, know your costs. Databricks two-layer pricing requires monitoring DBUs and cloud spend separately.
Annual Cost Benchmarks
Industry survey data and published benchmarks suggest median annual Databricks spend of approximately $36,000 per year for mid-size deployments, versus approximately $28,000 for comparable Snowflake deployments. However, these numbers come with significant caveats:
Median Databricks Annual Spend
~$36,000/yr
Higher because Databricks users tend to run more compute-intensive workloads (ETL, ML)
Median Snowflake Annual Spend
~$28,000/yr
Lower because Snowflake users tend to run SQL-only workloads with less total compute
Sources: Databricks and Snowflake customer surveys, industry analyst reports. Median figures. Actual costs vary wildly based on workload mix, team size, and optimization level. These numbers are not directly comparable because the platforms serve different primary use cases.