- Both platforms now cover warehousing, data engineering and AI; the differences are in heritage, strengths and operating model.
- Choose based on dominant workloads, team skills and governance needs, not feature checklists.
- Many large enterprises run both; the key is a clear division of labor and shared governance.
Two platforms converging
Snowflake began as a cloud data warehouse and expanded into data engineering, applications and AI. Databricks began with Apache Spark and data science and built a lakehouse with SQL warehousing. Today both offer governed storage on open table formats, SQL analytics, Python, ML and generative AI capabilities. The overlap is substantial; the differences are in emphasis.
How they compare
| Dimension | Snowflake | Databricks |
|---|---|---|
| Heritage | SQL data warehouse | Spark, data engineering and ML |
| Sweet spot | SQL analytics, data sharing, governed self-service BI | Large-scale engineering, streaming, ML and AI engineering |
| Primary users | Analysts and analytics engineers | Data engineers and data scientists |
| Governance | Horizon governance features | Unity Catalog |
| Cost model | Consumption credits by warehouse size and time | Consumption (DBUs) plus underlying cloud compute |
Both platforms evolve quickly. Validate current capabilities and pricing against your workloads during evaluation.
How to decide
- Profile your workloads: the share of BI and SQL versus engineering, streaming and ML.
- Assess your team: SQL-first or Python- and Spark-first?
- Run a proof of value: two or three representative workloads on each, measuring performance, cost and developer experience.
- Model total cost: include compute, storage, egress, tooling and people.
- Decide on governance: where policies, lineage and access will be managed if you run both.
When to use both
Running both is common in large enterprises, typically with Databricks for heavy engineering and ML and Snowflake for governed analytics and data sharing. It works when open table formats, a shared catalog strategy and clear ownership prevent duplicated data and costs.
Frequently asked questions
Is Snowflake or Databricks better for AI?
Both support enterprise AI. Databricks has deeper roots in ML engineering and model training; Snowflake emphasizes AI over governed data with simple SQL access. The better choice depends on your use cases and team.
Can we migrate from one to the other?
Yes, and open table formats like Apache Iceberg and Delta Lake make coexistence and migration easier than in the past.