Snowflake to Databricks Migration: Real Costs and Trade-offs Teams Must Know

Migrating from Snowflake to Databricks is often justified on cost savings, but teams that end up satisfied typically moved to consolidate ML, streaming, and GenAI workloads alongside analytics data. The two platforms differ significantly beneath the surface, particularly in how storage is handled — on Databricks, storage costs appear on the cloud provider's bill rather than the platform bill, a distinction finance teams must understand upfront. Migration approaches range from a quick lift-and-shift to a full re-architecture, and choosing the wrong strategy can eliminate any expected savings, since Snowflake-shaped tables on Databricks compute carry similar costs. While moving the data itself is relatively straightforward using bulk Parquet exports and Delta Lake, translating the code — especially complex stored procedures — is where timelines and budgets tend to overrun. Automated SQL conversion tools can handle standard queries, but the harder edge cases require significant manual engineering effort that is often underestimated at the planning stage.