How to switch from Snowflake to Databricks

Why teams switch from Snowflake to Databricks

Teams switch from Snowflake to Databricks mainly for one reason: they've outgrown SQL analytics alone and now run Spark data engineering and machine learning against the same data. Both platforms bill on usage rather than seats, so the move isn't about escaping consumption pricing. It's about which billing model and which tooling fits the workload you actually run.

The pricing model is more similar than people expect

Snowflake charges separately for compute, billed per second in Snowflake credits, and storage, billed per TB per month on compressed data. Databricks charges by Databricks Units (DBUs) consumed per second, on top of the underlying cloud provider's own compute and storage bill. Neither company publishes a flat per-credit or per-DBU dollar rate on its main pricing page. Both point you to an interactive calculator or a sales quote, and both rates vary by cloud provider, region, and workload type.

The real difference shows up in what that usage buys. Snowflake's editions (Standard, Enterprise, Business Critical, Virtual Private Snowflake) mainly gate security and governance features, not raw compute capability. Databricks doesn't publish tiers at all. Its cost structure depends more on workload type, since Jobs Compute, SQL Serverless, and Model Serving are metered differently. Third-party spend-benchmarking data puts the median Databricks buyer at roughly $300,000 a year, with SMB averages closer to $190,000 and enterprise averages near $580,000. Snowflake doesn't have an equivalent published benchmark in our data, so treat any dollar comparison as a starting point for your own calculator estimate, not a quote.

One real difference: Databricks Free Edition is free forever for notebooks, SQL, Genie BI, ML, and Lakeflow ETL, built for learning rather than production. Snowflake has no permanent free tier at all, only a 30-day trial with $400 in credits. Databricks also offers a 14-day trial of the full paid platform.

What actually has to move

A Snowflake-to-Databricks migration isn't one project. It's three, layered on top of each other: SQL and BI, data pipelines, and ML/AI, and each one migrates differently. Teams that treat it as a single lift-and-shift usually underestimate the pipeline rewrite and overestimate how much the SQL layer needs to change.

SQL and warehouse workloads

If your Snowflake usage is mostly SQL analytics and BI, Databricks SQL Serverless warehouses are the landing spot. Both platforms separate storage from compute and can auto-suspend idle capacity, so the operating model for BI teams doesn't change as much as the query syntax does. Expect to rewrite Snowflake-specific SQL (its Snowpark and native functions) against Databricks SQL, and to repoint whatever BI tool sits on top, whether that's Tableau, Looker, or Power BI, at the new warehouse endpoint. If you're not sure your workload actually needs a lakehouse, the Snowflake pricing and feature breakdown is worth rereading first, since a lot of pure-SQL teams never outgrow it.

Data pipelines

Databricks pipelines run on Lakeflow for ingestion and ETL, built on Apache Spark, rather than Snowflake's Snowpark for Python, Java, and Scala. If your pipelines are already dbt models, dbt itself runs against both platforms, so that layer often survives the move mostly intact. Orchestration tools like Apache Airflow also work with both. What doesn't carry over directly are Snowflake-specific features: Time Travel, zero-copy cloning, and Snowflake Marketplace listings have no one-to-one Databricks equivalent, even though Databricks has its own versioning through Delta Lake.

ML and AI workloads

This is where the case for switching is strongest. Databricks was built around Apache Spark and MLflow, and its current product focus is squarely on generative AI: Unity Catalog for governance, Mosaic AI and Foundation Model APIs for hosting and fine-tuning LLMs. Snowflake's answer is Cortex, its managed LLM and AI functions layer, which runs inside the same SQL platform rather than a separate ML environment. Teams running custom model training, not just calling a managed AI function against warehouse data, get more built-for-purpose tooling on Databricks. The full Databricks feature and pricing writeup has the detail on Mosaic AI and Foundation Model APIs if that's the deciding factor for your team.

Snowflake vs. Databricks vs. the rest of the field

Neither platform sits alone in this space, and the right pick depends on the same workload question that drives the Snowflake-to-Databricks decision itself.

| Platform | Pricing model | Free tier | Best known for | |---|---|---| | Snowflake | Usage-based (credits + storage), no flat price | None (30-day trial, $400 credits) | SQL warehousing, Iceberg tables, Cortex AI, Time Travel | | Databricks | Usage-based (DBUs + cloud cost), no flat price | Free Edition (forever, non-production) | Spark data engineering, MLflow, LLM fine-tuning | | Google BigQuery | On-demand per TiB scanned, or slot-based | First 1 TiB scanned/month free | Serverless SQL, deep Google Cloud integration | | Amazon Redshift | Serverless (RPU-hours) or provisioned clusters | 90-day, $300 credit trial | AWS-native warehousing, S3/Glue/QuickSight integration |

All four bill on usage in some form. None of them hands you a flat monthly number without running your own workload through a calculator.

How to run the migration

1. Inventory workloads by type first, not by table count. Split what's running today into SQL/BI, Spark/ETL pipelines, and ML/AI. The mix determines how much of the move is a lift-and-shift versus a rebuild.
2. Stand up Unity Catalog before moving data. Databricks' governance model is different enough from Snowflake's role-based access that mapping it out first avoids re-doing permissions twice.
3. Move data as Delta Lake or Iceberg, not a proprietary export. Snowflake stores and queries Iceberg tables directly. Databricks tables built on Delta Lake can expose Iceberg-compatible metadata to outside readers through a feature called UniForm, so data that's already in Iceberg format doesn't need a proprietary export first. Otherwise, plan for a bulk export and load into Delta Lake.
4. Rebuild pipelines in Lakeflow, keep dbt where you can. Snowpark jobs need Spark equivalents. dbt models generally point at the new warehouse with configuration changes rather than a rewrite.
5. Port BI connections last. Tableau, Looker, and Power BI all connect to Databricks SQL endpoints. Switch these once the underlying tables and pipelines are stable, so analysts aren't debugging two moving pieces at once.
6. Run both platforms in parallel during cutover. Usage-based billing on both sides means a parallel run has a real dollar cost, so scope the overlap window to weeks, not months, and confirm query results match before you decommission Snowflake compute.

When Snowflake is still the better fit

If your workload is SQL analytics and BI with little or no Spark engineering or custom ML, Databricks adds a steeper learning curve, since it assumes comfort with Spark, notebooks, and cluster or compute configuration, for limited added benefit. Snowflake's Cortex AI functions and native Iceberg support already cover teams that want AI features inside a SQL-first platform rather than a separate lakehouse. If nothing else about your workload changes, switching doesn't fix unpredictable costs either. Both platforms bill on usage, so the cost problem an oversized or always-on Snowflake warehouse creates can show up again as an oversized Databricks cluster. For a broader view of what else is out there, our Snowflake alternatives guide covers five options including BigQuery, Redshift, Dremio, and Starburst, several of which fit narrower needs than a full lakehouse migration.