Platform choice
Databricks vs Snowflake for banks and insurers: how to choose
A practical way for financial institutions to choose between Databricks and Snowflake by workload, team skills, governance and cost model, not by vendor benchmarks.
Hemant Walchale
· 3 min read
Every bank and insurer we speak to asks some version of the same question: Databricks or Snowflake? Both vendors will show you benchmarks where they win. Neither set of benchmarks is about your workloads.
Here’s a more useful way to decide.
Start with the work, not the platform
List the data work you’ll actually run in the next three years and estimate roughly how much of each there is:
| Workload | Leans Databricks | Leans Snowflake |
|---|---|---|
| Large-scale ETL and data engineering | ✓ | |
| Streaming (payments, fraud signals) | ✓ | |
| Machine learning and feature engineering | ✓ | |
| SQL analytics and BI for many analysts | ✓ | |
| Secure data sharing with partners and regulators | ✓ | |
| GenAI and retrieval over documents | Both have strong native options |
These are tendencies, not rules: both platforms now cover most of the list. But if most of your work sits in one column, that’s a strong signal.
Then look at your team
The best platform is the one your team can run well. A team of SQL analysts and BI developers will be productive on Snowflake quickly. A team with Python, Spark and ML experience will get more out of Databricks. Retraining is possible, but it’s a real cost and it slows the first year.
Governance: both can satisfy auditors
For regulated institutions, governance often decides the question, but both platforms are capable here. Databricks has Unity Catalog; Snowflake has Horizon. Both offer fine-grained access control, masking, lineage and audit history.
What matters more is how you implement it: policies defined as code, roles that map to your organization, and lineage that can trace a figure in a regulatory return back to its source. A well-governed platform on either vendor beats a badly governed one on the “right” vendor.
Cost: model your own workloads
Pricing models differ (Databricks charges for compute by DBU on top of cloud infrastructure; Snowflake charges credits for warehouses and serverless features). Comparing list prices tells you very little.
Instead:
- Pick three or four representative pipelines and queries.
- Estimate compute hours on each platform, including idle time and concurrency.
- Add the people cost of running it.
- If the estimate is close, run a two-week proof of concept on your own data.
For a sense of what’s achievable on either platform, our financial services benchmark analyses 38 published results. Both Databricks and Snowflake customers report cost reductions across a similar range.
Don’t ignore what you already have
Existing contracts and committed spend with a cloud provider, existing skills, and existing tools (dbt, BI platforms, orchestration) all shift the answer. If you’re on Microsoft and already licensing Fabric, that’s a third option worth evaluating seriously.
Using both
Many large banks run both: Databricks for engineering and ML, Snowflake for analytics and sharing. Open table formats (Delta Lake, Apache Iceberg) make that more practical than it used to be. It’s a valid architecture, as long as it’s a deliberate choice with clear ownership, not two platforms that grew up by accident.
How we’d decide
Workloads first, team second, governance implementation third, modelled cost fourth. If you’d like an independent recommendation (we have no reseller margin on either platform), the Data Platform Assessment ends with a written platform recommendation and cost comparison.
Not sure where your own platform stands? Take the free 3-minute data platform scorecard, or see how we work on Databricks. Unfamiliar terms are explained in the data engineering glossary.