Rates last reviewed: June 2025.

Snowflake vs Databricks Pricing

This comparison looks at the cost models for Snowflake and Databricks, including credits vs DBUs, storage, SQL Warehouse pricing, and the real hidden fees that matter in production.

2025 Comparison: Snowflake vs Databricks

For teams researching Snowflake vs Databricks in 2025, the decision is rarely just list-price math. It is about expected workload shape, support needs, data engineering tooling, and the hidden platform overhead that appears in long-running production use.

Snowflake credits vs Databricks DBUs

Snowflake charges compute in credits, which are consumed while warehouses run. Databricks charges compute in DBUs, which are consumed by clusters, jobs, and SQL Warehouses. Both models are volume-based, but they behave differently in practice:

• Snowflake: predictable warehouse credit burn based on size and active time, plus a 10% cloud services overhead in many environments.
• Databricks: DBUs depend on node type, cluster mode, and whether you use SQL Warehouses, Jobs clusters, or serverless compute.

How compute pricing differs

Snowflake uses a warehouse size system (XS, S, M, L, XL). Each size consumes roughly double the credits of the prior size. Databricks pricing is shaped by the underlying cloud VM, cluster size, and whether you run SQL Warehouse or Jobs compute.

Databricks SQL Warehouses are now the dominant analytics path for dashboards and BI workloads; they have a separate DBU profile from classic all-purpose clusters. That means two different compute stories:

• SQL Warehouses: optimized for concurrency and BI, often sold with serverless or classic options.
• Classic clusters: typically used for notebooks, ETL jobs, and custom Spark workloads.

Storage pricing and real storage differences

Snowflake storage is billed separately, usually around $23/TB/month for active data, plus long-term or capacity pricing when available. Databricks storage is object storage (S3, GCS, ADLS), typically billed at cloud provider rates, with additional costs from data copying, caching, and Unity Catalog metadata.

Both platforms also differ in metadata and management costs. Snowflake includes cloud services compute for the control plane. Databricks adds Unity Catalog overhead and a separate metadata layer for governance.

Hidden cost comparison: Snowflake cloud services vs Databricks Unity Catalog

Real-world cost surprises often come from platform overhead, not just raw compute:

• Snowflake: cloud services compute typically adds ~10% extra billing on top of warehouse credits for metadata, query planning, and caching.
• Databricks: Unity Catalog introduces additional metadata compute and can increase DBU usage when large catalogs, frequent permissions checks, or audit operations are active.

Sample comparison: moderate analytics workload

Example workload assumptions:

• Daily active workload: 8 hours
• Snowflake warehouse size: Small (2 credits/hr)
• Databricks cluster: 4 DBUs/hr
• Storage: 5 TB

Compute estimates:

Snowflake: 2 credits/hr × 8 hr/day × 22 days × $2.50 = $880/month

Databricks: 4 DBUs/hr × 8 hr/day × 22 days × $0.12 = $844.80/month

Storage estimate:

Snowflake: 5 TB × $23/TB-month = $115/month

Databricks: 5 TB × $0.023/GB-month = $117.50/month

In this example, Databricks is slightly cheaper on compute, but Snowflake’s simplified warehouse model and built-in cloud services can be easier to manage. The true answer depends on your actual query concurrency, edition, and governance needs.

When Snowflake tends to win

• Workloads that need simple, predictable warehouse sizing and strong data sharing support.
• Analytics teams that value managed compute and built-in security without managing VM instance types.
• Workloads with moderate concurrency and high concurrency scaling needs where Snowflake’s warehouse scaling helps reduce idle cluster orchestration.

When Databricks tends to win

• Workloads that require Spark-heavy ETL, machine learning, or advanced data engineering pipelines.
• Teams using Databricks SQL Warehouses for BI and dashboards, where SQL pricing can be tuned separately from batch jobs.
• Workloads that benefit from spot/preemptible VMs, cluster reuse, and explicit node-level control to reduce cloud costs.

Total cost of ownership: support, connectors, and ecosystem

Licensing and raw compute are only the start. Total cost of ownership includes support tiers, partner integrations, and how much the platform forces you to buy additional services.

• Snowflake: strong support for third-party connectors like Fivetran and dbt Cloud, built-in data sharing, and managed access with external tokenization. Those partner costs add to the stack, but they also reduce engineering integration work.
• Databricks: native support for Spark, MLflow, Delta Lake, and Unity Catalog means fewer separate tools for machine learning and data engineering, but the model can add cost if you pay for Databricks Premium or Enterprise support.

For many teams, the OPEX impact is driven by:

• Support tier: Snowflake and Databricks both charge more for enterprise SLAs, security reviews, and dedicated account teams.
• Connector and ingestion tooling: Snowflake often uses partner ETL connectors, while Databricks can be more self-contained for Spark-heavy pipelines.
• Governance and compliance: Unity Catalog and Snowflake access controls are both valuable, but each can require extra engineering effort to maintain.

Migration considerations: switching platforms in 2025

Switching from Snowflake to Databricks or vice versa is not just a licensing decision. It is a migration project with engineering, data modeling, and testing costs.

• Snowflake to Databricks: rework SQL and data models for Spark, convert stored procedures and tasks, and migrate metadata into Unity Catalog or Delta Lake. Data movement can be expensive if you are copying TBs of data between clouds or regions.
• Databricks to Snowflake: move ETL logic from Spark jobs to Snowflake warehouses, refactor SQL workflows, and rebuild access controls for Snowflake’s security model. You may also need to change batch job orchestration if you previously relied on Databricks-native notebooks and jobs.

In both directions, the time cost is often higher than the difference in list prices. Plan for:

• Data validation and reconciliation between systems.
• Rewriting or adapting analytics queries and BI dashboards.
• Training teams on a new deployment, monitoring, and cost-management workflow.

Practical comparison advice

Use actual workload data in the calculator rather than generic list prices. Key inputs that matter most:

• Snowflake warehouse size and expected active hours
• Databricks DBU rate, cluster size, and SQL Warehouse usage
• Storage volume and expected retention
• Edition and governance requirements

Next step: run a side-by-side estimate

The best way to choose is to model your workload in both systems. Use the calculator to compare the total bill for your actual query volume, storage, and compute profile.

Compare Snowflake and Databricks with your actual workload