Rates last reviewed: June 2025.

Snowflake vs BigQuery Pricing

This page compares Snowflake and BigQuery pricing models in 2025, including credits, slot reservations, storage billing, and the hidden platform costs that matter for long-term ownership.

2025 Comparison: Snowflake vs BigQuery

Snowflake and BigQuery are both major analytics platforms, but their cost profiles diverge sharply. Snowflake bills managed compute in credits with separate storage, while BigQuery blends on-demand scan pricing with optional slot reservations.

How the compute models differ

Snowflake compute is priced by warehouse size and active time. BigQuery offers two compute paths: pay-per-TB scanned or flat-rate slot reservations. The choice matters for predictable workloads versus bursty, exploratory analytics.

• Snowflake: predictable credit burn based on warehouse size and active duration, with cloud services billed separately.
• BigQuery: on-demand scan costs are easy to start with, while slots deliver stable monthly pricing for high and consistent query volume.

Storage and data management

Snowflake storage is built into the platform and charged per TB/month. BigQuery storage pricing is managed by Google and can automatically drop to long-term rates after 90 days of inactivity.

• Snowflake: storage is billed alongside your storage tier, and time travel / fail-safe retention increases overall capacity.
• BigQuery: active storage is priced around $0.02/GB-month; long-term storage often costs roughly half that when data is not modified.

Hidden costs: Snowflake control plane vs BigQuery scan volume

Snowflake’s hidden costs are often control-plane overhead and retained storage. BigQuery’s hidden costs are usually query scanning behavior and over-committed slots.

• Snowflake: cloud services compute can add roughly 10% to active compute costs, and failing to purge old staging tables increases storage unexpectedly.
• BigQuery: repeated exploratory queries and wide table scans inflate costs quickly, while slot reservations can become wasteful if usage is seasonal.

Sample comparison: predictable analytics workload

Example workload assumptions:

• Daily active workload: 8 hours
• Snowflake warehouse size: Small (2 credits/hr)
• BigQuery on-demand scan volume: 100 TB/month
• Storage: 5 TB

Compute estimates:

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

BigQuery: 100 TB × $6.25/TB = $625/month

Storage estimate:

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

BigQuery: 5 TB × $0.02/GB-month = $102.40/month

In this case, BigQuery may be cheaper on pure query and storage cost, but Snowflake can still win when you need managed data sharing, simpler SQL governance, and predictable performance planning.

When Snowflake tends to win

• Workloads with strong data sharing, governed analytics, and frequent interactive BI queries.
• Teams that prefer a managed compute model with less infrastructure detail to tune.
• Companies that rely on built-in Snowflake features like time travel, zero-copy cloning, and secure data sharing.

When BigQuery tends to win

• Teams with bursty or exploratory analytics and irregular TB scan volumes.
• Environments that want serverless scaling and lower friction for huge data scan workloads.
• Organizations that already run heavily in Google Cloud and want to keep the platform native.

Total cost of ownership

Snowflake and BigQuery both have costs beyond the rate card:

• Snowflake: partner ETL connectors, dbt Cloud, and data sharing ecosystems are often purchased alongside the platform.
• BigQuery: licensing is simpler, but you may still need extra Google services for networking, ingestion, and governance.

The OPEX impact depends on how much of the stack you want to manage yourself versus buying a managed service bundle.

Migration considerations

Moving between Snowflake and BigQuery is a significant project. Costs show up in engineering time, query rewrites, and data validation.

• Snowflake to BigQuery: rewrite SQL for BigQuery’s dialect, move stored procedures and pipelines, and map warehouses to slot-based or on-demand pricing.
• BigQuery to Snowflake: convert query workflows to Snowflake SQL, align data models with Snowflake tables, and rebuild ingestion and governance around credits.

Plan for data reconciliation, dashboard updates, and training teams on the new query and cost model.

Practical comparison advice

Use actual workload inputs rather than published list prices. The calculator is the best place to compare because it handles both Snowflake credit math and BigQuery slot/scan math using your real TB volume, active hours, and storage footprint.

• Compare expected monthly scan volume against slot commitment break-even points.
• Model Snowflake warehouse size and active hours separately from storage retention.
• Review data sharing and governance requirements before choosing a primarily cost-driven option.

Next step: compare Snowflake and BigQuery with your workload

The best decision is driven by actual usage. Run your workload through the calculator and compare the total bill for both platforms side by side.

Compare Snowflake and BigQuery with your actual workload