Data Warehouse Costs in Google BigQuery

What Drives Data Warehouse Costs?

After setting up or migrating to a cloud data warehouse, two main cost factors typically arise:

• Storage Costs
• Compute Costs

Choosing the right provider, pricing model, and an optimized data structure is crucial to avoiding unnecessary expenses. BigQuery's flexibility allows you to intelligently scale workloads and manage costs according to your needs.

How BigQuery's Pricing Structure Works

BigQuery data warehouse costs are based on a usage-based model and are divided into two main categories: compute costs and storage costs.

What’s unique: Both cost categories scale independently. This allows for flexible growth and prevents the creation of unnecessary resources – ultimately leading to reduced costs. Additionally, BigQuery offers a free tier in both areas for easy kick-offs and smaller data volumes.

With BigQuery’s flexible pricing models, you can tailor your costs precisely to your needs. You only pay for what you actually use and have the option to increase cost certainty through capacity models. Thanks to the free tier, it’s easy to start using BigQuery without making large upfront investments.

Overview Data Warehouse Pricing (PDF)

Free Tier

BigQuery offers a free tier for storage and compute costs, which is particularly attractive for startups, smaller projects, or companies with lower data volumes.

This tier includes 10 GiB of free storage per month and up to 1 TiB of free compute queries per month. This provides an ideal entry point to try out BigQuery without significant financial risk.

Compute Pricing

For compute costs, you pay for the computing power used to process queries, scripts, and other SQL-based operations in BigQuery. There are two pricing models you can choose from depending on your workload:

• On-demand pricing: With the on-demand model, you only pay for the amount of data processed by your queries. The price is based on the volume of data analyzed and is $6.25 per scanned TiB.
• Capacity pricing: With capacity pricing, you pay for reserved compute capacity in the form of slots (virtual CPUs) allocated to your queries. This model offers greater control and cost predictability, especially for companies with consistently high data volumes.

The on-demand model is ideal for companies with irregular or smaller queries, as you only pay for actual usage. The capacity model is best suited for companies that continuously process large amounts of data and need dedicated compute capacity. By reserving slots, the capacity model can also offer long-term cost advantages.

Storage Pricing

Storage costs are incurred as soon as you store data in BigQuery. Here, too, BigQuery offers flexibility with two pricing models: active storage and long-term storage.

• Active storage: This refers to the storage space for data that has been modified or used in the last 90 days. Prices for active storage start at $0.02 per GiB per month.
• Long-term storage: For data that hasn't been modified for 90 days, storage costs are automatically reduced by about 50%. This applies to historical or infrequently used data that remains readily available. Prices for long-term storage start at $0.01 per GiB per month.

Active storage is suitable for frequently used data, such as sales or sensor data, that is regularly updated. Long-term storage is ideal for archives, historical sales data, or compliance data that is rarely used but still needs to be stored. Long-term storage offers the same performance and reliability as active storage but at a lower cost.

In addition to the active and long-term storage options, BigQuery storage costs are also calculated using two other pricing models: physical and logical storage. We are happy to help you determine which option is best suited for your data.