The Right Price-Performance Mix: Changes to BigQuery’s Pricing Model

BigQuery is Google’s fully managed, serverless data warehouse that enables scalable analysis over petabytes of data. Google recently announced upcoming changes to BigQuery’s commercial model, including the introduction of:

• Tiered compute pricing model for BigQuery called BigQuery editions
• Autoscaling capability that scales capacity up and down based on customer workloads
• Compressed (physical) storage billing model

Google also announced new BigQuery editions with 3 pricing tiers:

• Standard
• Enterprise
• Enterprise Plus

You can pick the right feature set for individual workload requirements, and mix and match for the right price-performance.

BigQuery editions come with two innovations:

1. Compute capacity autoscaling that adds fine-grained compute resources in real-time to match the needs of your workload demands. Google estimates that with new granular autoscaling, customers can reduce their current committed capacity by 30-40%.
2. Compressed storage pricing allows you to only pay for data storage after it’s been highly compressed.

All of these changes should deliver greater flexibility, predictable pricing, and the best price performance, and reflect Google’s larger pricing commitment for their cloud portfolio. The combination of these innovations with multi-year commitment usage discounts, can help you lower your total cost of ownership.

No More Flat-Rate Slot Reservation Model

As Google transitions to this new commercial model, they will no longer offer BigQuery’s existing flat-rate slot reservation model. Starting on July 5, 2023, Google will:

• No longer offer flat-rate annual, flat-rate monthly, and flex slot commitments
• Begin migration of existing flat-rate and flex slots to the new BigQuery editions 
• Increase the on-demand pricing model by 25% across all regions

Price Increasing in All Regions

The price per TB of data scanned is increasing by 25% in all regions. Billing will reflect the new calculated price starting July 5, 2023.

Customers can also use the new physical storage billing model, where charges are based on the compressed size of data. Contact us for details on eligibility.

Next Steps for BigQuery Customers

Existing slot commitments that expire before July 5, 2023, have the option to renew their flat-rate annual, flat-rate monthly, or flex slot commitments.

On July 5, 2023, Google will begin migration of existing flat-rate and flex slots to the new BigQuery editions. See below for the default migration path to the new BigQuery edition SKUs.Wursta can also assist with reviewing your current flat-rate slot usage to:

• Understand how best to use a combination of commitments and autoscaler
• Learn more about BigQuery editions to match your workloads to edition tiers
• Comprehend your storage billing options to support optimization of your data storage

Migration date	Current	After migration
At the end of the annual flat rate commitment period	Annual flat-rate (2.3c/slot hr)	Default: Enterprise edition 1-year commitment (4.8c/slot hr) Option: Migrate to the 3-year Enterprise edition commitment at 3.6c/slot hr, and/or optimize your usage with a combination of commitments and autoscaler.
On July 5, 2023, or at the end of the 30-day commitment period, whichever is later	Monthly flat-rate (2.7c/slot hr)	Default: Enterprise edition baseline reservation (6c/slot hr) Option: You can change or remove this reservation at any time post- migration with the option of leveraging autoscaler to optimize your usage. You can also purchase new slot commitments to get a discount on baseline slots.
On July 5, 2023	Flex slots (4c/slot hr)	Default: Enterprise edition baseline reservation (6c/slot hr) Option: You can change or remove this reservation at any time post- migration with the option to configure the autoscaler to optimize your usage. You can also purchase new slot commitments to get a discount on baseline slots.

We’re Here to Help

If you have any questions, please refer to the launch blog, FAQs or reach out to Wursta directly.