BigQuery Pricing Updates

Recently, Google announced changes to its BigQuery pricing model that will impact how businesses and individuals use the platform.

Recently, Google announced changes to its BigQuery pricing model that will impact how businesses and individuals use the platform. (BigQuery is a serverless data analytics platform. You don't need to provision individual instances or virtual machines to use BigQuery. Instead, BigQuery automatically allocates computing resources as you need them. You can also reserve compute capacity ahead of time in the form of slots, which represent virtual CPUs. The pricing structure of BigQuery reflects this design.) It’s important to note that as of July 5, 2023, BigQuery customers will no longer be able to purchase flat-rate annual, flat-rate monthly, and flex slot commitments.

How BiqQuery Pricing Works:

BigQuery pricing has two main components:

  • Compute (analysis) pricing is the cost to process queries, including SQL queries, user-defined functions, scripts, and certain data manipulation language (DML) and data definition language (DDL) statements.
  • Storage pricing is the cost to store data that you load into BigQuery.

Other components include additional services, and data ingestion and extraction. As always, it’s free to load and export data. 

Billing 

Every project you create has to have a billing account attached to it. Any charges incurred by BigQuery jobs run in the project are billed to the attached billing account. BigQuery storage charges are also billed to the attached billing account. You can view BigQuery costs and trends by using the Cloud Billing reports page in the Google Cloud console.

You can also check the amount of process the query will use when you run it BEFORE you run it. This should give you a better understanding of price (once you do the calculation) and insights on query writing improvement.

 

What’s new and what’s not?

Not New: Pricing Storage

 In the new pricing, the cost of storage is pretty much unchanged. 

Active local storage

Based on the uncompressed bytes used in tables or table partitions modified in the last 90 days. 

Starting at

$0.02

Per GB. The first 10 GB is free each month.

Long-term logical storage

Based on the uncompressed bytes used in tables or table partitions modified for 90 consecutive days. 

Starting at

$0.01

Per GB. The first 10 GB is free each month.

Active physical storage

Based on the compressed bytes used in tables or table partitions modified for 90 consecutive days.

Starting at

$0.04

Per GB. The first 10 GB is free each month.

Long-term physical storage

Based on compressed bytes in tables or partitions that have not been modified for 90 consecutive days.

Starting at

$0.02

Per GB. The first 10 GB is free each month.

 

New: Compute Pricing Tiers

Compute pricing is moving from a per-unit-data-scanned model to a per-compute-instance model with three editions. This decision could be driven by a desire to generate more revenue from their compute-heavy BigQuery features. However, there is a possibility it could lead to unexpected expenses for businesses with infrequently accessed projects, as they may be subject to minimum compute slot costs.

Standard Edition

$0.04 per slot hour. This is a low-cost option for standard SQL analysis. 

Enterprise Edition

 $0.06 per slot hour. Supports advanced enterprise analytics. 

Enterprise Plus

$0.10 per slot hour. Supports mission-critical enterprise analytics. 

Keep in mind that BigQuery charges for other operations, including using BigQuery Omni, BigQuery ML, BI Engine, and streaming reads and writes.

 

Not New: Free Tier

Google’s BiqQuery free tier remains unchanged. Customers still receive 10 GB of storage, up to 1 TB queries free per month, and other resources like up to 1 GB of free capacity for Looker Studio users. These free usage limits are available during and after the free trial period.

 

New: Compressed Storage

Google has rolled out a new feature called "compressed storage" that offers users a cost-effective option for storage by paying more for computation. This trade-off can bring down expenses for infrequently accessed data. However, users need to be mindful that it may not apply to all scenarios and could result in higher computation costs in the long term.

Need specifics? View all BiQuery pricing details 

Conclusion

So what does this mean if your business is using Google BigQuery?  To be honest, it’s too early to tell. 

The new compressed storage feature is beneficial for infrequently accessed data, but those with rarely-used projects might experience higher costs due to minimum compute slot requirements. On the other hand,  businesses can potentially save money by dividing their databases and utilizing lower-cost compute units. It will be interesting to see how these changes unfold and how businesses adjust to the updated pricing structure.

To learn more about the benefits of BigQuery, check out our 5 Ways Digital Marketers Can Benefit from BigQuery article and Get in touch with our analytics consultants to explore how you can leverage Google BQ to simplify marketing data analysis and build winning digital marketing solutions.  

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