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Increase your availability and maximize your Bigtable spend with committed use discounts

August 14, 2023
Anton Gething

Senior Product Manager

Cloud Bigtable is a fully managed, scalable NoSQL database service that offers high performance and high availability at any scale. It is a good choice for storing large amounts of data that need single-digit millisecond low-latency access, such as operational logs, customer data, and machine learning datasets for use cases like personalization, feature stores, and fraud detection. 

Customers rely on Bigtable for business critical services with the expectation that Bigtable will scale to support their business needs as they grow. Bigtable already provides cost optimization through features like autoscaling to help customers manage costs. With this objective in mind, we are excited to announce the launch of committed use discounts to further optimize costs for customers committing to use Bigtable. You can get up to a 40% discount on Bigtable compute capacity by purchasing committed use discounts. 

Flexibility with Bigtable committed use discounts

Bigtable committed use discounts are similar to other Google Cloud committed use discounts and provide deeply discounted prices in exchange for your commitment to continuously use Bigtable compute capacity (as measured in Bigtable nodes) for a one- or three-year period. A one-year commitment provides a 20% discount. A three-year commitment provides a 40% discount! Bigtable committed use discounts are available now in all regions where Bigtable is available.1

Bigtable committed use discounts are spend-based against your Bigtable compute nodes and apply flexibly across all regions and projects; the spend commitment can be applied to any region where your Bigtable instance is located, or where you chose to have your Bigtable clusters. 

High availability and workload isolation with commitment flexibility

Commitment discounts can free up spend that can instead be used for replication — double your Bigtable footprint with a 40% committed use discount.

Cloud Bigtable replication lets you increase the availability and durability of your data, and enables capabilities like workload isolation and zero-touch failover. Expanding your Bigtable instance through replication means additional Bigtable clusters across zones, and potentially across regions. With committed use discounts of up to 40%, Bigtable helps optimize costs to leverage Bigtable replication capability and expand your service availability to the world. 

Isolate serving applications from batch reads

When you use a single cluster to run a batch analytics job that performs numerous large reads alongside an application that performs a mix of reads and writes, the large batch job can slow things down for the application's users. With replication, you can isolate workloads by routing the batch analytics jobs and application traffic to different clusters, so that batch jobs don't affect your application’s users. 

Ensure your data has a global presence

You can set up replication in locations across the world to put your data closer to your customers. For example, you can create an instance with replicated clusters in the US, Europe, and Asia and route application traffic to the nearest cluster. 

When are committed use discounts right for you?

Bigtable committed use discounts are ideal when your spend on Bigtable compute has a predictable portion that you are comfortable commiting to for a one- or three-year term. Let’s look at some examples…

Say that you have a Bigtable instance that has two clusters in different regions: us-central1 (Iowa) and us-west2 (Los Angeles). The cluster in us-central1 has 10 nodes and the cluster in us-west2 has 20 nodes.

Note: These are pricing examples. See Cloud Bigtable Pricing for current prices.

  • Expenditure in us-central1: 10 nodes * $0.65 per node per hour = $6.50 per hour

  • Expenditure in us-west2: 20 nodes * $0.78 per node per hour = $15.60 per hour

  • Total expenditure across all regions: $6.50 + $15.60 = $22.10 per hour

If you expect to spend that minimum of $22.10 per hour continuously for the next year or more, then you can make a commitment for that amount. In other words, when purchasing the commitment, you enter "$22.10" as the hourly on-demand commitment amount.

On the other hand, if you expect to scale down your clusters sometimes, you can make a commitment for a lower amount. Any expenditure above the commitment amount is charged at the on-demand rate.

Continuing this example, assume that you decide on a commitment of $22.10 per hour. As your next step, you choose the length of the commitment period.

As a basis for comparison, calculate the on-demand cost of Bigtable node usage at the chosen commitment rate, without the application of any commitment discounts:

Monthly cost based on on-demand pricing: $22.10 per hour * 730 hours = $16,133 per month.

From here, you can calculate the monthly costs and savings that you would see under a 1-year commitment with a 20% discount compared to a year of paying the full rates:

  • Monthly cost of a 1-year, $22.10/hour commitment: ($22.10 per hour - 20% discount) * 730 hours = $12,906.40 per month

  • Total savings per month: $16,133 - $12,906.40 = $3,226.60

  • Total savings with a 1-year, $22.10/hour commitment: $3,226.60 per month * 12 months = $38,719.2

You can apply similar math to calculating the costs and savings of a 3-year commitment, a 40% discount compared to the on-demand rates:

  • Monthly cost of a 3-year, $22.10/hour commitment: ($22.10 per hour - 40% discount) * 730 hours = $9,679.80 per month

  • Total savings per month: $16,133 - $9,679.80 = $6,453.20

  • Total savings with a 3-year, $22.10/hour CUD: $6,453.20 per month * 36 months = $232,315.20

A commitment that covers your expected minimum Bigtable usage over the years to come can lead to significant savings.

Learn more

Check out our documentation for more details on Bigtable committed use discounts. For Bigtable pricing information, take a look at our pricing page.

1.  Please see discount exclusions for select regions

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