Committed use discounts

This document describes committed use discounts (CUDs) for Google Cloud Managed Service for Apache Kafka.

Committed use discounts (CUDs) for Managed Service for Apache Kafka provide discounted prices in exchange for your commitment to continuously use a certain amount of compute resources to run Managed Service for Apache Kafka clusters for a year or longer. The compute resources that are eligible for CUDs are the vCPU and RAM.

We recommend Managed Service for Apache Kafka CUDs when you have a predictable minimum compute cost for the product that you can commit to for at least a year.

Managed Service for Apache Kafka CUD pricing

When you purchase a CUD, you choose the following:

  • A commitment period. This is a one- or three-year period. The commitment period dictates the level of discount that is offered to you. Managed Service for Apache Kafka CUDs offer two levels of discounts:

    • A one-year CUD gives you a 20% discount from the on-demand rate.
    • A three-year CUD gives you a 40% discount from the on-demand rate.

  • A commitment amount. This is your expected Managed Service for Apache Kafka expenditure on compute capacity per hour during the commitment period.

You are billed for the commitment fee on a monthly basis for the duration of the commitment period. For a full example, see the Managed Service for Apache Kafka CUD of this document.

The discount applies to any eligible usage in Managed Service for Apache Kafka projects associated with the Cloud Billing account used to purchase the commitment in any available region.

Any expenditure beyond the commitment is billed at the on-demand rate. As your Managed Service for Apache Kafka usage grows, you can purchase additional commitments to receive discounts on increased expenditures not covered by previous commitments.

If the on-demand rates for Managed Service for Apache Kafka change after you purchase a commitment, your commitment fee doesn't change. You receive the same discount percentage on applicable usage.

Resources eligible for Managed Service for Apache Kafka CUDs

Managed Service for Apache Kafka CUDs automatically apply to your spending on Kafka compute (vCPU and RAM) across projects for a billing account.

You can apply Managed Service for Apache Kafka CUDs on the following resources:

  • Compute: Consists of vCPU and RAM usage required to run Managed Service for Apache Kafka clusters. The usage is reported in terms of abstract units called Data Compute Units (DCUs). For more information about DCUs, see the Compute charges section in Managed Service for Apache Kafka pricing.

You cannot apply Managed Service for Apache Kafka CUDs on the pricing of the following resources:

  • Storage
  • Networking
  • Private Service Connect (PSC)

For pricing details for these resources, see the Managed Service for Apache Kafka pricing page.

Purchase a Managed Service for Apache Kafka commitment

To purchase or manage Managed Service for Apache Kafka CUDs for your Cloud Billing account, you must have a Billing Account Administrator role with that account.

You can purchase a Managed Service for Apache Kafka CUD in the Google Cloud console Commitments page. Select your Cloud Billing account, then click Purchase. For more details, see Purchasing spend-based commitments in the Google Cloud documentation.

After purchasing a commitment, the commitment goes into effect within the next hour. Its discounts are automatically applied to subsequent eligible usage. After you purchase a commitment, you can't cancel it. Make sure the size and duration of your commitment aligns with both your historical and your expected minimum expenditure on Managed Service for Apache Kafka compute.

In addition, before you purchase a commitment, read the Service Specific Terms regarding Committed Units.

An example Managed Service for Apache Kafka CUD scenario

To save on costs, you can commit to a minimum hourly spending for your Managed Service for Apache Kafka compute usage over the next one or three years. This commitment reflects your expected minimum usage.

As an example, assume that you have a cluster in us-central1 that consumes 18 DCUs per hour. From the pricing page, you can calculate the approximate hourly commitment cost running in us-central1 as $1.62 per hour.

If you expect to spend a minimum of $1.62 per hour continuously for the next year or more, then you can make a commitment for that amount. When purchasing the commitment, you enter $1.62 as the hourly on-demand commitment amount.

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 $1.62 per hour. As your next step, choose the length of the commitment period.

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

  • Monthly cost based on on-demand pricing: $1.62 per hour * 730 hours = $1,182.6 per month.

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

  • Monthly cost of a one-year, $1.62/hour commitment: ($1.62 per hour - 20% discount) * 730 hours = $946.08 per month
  • Total savings per month: $1,182.6 - $946.08 = $236.52
  • Total savings with a one-year, $1.62/hour commitment: $236.52 per month * 12 months = $2838.24

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

  • Monthly cost of a three-year, $1.62/hour commitment: ($1.62 per hour - 40% discount) * 730 hours = $709.56 per month
  • Total savings per month: $1,182.6 - $709.56 = $473.04
  • Total savings with a three-year, $1.62/hour CUD: $473.04 per month * 36 months = $17,029.44

A commitment that covers your expected minimum Managed Service for Apache Kafka compute usage over the years to come can lead to significant savings.

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