Apache Kafka pricing on Google Cloud explained: Use the calculator to size your cluster

Key Concepts:

• Managed Service for Apache Kafka (Google Cloud)
• Cost factors: Data throughput, storage duration, traffic spikiness
• Google Cloud Pricing Calculator
• Producer bandwidth
• Tiered storage model
• Target CPU utilization
• Intrazone traffic
• Local replica following
• Committed use discounts

Cost Factors for Managed Kafka Cluster

The cost of running a managed Apache Kafka cluster on Google Cloud is primarily determined by three factors:

1. Data Throughput: The average amount of data written to the cluster per unit of time. This is the most important factor as it directly influences the required CPU, storage, and network resources.
   • Example: Producing and consuming 10 megabytes per second is considered substantial capacity and translates to 100 to 1,000 messages per second.
2. Storage Duration: The length of time data is stored in the cluster. This determines the total storage space needed.
   • Calculation: Storage space = Average producer bandwidth * Storage duration.
   • The service uses a tiered storage model, so you generally pay as you go without needing to pre-provision capacity.
3. Traffic Spikiness: The variability of traffic to the cluster. This affects the amount of RAM and CPU that needs to be pre-provisioned to handle peak loads.
   • Target CPU utilization is used to manage this. Lower utilization is needed for spikier traffic to ensure sufficient spare CPU capacity, which increases cost.
   • Example: If traffic doesn't vary much, utilization can be kept at 50%, allowing the cluster to handle traffic spikes double the average.

Using the Google Cloud Pricing Calculator

The Google Cloud Pricing Calculator can be used to estimate the cost of a managed Kafka cluster.

1. Search for "Kafka" in the calculator.
2. Select "Managed Service for Apache Kafka."
3. Add a managed Kafka cluster to the estimate.
4. Input the average data throughput (producer bandwidth).
5. Specify the storage duration.
6. Adjust the target CPU utilization based on traffic spikiness.

Cost Optimization Strategies

Several strategies can be employed to reduce the cost of running a managed Kafka cluster:

1. Local Replica Following: Reduce intrazone traffic by ensuring consumer clients read data from replicas in the same zone whenever possible. This minimizes data transfer costs across zones.
2. Committed Use Discounts: Purchase committed use discounts for CPU and RAM. One-year commitments offer a 20% discount, while three-year commitments provide a 40% discount.

Additional Resources

The Google Cloud pricing page provides example configurations and details on how cluster capacity translates to actual cluster size. The link to this resource is provided in the video description.

Conclusion

The cost of a managed Apache Kafka cluster on Google Cloud is primarily determined by data throughput, storage duration, and traffic spikiness. The Google Cloud Pricing Calculator can be used to estimate costs, and strategies like local replica following and committed use discounts can help reduce expenses. Understanding these factors and utilizing available resources can help optimize the cost of running a Kafka cluster.