AWS Cost Efficiency
How to Save AWS DynamoDB Costs?

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Amazon DynamoDB, a NoSQL database service offered by Amazon Web Services (AWS), provides fast and flexible storage for data. It is a popular choice for applications that require high performance and scalability. However, DynamoDB can be expensive to operate if you are not mindful of the usage. This blog post discusses some proven methods for effective cost-optimization for your DynamoDB.

Optimizing costs with capacity mode selection

The right choice of capacity mode for DynamoDB is important for cost-optimization. Let us discuss some of the options available.

On-demand capacity mode

On-demand capacity mode is ideal for workloads with unpredictable traffic patterns. It allows DynamoDB to handle the resource allocation dynamically, scaling up and down based on demand spikes. You pay only for the actual requests made to your tables in the absence of data for any sort of capacity planning. This mode is well-suited for new workloads where traffic patterns are yet to be determined.

Provisioned capacity mode

Provisioned capacity is the right fit for workloads with predictable traffic patterns. This mode provides fine-grained control over resource allocation based on the expected number of reads and writes per second.

Auto-scaling for provisioned capacity mode

Auto-scaling complements provisioned capacity mode. By setting up auto-scaling policies, you can define the target utilization, upper and lower throughput ranges, and let DynamoDB manage the capacity provisioning for you. This helps to avoid overprovisioning and ensures cost optimization by aligning the capacity with the actual demand.

Reserved capacity mode

If your application has a highly consistent and predictable traffic pattern, you can consider purchasing reserved capacity for your DynamoDB tables. Reserved capacity offers substantial cost savings compared to the on-demand and provisioned capacity modes. By committing to a specific amount of provisioned capacity for a certain duration, you can enjoy heavily discounted pricing.

Optimizing storage costs with table classes

DynamoDB offers different table classes that you can use to balance storage costs with read/write costs.

Standard table class

This is the default class option in DynamoDB. It provides a balanced approach to storage and read/write costs. This table class is suitable for most use cases where the storage and throughout requirements are proportional.

Standard-infrequent access (Standard-IA) table class

This class is designed to optimize storage costs for workloads that have large amounts of data with infrequent access needs. With up to 60% lower storage costs compared to the standard table class, standard-IA provides the same level of durability, availability, performance, and scalability. Choose this class for workloads with data that is not frequently accessed but still needs to be retained within DynamoDB.

Leveraging cost allocation tags

Cost allocation tags provide a powerful mechanism for tracking and analyzing costs associated with your DynamoDB tables. These tags help achieve fine-grained visibility into the cost breakdown per table. To enable table-level cost analysis, tag your tables based on criteria such as organization, teams, services, or applications. With this, you can identify the tables that contribute the most to overall costs and take necessary actions for optimization.  

Conclusion

Optimizing costs for your Amazon DynamoDB workloads is essential to ensure efficient resource allocation and maximize cost savings. By selecting the appropriate capacity mode, leveraging auto-scaling, and utilizing cost allocation tags you can optimize your DynamoDB usage and reduce costs without compromising performance or scalability. In addition, leveraging CloudWatch to monitor key metrics such as throttling, latency, and deleted items can help you identify bottlenecks and optimize your application's performance. Keep monitoring your workloads and exploring optimization opportunities to continuously improve cost efficiency in your DynamoDB deployments.

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