Monitor the instance utilization metrics like CPU usage, memory usage, and network traffic.
Storage usage:
Monitor the storage usage and optimize the storage allocation based on the changing workload demands.
Query optimization:
Optimize the database queries and indexes to reduce the number of queries and improve the query response time, enabling you to reduce the database instance size and save on costs.
Backup and retention:
Use Amazon RDS automated backup and retention policies to manage database backups and reduce the storage costs associated with manual backups.
Database size:
Monitor the database size and optimize the data retention policies based on the changing workload demands, enabling you to avoid over-provisioning and unnecessary storage costs.
On-Demand Capacity mode for predictable, intermittent, or unknown workloads to pay only for the read/write capacity you consume.
DynamoDB Auto Scaling to automatically adjust the read/write capacity based on demand, enabling you to optimize resource allocation and avoid over-provisioning.
Provisioned Capacity mode for sustained and predictable workloads to save up to 70% compared to On-Demand Capacity mode.
DynamoDB Global Tables for global data replication and disaster recovery.
DynamoDB Accelerator (DAX) for read-intensive workloads to improve query performance and reduce the read capacity needed.
DynamoDB Streams to capture real-time data changes and replicate data across different AWS services.
DynamoDB TTL to automatically delete expired items and reduce storage costs associated with old data.
DynamoDB Sparse Indexes to reduce storage costs by only indexing the attributes that are frequently used for queries.
AWS cost allocation tags to track and manage costs by application, environment, and business unit, allowing you to optimize resource allocation and avoid overages.
Reserved Capacity for sustained workloads to reduce the hourly rate and save up to 70% compared to on-demand pricing.
Monitor the read and write capacity utilization metrics and optimize the provisioned capacity based on the changing workload demands, enabling you to avoid over-provisioning and unnecessary capacity costs.
Item size and data types
Optimize the item size and data types to reduce the storage and I/O costs associated with large items and data types.
Query and scan efficiency
Optimize the query and scan operations to reduce the number of read capacity units consumed and the query response time, enabling you to reduce the provisioned capacity and save on costs.
Index size and usage
Monitor the index size and usage and optimize the index design to reduce the storage and I/O costs associated with unused or underutilized indexes.
Backup and retention
Use Amazon DynamoDB backup and restore to manage database backups and retention policies, and reduce the storage costs associated with manual backups.
Database Cost Savings