How to Reduce DynamoDB Cost: Practical Tips and Examples

To reduce DynamoDB cost, optimize your table's capacity mode by using on-demand or provisioned with auto-scaling, and minimize read/write units by efficient data modeling and indexing. Also, use TTL to delete unused data automatically and avoid unnecessary scans or queries.

Syntax

Here are key DynamoDB settings and features to control cost:

• Capacity Modes: PROVISIONED (set read/write units) or PAY_PER_REQUEST (pay per request).
• Auto Scaling: Automatically adjusts provisioned capacity based on traffic.
• Time To Live (TTL): Automatically deletes expired items to save storage cost.
• Indexes: Use Global Secondary Indexes (GSI) and Local Secondary Indexes (LSI) wisely as they consume additional capacity.

bash

aws dynamodb update-table --table-name YourTableName --billing-mode PROVISIONED --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

aws dynamodb update-table --table-name YourTableName --billing-mode PAY_PER_REQUEST

aws dynamodb update-time-to-live --table-name YourTableName --time-to-live-specification Enabled=true,AttributeName=ttl

Example

This example shows how to create a DynamoDB table with provisioned capacity and enable TTL to reduce costs by deleting old data automatically.

python

import boto3

# Create DynamoDB client
client = boto3.client('dynamodb')

# Create table with provisioned capacity
client.create_table(
    TableName='CostOptimizedTable',
    KeySchema=[{'AttributeName': 'id', 'KeyType': 'HASH'}],
    AttributeDefinitions=[{'AttributeName': 'id', 'AttributeType': 'S'}],
    BillingMode='PROVISIONED',
    ProvisionedThroughput={'ReadCapacityUnits': 5, 'WriteCapacityUnits': 5}
)

# Enable TTL on attribute 'expireAt'
client.update_time_to_live(
    TableName='CostOptimizedTable',
    TimeToLiveSpecification={'Enabled': True, 'AttributeName': 'expireAt'}
)

print('Table created with provisioned capacity and TTL enabled.')

Output

Table created with provisioned capacity and TTL enabled.

Common Pitfalls

Many users overspend by:

• Using ON_DEMAND mode for consistently high traffic instead of PROVISIONED with auto-scaling.
• Creating too many or unnecessary indexes that increase write costs.
• Performing full table scans instead of targeted queries, which consume more read capacity.
• Not enabling TTL, causing storage of stale data and higher storage costs.

Correcting these can significantly reduce your bill.

python

Wrong way (excessive scans):
response = table.scan()

Right way (targeted query):
response = table.query(
    KeyConditionExpression=Key('id').eq('123')
)

Quick Reference

Tip	Description
Choose Capacity Mode	Use PROVISIONED with auto-scaling for steady traffic, PAY_PER_REQUEST for unpredictable traffic.
Enable TTL	Automatically delete expired items to save storage costs.
Limit Indexes	Create only necessary GSIs and LSIs to reduce write costs.
Optimize Queries	Use queries over scans to reduce read capacity usage.
Batch Operations	Use batch writes and reads to reduce request overhead.

Key Takeaways

Use provisioned capacity with auto-scaling for predictable workloads to save costs.

Enable TTL to automatically remove expired data and reduce storage charges.

Avoid unnecessary indexes as they increase write costs.

Prefer queries over scans to minimize read capacity consumption.

Batch your read and write operations to optimize request efficiency.