DynamoDB Cost Optimization: A Comprehensive Guide by JusDB

August 19, 2025

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DynamoDB Cost Optimization: A Comprehensive Guide by JusDB

Amazon DynamoDB is a powerful NoSQL database service that offers excellent performance and scalability, but without proper optimization, costs can quickly spiral out of control. This guide provides practical strategies to optimize DynamoDB costs while maintaining the performance and reliability that JusDB requires.

🚀 Major Updates: November 2024 Pricing Revolution

Effective November 1, 2024, AWS announced the most significant DynamoDB pricing transformation in the service's history, with on-demand throughput costs reduced by 50% and global tables pricing slashed by up to 67%. This fundamental shift changes the cost-benefit equation for most DynamoDB workloads and makes on-demand mode the recommended default choice for the majority of applications.

Key Changes:

On-Demand Throughput: 50% cost reduction makes it competitive with provisioned capacity for many workloads
Global Tables: Up to 67% reduction for on-demand, 33% for provisioned capacity
Multi-Region Simplification: Replicated writes now match single-region pricing
Automatic Application: Changes applied automatically to all regions with no action required

Understanding DynamoDB Pricing Components

Before diving into optimization strategies, it's crucial to understand how DynamoDB pricing works:

Primary Cost Drivers:

Read/Write Capacity Units (RCUs/WCUs) - The throughput you provision or consume
Storage - Data stored in tables and indexes
Data Transfer - Moving data in and out of DynamoDB
Additional Features - Backups, streams, global tables, etc.

Capacity Mode Optimization

On-Demand vs Provisioned Capacity (Updated for 2024 Pricing)

Choose On-Demand When (Now More Attractive):

Workloads are unpredictable or have variable traffic patterns
You're prototyping or in early development stages
Traffic has sudden spikes that are difficult to predict
You have unused DynamoDB tables in your account (to reduce costs from provisioned but unused capacity)
NEW: Most workloads with consistent traffic (50% price reduction makes it cost-competitive)
You want to avoid capacity management overhead

Choose Provisioned When:

You have extremely predictable, steady traffic patterns with high utilization (70%+)
You can accurately forecast capacity requirements and commit to reserved capacity
Cost predictability is critical for budgeting
Application traffic is extremely consistent with minimal variance

Updated Cost Impact: With the November 2024 pricing changes, on-demand is now only approximately 3.5x more expensive than fully-utilized provisioned capacity (down from 6.94x), making the engineering effort to optimize provisioned capacity less economically justified for most workloads.

Important Note: With provisioned capacity mode, you're charged for provisioned capacities even if you haven't consumed any I/O. For unused tables, switching to on-demand mode can safely reduce costs.

New Cost Control Features (2024)

Configurable Maximum Throughput:
Introduced in May 2024, this feature allows you to set maximum read/write throughput limits for on-demand tables, providing cost predictability while maintaining auto-scaling benefits.

import boto3

dynamodb = boto3.client('dynamodb')

# Create table with maximum throughput limits
response = dynamodb.create_table(
    TableName='JusDB-CostControlled',
    BillingMode='PAY_PER_REQUEST',
    AttributeDefinitions=[
        {
            'AttributeName': 'user_id',
            'AttributeType': 'S'
        }
    ],
    KeySchema=[
        {
            'AttributeName': 'user_id',
            'KeyType': 'HASH'
        }
    ],
    OnDemandThroughput={
        'MaxReadRequestUnits': 10000,  # Maximum 10K RCUs
        'MaxWriteRequestUnits': 5000   # Maximum 5K WCUs
    }
)

Benefits:

Prevents runaway costs from poorly optimized code
Protects against accidental traffic surges
Maintains predictable cost ceilings
No additional cost to enable this feature
Can be adjusted anytime based on requirements

Auto Scaling Configuration

For provisioned capacity, implement auto scaling to optimize costs:

{
  "TableName": "JusDB-UserData",
  "BillingMode": "PROVISIONED",
  "ProvisionedThroughput": {
    "ReadCapacityUnits": 100,
    "WriteCapacityUnits": 50
  },
  "AutoScalingEnabled": true,
  "AutoScalingSettings": {
    "TargetTrackingScalingPolicies": [
      {
        "TargetValue": 70.0,
        "ScaleInCooldown": 300,
        "ScaleOutCooldown": 60
      }
    ]
  }
}

Best Practices:

Set target utilization between 60-80%
Use shorter scale-out cooldowns (60s) and longer scale-in cooldowns (300s)
Monitor CloudWatch metrics to fine-tune scaling policies

Warm Throughput Management (New Feature)

Introduced in November 2024, warm throughput provides visibility into your table's instant scaling capacity and allows pre-warming for peak events.

Understanding Warm Throughput:

Shows the number of read/write operations your table can instantly support
Automatically adjusts based on historical usage patterns
Available at no cost for all tables and indexes
Critical for planning peak events (product launches, flash sales)

Pre-Warming Tables:

import boto3

dynamodb = boto3.client('dynamodb')

# Check current warm throughput
response = dynamodb.describe_table(TableName='JusDB-FlashSale')
warm_throughput = response['Table']['WarmThroughput']
print(f"Current warm read capacity: {warm_throughput['ReadUnitsPerSecond']}")
print(f"Current warm write capacity: {warm_throughput['WriteUnitsPerSecond']}")

# Pre-warm table for anticipated traffic spike
dynamodb.modify_table(
    TableName='JusDB-FlashSale',
    WarmThroughput={
        'ReadUnitsPerSecond': 100000,   # Pre-warm for 100K reads/sec
        'WriteUnitsPerSecond': 50000    # Pre-warm for 50K writes/sec
    }
)

Pre-Warming Use Cases:

Product launches with expected 10x-100x traffic increase
Marketing campaigns and flash sales
Database migrations with high initial load
Black Friday/Cyber Monday preparation
Live events with predictable traffic surges

Table Design Optimization

Efficient Key Design

Partition Key Distribution:
Ensure your partition key provides good distribution to avoid hot partitions:

# Poor partition key - creates hot partitions
partition_key = "user_type"  # Only a few values

# Better partition key - distributes load
partition_key = f"user_id#{timestamp_bucket}"

Composite Keys for Query Efficiency:
Design sort keys to enable efficient range queries:

# Structure: PK = user_id, SK = timestamp#action
{
  "PK": "user_123",
  "SK": "2024-01-15T10:30:00#login",
  "details": {...}
}

Item Size Optimization

Minimize Item Size:

Remove unnecessary attributes
Use shorter attribute names for frequently accessed items
Compress large text fields before storage
Consider splitting large items across multiple records

# Instead of:
{
  "user_identification_number": "12345",
  "user_full_name": "John Smith",
  "user_email_address": "john@example.com"
}

# Use:
{
  "uid": "12345",
  "name": "John Smith", 
  "email": "john@example.com"
}

Index Strategy

Global Secondary Index (GSI) Optimization

Sparse Indexes:
Create indexes only for items that need them:

# Only add GSI attributes for items that will be queried
if user_type == "premium":
    item["GSI1PK"] = f"premium#{region}"
    item["GSI1SK"] = signup_date

Right-size GSI Capacity:
GSIs often need different capacity than base tables:

{
  "GlobalSecondaryIndexes": [
    {
      "IndexName": "UserTypeIndex",
      "ProvisionedThroughput": {
        "ReadCapacityUnits": 20,  # Lower than base table
        "WriteCapacityUnits": 10
      }
    }
  ]
}

Local Secondary Index (LSI) Considerations

Use LSIs sparingly as they:

Share capacity with the base table
Increase item size limits complexity
Cannot be added after table creation

Global Tables Cost Optimization (Major Update)

The November 2024 pricing changes make global tables significantly more affordable, with replication costs reduced by up to 67% for on-demand tables.

Updated Global Tables Strategy

Before November 2024:

Global table replication was often cost-prohibitive
Complex cost modeling required for multi-region deployments
Limited to critical applications requiring global presence

After November 2024:

Replicated writes now match single-region write pricing
67% cost reduction for on-demand global tables
33% cost reduction for provisioned global tables
Simplified cost modeling across regions

# Global table creation with new cost-effective pricing
import boto3

dynamodb = boto3.client('dynamodb')

# Create global table with replicas in multiple regions
response = dynamodb.create_global_table(
    GlobalTableName='JusDB-GlobalUserData',
    ReplicationGroup=[
        {'RegionName': 'us-east-1'},
        {'RegionName': 'eu-west-1'},
        {'RegionName': 'ap-southeast-1'}
    ]
)

New Global Tables Benefits:

Cost Parity: Multi-region replication costs same as single-region writes
Simplified Architecture: Easier to justify global deployment
Enhanced Availability: 99.999% availability with multi-active regions
Reduced Complexity: No complex cost calculations for multi-region strategies

Query and Access Pattern Optimization

Batch Operations

Use BatchGetItem and BatchWriteItem:

import boto3

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('JusDB-UserData')

# Batch read - up to 100 items
response = dynamodb.batch_get_item(
    RequestItems={
        'JusDB-UserData': {
            'Keys': [
                {'user_id': '123'},
                {'user_id': '124'},
                # ... up to 100 keys
            ]
        }
    }
)

# Batch write - up to 25 items
with table.batch_writer() as batch:
    for item in items:
        batch.put_item(Item=item)

Efficient Pagination

Use LastEvaluatedKey for pagination instead of scanning all items:

def paginated_scan(table_name, limit=100):
    response = dynamodb.scan(
        TableName=table_name,
        Limit=limit
    )
    
    while 'LastEvaluatedKey' in response:
        yield response['Items']
        response = dynamodb.scan(
            TableName=table_name,
            Limit=limit,
            ExclusiveStartKey=response['LastEvaluatedKey']
        )

Avoid Expensive Operations

Minimize Scans:

Use Query instead of Scan whenever possible
If scanning is necessary, use parallel scans with filters
Implement pagination to avoid timeout issues

Optimize Filters:

Apply filters on sort keys when possible
Use projection expressions to retrieve only needed attributes

Strategic Migration: On-Demand to Provisioned Capacity

Despite the November 2024 pricing improvements, there are still scenarios where migrating from on-demand to provisioned capacity can yield significant cost savings, particularly for high-traffic, predictable workloads.

When to Consider Migration

Ideal Candidates for Provisioned Capacity:

Consistent traffic patterns with predictable peaks and troughs
High-volume workloads (>1000 RCU/WCU sustained)
Applications with steady baseline traffic plus predictable spikes
Workloads with >50% capacity utilization over time
Budget-constrained environments requiring cost predictability

Workload Analysis Framework

Step 1: Traffic Pattern Analysis

Use CloudWatch metrics to analyze your workload over 2-4 weeks:

import boto3
import datetime
from datetime import timedelta

cloudwatch = boto3.client('cloudwatch')

def analyze_dynamodb_usage(table_name, days=30):
    end_time = datetime.datetime.utcnow()
    start_time = end_time - timedelta(days=days)
    
    # Get consumed capacity metrics
    metrics = {
        'ConsumedReadCapacityUnits': [],
        'ConsumedWriteCapacityUnits': []
    }
    
    for metric_name in metrics.keys():
        response = cloudwatch.get_metric_statistics(
            Namespace='AWS/DynamoDB',
            MetricName=metric_name,
            Dimensions=[
                {
                    'Name': 'TableName',
                    'Value': table_name
                }
            ],
            StartTime=start_time,
            EndTime=end_time,
            Period=3600,  # 1 hour periods
            Statistics=['Average', 'Maximum', 'Sum']
        )
        metrics[metric_name] = response['Datapoints']
    
    return analyze_provisioned_feasibility(metrics)

Migration Decision Matrix

Criteria	On-Demand	Provisioned	Weight
Traffic Predictability	Unpredictable, spiky	Predictable patterns	High
Capacity Utilization	<40% average	>50% average	High
Cost Savings Potential	<20% savings	>20% savings	High
Operational Complexity	Minimal	Requires monitoring	Medium
Development Stage	Prototype/Early	Production/Mature	Medium
Budget Requirements	Flexible	Fixed/Predictable	Low

When NOT to Migrate

Keep On-Demand For:

Tables with <$50/month costs (management overhead exceeds savings)
Highly variable workloads with unpredictable spikes
Development and testing environments
Tables with <40% projected capacity utilization
Applications in early development stages

Storage Cost Optimization

Table Class Selection

DynamoDB Standard vs Standard-IA:

Choose Standard-IA (Infrequent Access) when:

Storing data that you don't access regularly (application logs, old social media posts, archived records)
Long-term storage requirements with infrequent access patterns
Storage cost reduction is more important than read/write performance cost

Cost Considerations:

Standard-IA reduces storage costs by up to 60%
Read and write operations cost more than standard tables
Not compatible with reserved capacity
Best for tables where storage cost exceeds throughput cost

# Create table with Standard-IA class
table = dynamodb.create_table(
    TableName='JusDB-ArchiveData',
    TableClass='STANDARD_INFREQUENT_ACCESS',
    AttributeDefinitions=[
        {
            'AttributeName': 'archive_id',
            'AttributeType': 'S'
        }
    ],
    KeySchema=[
        {
            'AttributeName': 'archive_id',
            'KeyType': 'HASH'
        }
    ],
    BillingMode='PAY_PER_REQUEST'
)

Data Lifecycle Management

Implement TTL (Time To Live):

import time

# Set TTL for temporary data
ttl_timestamp = int(time.time()) + (30 * 24 * 60 * 60)  # 30 days

item = {
    'user_id': '123',
    'session_data': {...},
    'ttl': ttl_timestamp
}

# Enable TTL on the table
table.update(
    AttributeDefinitions=[
        {
            'AttributeName': 'ttl',
            'AttributeType': 'N'
        }
    ],
    TimeToLiveSpecification={
        'AttributeName': 'ttl',
        'Enabled': True
    }
)

Data Archiving Strategy:

Move old data to S3 using DynamoDB exports
Use DynamoDB Streams to trigger archival processes
Consider partitioning by date for easier archival

Monitoring and Optimization

CloudWatch Metrics to Monitor

Key Metrics for Cost Optimization:

ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits
ProvisionedReadCapacityUnits and ProvisionedWriteCapacityUnits
ReadThrottledEvents and WriteThrottledEvents
ItemCount and TableSizeBytes

Cost Monitoring Dashboard

Create custom CloudWatch dashboards to track:

Capacity utilization percentages
Monthly cost trends
Hot partition indicators
Query vs Scan ratios

Reserved Capacity

For predictable workloads, consider DynamoDB Reserved Capacity:

Benefits:

Up to 76% cost savings over on-demand
1-year or 3-year terms available
Applies automatically to matching usage
Allows upfront commitment on base level of provisioned capacity

Best For:

Steady-state production workloads
Applications with predictable traffic patterns
Long-term projects with stable requirements
Workloads in specific AWS regions where you can predict throughput needs

Updated Limitations:

Not available for DynamoDB Standard-IA table class
Not available for on-demand capacity mode
Regional commitment required
NEW CONSIDERATION: With 2024 pricing changes, the cost gap between reserved and on-demand capacity has narrowed significantly

Cost Tracking and Management

Cost Allocation Tags

Implement comprehensive tagging strategy for fine-grained cost visibility:

Essential Tags for JusDB:

# Apply tags when creating tables
table_tags = [
    {
        'Key': 'Environment',
        'Value': 'Production'
    },
    {
        'Key': 'Application',
        'Value': 'JusDB'
    },
    {
        'Key': 'Team',
        'Value': 'Database'
    },
    {
        'Key': 'CostCenter',
        'Value': 'Engineering'
    },
    {
        'Key': 'Purpose',
        'Value': 'UserData'
    }
]

# Tag existing table
dynamodb.tag_resource(
    ResourceArn='arn:aws:dynamodb:region:account:table/JusDB-UserData',
    Tags=table_tags
)

Tag-Based Cost Analysis:

Create cost allocation reports per tag
Track spending by environment (dev/staging/prod)
Monitor costs per application feature
Identify optimization opportunities by team/project

AWS Pricing Calculator Integration

Pre-Deployment Cost Estimation:
Use the AWS Pricing Calculator for DynamoDB to estimate costs before building:

Capacity Planning: Input expected read/write patterns
Feature Costing: Include backups, streams, DAX costs
Scenario Testing: Compare on-demand vs provisioned costs
Growth Modeling: Project costs as data and traffic scale

Calculator Best Practices:

Use conservative estimates and add 20% buffer
Include all DynamoDB features in calculations
Test multiple capacity modes for comparison
Factor in data transfer costs for multi-region scenarios

Advanced Optimization Techniques

Single-Table Design

Benefits:

Reduced complexity and cost
Better performance through data locality
Fewer network calls

Implementation:

# Single table with entity types
{
  "PK": "USER#123",
  "SK": "PROFILE",
  "entity_type": "user_profile",
  "name": "John Smith"
}

{
  "PK": "USER#123", 
  "SK": "ORDER#456",
  "entity_type": "order",
  "total": 99.99
}

Modern Capacity Planning (2024 Update)

The New Default Recommendation:
With the November 2024 pricing changes, on-demand mode is now the recommended default for most DynamoDB workloads. The previous guidance of "start with on-demand, migrate to provisioned for predictable workloads" should be reconsidered.

Updated Decision Matrix:

Workload Type	Recommendation	Reasoning
New Applications	On-Demand	50% cost reduction + no capacity management
Variable Traffic	On-Demand	Automatic scaling with improved economics
Predictable, High Utilization (80%+)	Provisioned + Reserved	Still most cost-effective for heavy, consistent usage
Development/Testing	On-Demand	Pay only for actual usage
Global Applications	On-Demand Global Tables	67% cost reduction makes this highly attractive

Connection Pooling and Caching

Implement Connection Pooling:

import boto3
from botocore.config import Config

# Configure connection pooling
config = Config(
    max_pool_connections=50,
    retries={'max_attempts': 3}
)

dynamodb = boto3.resource('dynamodb', config=config)

Add Caching Layer:

Use ElastiCache or Application-level caching
Cache frequently accessed read-heavy data
Implement cache invalidation strategies

Cost Optimization Checklist

Pre-Deployment Planning (Updated for 2024)

☐ Use AWS Pricing Calculator with updated November 2024 pricing
☐ Design table schema for optimal partition distribution
☐ NEW: Default to on-demand mode unless extremely predictable workload
☐ Evaluate table class requirements (Standard vs Standard-IA)
☐ Define comprehensive tagging strategy
☐ NEW: Assess global table requirements with improved economics
☐ NEW: Plan warm throughput strategy for peak events

Monthly Review Tasks (Updated)

☐ Analyze capacity utilization reports
☐ Review auto-scaling metrics and adjust targets (if using provisioned)
☐ Check for unused indexes and tables
☐ UPDATED: Re-evaluate provisioned vs on-demand based on new pricing
☐ Monitor query patterns for optimization opportunities
☐ Review data retention policies and TTL settings
☐ UPDATED: Reassess reserved capacity ROI with new pricing dynamics
☐ Analyze cost allocation tag reports
☐ Review table class assignments for infrequently accessed data
☐ NEW: Monitor warm throughput utilization and pre-warming costs
☐ NEW: Evaluate global table opportunities with reduced costs

Quarterly Optimization (Updated)

☐ Perform comprehensive table design review
☐ Analyze access patterns for single-table opportunities
☐ Review backup and point-in-time recovery needs
☐ NEW: Evaluate global table expansion opportunities
☐ UPDATED: Reconsider capacity mode strategies based on actual usage and new pricing
☐ Review and optimize cost allocation tags
☐ Assess Standard-IA migration opportunities
☐ NEW: Update AWS Pricing Calculator estimates with November 2024 pricing
☐ NEW: Review warm throughput patterns and pre-warming effectiveness
☐ NEW: Analyze cost impact of maximum throughput configurations

Conclusion

Optimizing DynamoDB costs requires a combination of proper table design, efficient access patterns, appropriate capacity planning, and continuous monitoring. The November 2024 pricing changes fundamentally alter the cost optimization landscape, making DynamoDB more accessible and cost-effective than ever before.

Key Takeaways for 2024 and Beyond:

On-Demand First: The 50% price reduction makes on-demand the default choice for most workloads
Global Tables Revolution: 67% cost reduction opens new architectural possibilities
Enhanced Control: Configurable maximum throughput and warm throughput provide better cost management
Simplified Architecture: Reduced need for complex provisioned capacity optimization
Cost Visibility: Comprehensive tagging and AWS Pricing Calculator for planning

The New Cost Optimization Paradigm:

The traditional approach of starting with on-demand and migrating to provisioned capacity is no longer universally applicable. Many workloads will find on-demand mode remains the most cost-effective choice throughout their lifecycle, especially when factoring in the engineering effort required to optimize provisioned capacity.

Next Steps for JusDB:

1. Immediate Actions:

Review existing provisioned tables for on-demand migration opportunities
Implement cost allocation tags across all DynamoDB resources
Configure maximum throughput limits for cost protection
Evaluate global table expansion with new pricing

2. Strategic Planning:

Reassess capacity planning methodologies based on new pricing
Update cost modeling templates with November 2024 prices
Plan warm throughput strategies for upcoming peak events
Establish new monitoring and optimization cycles

3. Long-term Optimization:

Develop expertise in warm throughput management
Create automated cost optimization workflows
Implement advanced monitoring for the new cost model
Regular reviews based on evolving pricing and features

Remember that cost optimization is now more straightforward with improved pricing, but continuous monitoring and adaptation remain essential as AWS continues to innovate and your application evolves.

Additional Resources:

This guide was last updated in January 2025 to reflect the latest DynamoDB pricing changes and feature releases. For the most current information, always refer to the official AWS documentation.

DynamoDB Cost Optimization: A Comprehensive Guide by JusDB

🚀 Major Updates: November 2024 Pricing Revolution

Understanding DynamoDB Pricing Components

Primary Cost Drivers:

Capacity Mode Optimization

On-Demand vs Provisioned Capacity (Updated for 2024 Pricing)

Choose On-Demand When (Now More Attractive):

Choose Provisioned When:

New Cost Control Features (2024)

Auto Scaling Configuration

Warm Throughput Management (New Feature)

Table Design Optimization

Efficient Key Design

Item Size Optimization

Index Strategy

Global Secondary Index (GSI) Optimization

Local Secondary Index (LSI) Considerations

Global Tables Cost Optimization (Major Update)

Updated Global Tables Strategy

Query and Access Pattern Optimization

Batch Operations

Efficient Pagination

Avoid Expensive Operations

Strategic Migration: On-Demand to Provisioned Capacity

When to Consider Migration

Workload Analysis Framework

Migration Decision Matrix

When NOT to Migrate

Storage Cost Optimization

Table Class Selection

Data Lifecycle Management

Monitoring and Optimization

CloudWatch Metrics to Monitor

Cost Monitoring Dashboard

Reserved Capacity

Cost Tracking and Management

Cost Allocation Tags

AWS Pricing Calculator Integration

Advanced Optimization Techniques

Single-Table Design

Modern Capacity Planning (2024 Update)

Connection Pooling and Caching

Cost Optimization Checklist

Pre-Deployment Planning (Updated for 2024)

Monthly Review Tasks (Updated)

Quarterly Optimization (Updated)

Conclusion

Key Takeaways for 2024 and Beyond:

The New Cost Optimization Paradigm:

Next Steps for JusDB:

1. Immediate Actions:

2. Strategic Planning:

3. Long-term Optimization:

Additional Resources:

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