Amazon DynamoDB Cost Optimization Guide

Service Overview

What is Amazon DynamoDB?

Fully managed NoSQL database service with single-digit millisecond performance
Serverless with automatic scaling capabilities
Pay-per-use pricing model with multiple capacity modes
Built-in security, backup, and multi-region replication

Why DynamoDB Cost Optimization Matters

Costs can vary dramatically between On-Demand and Provisioned modes (up to 70% difference)
Read/write capacity units directly impact costs
Storage class selection affects long-term costs
Feature selection (Global Tables, backups, streams) adds significant costs

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Cost Analysis & Monitoring

Key Cost Drivers

Core Cost Dimensions:

**Provisioned Write Capacity Units (WCU)**: $0.000065 per WCU-hour (1 WCU = 1KB/second)
**Provisioned Read Capacity Units (RCU)**: $0.00013 per RCU-hour (1 RCU = 4KB/second strongly consistent, 8KB eventually consistent)
**Data Storage**: $0.10 per GB-month (Standard), lower for Standard-IA
**On-Demand Pricing**: $1.25 per million write requests, $0.25 per million read requests

Optional Cost Features:

**Point-in-Time Recovery (PITR)**: $0.20 per GB-month
**On-Demand Backup**: $0.10 per GB-month
**Data Transfer Out**: $0.09 per GB to other regions
**Data Export to S3**: $0.10 per GB
**Global Tables**: $0.000975 per replicated WCU-hour

Using the Power's Tools

Get DynamoDB costs by API operation:


usePower("aws-cost-optimization", "awslabs.billing-cost-management-mcp-server", "cost_explorer", {
  "operation": "getCostAndUsage",
  "start_date": "2024-11-01",
  "end_date": "2024-12-01",
  "granularity": "DAILY",
  "group_by": "[{\"Type\": \"DIMENSION\", \"Key\": \"API_OPERATION\"}]",
  "metrics": "[\"UnblendedCost\"]",
  "filters": "{\"Dimensions\": {\"Key\": \"SERVICE\", \"Values\": [\"Amazon DynamoDB\"]}}"
})

Analyze DynamoDB usage patterns:


usePower("aws-cost-optimization", "awslabs.billing-cost-management-mcp-server", "cost_explorer", {
  "operation": "getCostAndUsage",
  "start_date": "2024-11-01",
  "end_date": "2024-12-01",
  "granularity": "MONTHLY",
  "group_by": "[{\"Type\": \"DIMENSION\", \"Key\": \"USAGE_TYPE\"}]",
  "metrics": "[\"UsageQuantity\", \"UnblendedCost\"]",
  "filters": "{\"Dimensions\": {\"Key\": \"SERVICE\", \"Values\": [\"Amazon DynamoDB\"]}}"
})

Monitor DynamoDB performance metrics:


usePower("aws-cost-optimization", "awslabs.cloudwatch-mcp-server", "get_metric_statistics", {
  "namespace": "AWS/DynamoDB",
  "metric_name": "ConsumedReadCapacityUnits",
  "dimensions": [{"Name": "TableName", "Value": "my-table"}],
  "start_time": "2024-11-01T00:00:00Z",
  "end_time": "2024-12-01T00:00:00Z",
  "period": 3600,
  "statistics": ["Sum", "Average"]
})

---

Optimization Strategies

1. Capacity Mode Selection

Strategy Overview:

Choose between On-Demand and Provisioned capacity based on traffic patterns and predictability.

On-Demand Mode:

**Best for**: Unpredictable workloads, new applications, sporadic traffic
**Pricing**: $1.25 per million write requests, $0.25 per million read requests
**Benefits**: No capacity planning, automatic scaling, pay-per-request

Provisioned Mode:

**Best for**: Predictable workloads, steady traffic patterns
**Pricing**: $0.000065 per WCU-hour, $0.00013 per RCU-hour
**Benefits**: Up to 70% cost savings for consistent workloads

Cost Comparison Analysis:


// Calculate break-even point between modes
usePower("aws-cost-optimization", "awslabs.aws-pricing-mcp-server", "get_pricing", {
  "service_code": "AmazonDynamoDB",
  "region": ["us-east-1"],
  "filters": [
    {"Field": "productFamily", "Value": "Database Storage", "Type": "EQUALS"}
  ]
})

Decision Framework:

**Use On-Demand if**: Traffic varies >50% day-to-day, new application, <40% utilization
**Use Provisioned if**: Steady traffic, >40% utilization, predictable patterns

2. Auto Scaling Configuration

Strategy Overview:

Optimize Provisioned mode with auto-scaling to handle traffic variations while minimizing costs.

Auto Scaling Best Practices:

**Target Utilization**: 70% for cost optimization, 40% for performance
**Scale-up**: Aggressive (double capacity)
**Scale-down**: Conservative (50% reduction, longer cooldown)

Implementation:


// Monitor auto-scaling effectiveness
usePower("aws-cost-optimization", "awslabs.cloudwatch-mcp-server", "get_metric_statistics", {
  "namespace": "AWS/DynamoDB",
  "metric_name": "ProvisionedReadCapacityUnits",
  "dimensions": [{"Name": "TableName", "Value": "my-table"}],
  "start_time": "2024-11-01T00:00:00Z",
  "end_time": "2024-12-01T00:00:00Z",
  "period": 3600,
  "statistics": ["Average", "Maximum"]
})

Optimization Techniques:

Set minimum capacity based on baseline traffic
Use predictive scaling for known traffic patterns
Monitor throttling events to adjust scaling policies
Implement application-level retry logic with exponential backoff

3. Read/Write Optimization

Strategy Overview:

Optimize read and write operations to minimize capacity unit consumption.

Write Optimization:

**Batch Operations**: Use BatchWriteItem for multiple items (up to 25 items)
**Conditional Writes**: Avoid unnecessary writes with condition expressions
**Item Size**: Keep items under 1KB to minimize WCU consumption
**Sparse Indexes**: Use GSI projections to reduce write amplification

Read Optimization:

**Eventually Consistent Reads**: Use when possible (50% cost reduction)
**Query vs Scan**: Always prefer Query over Scan operations
**Projection Expressions**: Retrieve only needed attributes
**Pagination**: Use proper pagination to avoid large result sets

Implementation Example:


// Monitor read/write patterns
usePower("aws-cost-optimization", "awslabs.cloudwatch-mcp-server", "get_metric_data", {
  "metric_data_queries": [
    {
      "id": "read_efficiency",
      "metric_stat": {
        "metric": {
          "namespace": "AWS/DynamoDB",
          "metric_name": "ConsumedReadCapacityUnits",
          "dimensions": [{"Name": "TableName", "Value": "my-table"}]
        },
        "period": 3600,
        "stat": "Sum"
      }
    },
    {
      "id": "write_efficiency",
      "metric_stat": {
        "metric": {
          "namespace": "AWS/DynamoDB",
          "metric_name": "ConsumedWriteCapacityUnits",
          "dimensions": [{"Name": "TableName", "Value": "my-table"}]
        },
        "period": 3600,
        "stat": "Sum"
      }
    }
  ],
  "start_time": "2024-11-01T00:00:00Z",
  "end_time": "2024-12-01T00:00:00Z"
})

4. Storage Class Optimization

Strategy Overview:

Use appropriate table classes based on access patterns to optimize storage costs.

Standard Class:

**Use for**: Frequently accessed data
**Cost**: $0.10 per GB-month
**Best for**: Active applications, real-time workloads

Standard Infrequent Access (Standard-IA):

**Use for**: Infrequently accessed data
**Cost**: Lower storage cost, higher access cost
**Best for**: Archive data, backup tables, historical records

Implementation:


// Analyze storage costs by table class
usePower("aws-cost-optimization", "awslabs.billing-cost-management-mcp-server", "cost_explorer", {
  "operation": "getCostAndUsage",
  "start_date": "2024-11-01",
  "end_date": "2024-12-01",
  "granularity": "MONTHLY",
  "group_by": "[{\"Type\": \"DIMENSION\", \"Key\": \"USAGE_TYPE\"}]",
  "metrics": "[\"UnblendedCost\"]",
  "filters": "{\"Dimensions\": {\"Key\": \"SERVICE\", \"Values\": [\"Amazon DynamoDB\"]}, \"And\": [{\"Dimensions\": {\"Key\": \"USAGE_TYPE\", \"Values\": [\"TimedStorage-ByteHrs\"]}}]}"
})

5. Global Tables Cost Management

Strategy Overview:

Optimize Global Tables configuration to balance availability requirements with cost implications.

Cost Considerations:

Each region incurs separate storage costs
Replicated write capacity units (rWCU) charged at $0.000975 per hour
Cross-region data transfer charges apply
TTL is not free with Global Tables

Optimization Techniques:

**Selective Replication**: Only replicate necessary tables
**Regional Optimization**: Place tables in regions closest to users
**Write Optimization**: Minimize cross-region writes
**Monitoring**: Track replication costs separately

Implementation:


// Monitor Global Tables costs
usePower("aws-cost-optimization", "awslabs.billing-cost-management-mcp-server", "cost_explorer", {
  "operation": "getCostAndUsage",
  "start_date": "2024-11-01",
  "end_date": "2024-12-01",
  "granularity": "MONTHLY",
  "group_by": "[{\"Type\": \"DIMENSION\", \"Key\": \"REGION\"}]",
  "metrics": "[\"UnblendedCost\"]",
  "filters": "{\"Dimensions\": {\"Key\": \"SERVICE\", \"Values\": [\"Amazon DynamoDB\"]}}"
})

6. Backup and Recovery Optimization

Strategy Overview:

Balance data protection requirements with backup costs through strategic backup configuration.

Backup Options:

**Point-in-Time Recovery (PITR)**: $0.20 per GB-month, 35-day retention
**On-Demand Backups**: $0.10 per GB-month, custom retention
**Cross-Region Backup**: Additional data transfer costs

Optimization Strategies:

Use PITR only for critical tables
Implement lifecycle policies for on-demand backups
Consider application-level backup strategies for cost-sensitive workloads
Use S3 export for long-term archival at lower cost

Cost Analysis:


// Monitor backup costs
usePower("aws-cost-optimization", "awslabs.billing-cost-management-mcp-server", "cost_explorer", {
  "operation": "getCostAndUsage",
  "start_date": "2024-11-01",
  "end_date": "2024-12-01",
  "granularity": "MONTHLY",
  "group_by": "[{\"Type\": \"DIMENSION\", \"Key\": \"API_OPERATION\"}]",
  "metrics": "[\"UnblendedCost\"]",
  "filters": "{\"Dimensions\": {\"Key\": \"SERVICE\", \"Values\": [\"Amazon DynamoDB\"]}, \"And\": [{\"Dimensions\": {\"Key\": \"API_OPERATION\", \"Values\": [\"BackupStorage\", \"RestoreTableFromBackup\"]}}]}"
})

---

Common Cost Pitfalls & Solutions

Pitfall 1: Using On-Demand for Steady Workloads

Problem Description:

Using On-Demand pricing for predictable, steady workloads results in 70% higher costs compared to Provisioned mode.

Detection:


// Analyze traffic consistency
usePower("aws-cost-optimization", "awslabs.cloudwatch-mcp-server", "get_metric_statistics", {
  "namespace": "AWS/DynamoDB",
  "metric_name": "ConsumedReadCapacityUnits",
  "dimensions": [{"Name": "TableName", "Value": "my-table"}],
  "start_time": "2024-11-01T00:00:00Z",
  "end_time": "2024-12-01T00:00:00Z",
  "period": 86400,
  "statistics": ["Average", "Maximum", "Minimum"]
})

Solution:

Switch to Provisioned mode for tables with <50% daily variation
Implement auto-scaling with 70% target utilization
Monitor for 2-4 weeks before making capacity adjustments

Pitfall 2: Over-Provisioning Capacity

Problem Description:

Provisioning more capacity than needed due to poor traffic analysis or fear of throttling.

Detection:


// Check capacity utilization
usePower("aws-cost-optimization", "awslabs.cloudwatch-mcp-server", "get_metric_data", {
  "metric_data_queries": [
    {
      "id": "utilization",
      "expression": "consumed / provisioned * 100",
      "label": "Capacity Utilization %"
    },
    {
      "id": "consumed",
      "metric_stat": {
        "metric": {
          "namespace": "AWS/DynamoDB",
          "metric_name": "ConsumedReadCapacityUnits"
        },
        "period": 3600,
        "stat": "Average"
      }
    },
    {
      "id": "provisioned",
      "metric_stat": {
        "metric": {
          "namespace": "AWS/DynamoDB",
          "metric_name": "ProvisionedReadCapacityUnits"
        },
        "period": 3600,
        "stat": "Average"
      }
    }
  ],
  "start_time": "2024-11-01T00:00:00Z",
  "end_time": "2024-12-01T00:00:00Z"
})

Solution:

Target 70% average utilization for cost optimization
Use auto-scaling to handle traffic spikes
Regularly review and adjust baseline capacity

Pitfall 3: Inefficient Query Patterns

Problem Description:

Using Scan operations instead of Query, or retrieving unnecessary data, leading to excessive capacity consumption.

Detection:

Monitor throttling events and high capacity consumption without corresponding business value.

Solution:

Always use Query instead of Scan when possible
Implement proper GSI design for access patterns
Use projection expressions to retrieve only needed attributes
Implement pagination for large result sets

---

Best Practices Summary

✅ Do:

**Choose the right capacity mode** based on traffic predictability
**Use auto-scaling** with 70% target utilization for cost optimization
**Optimize query patterns** - prefer Query over Scan operations
**Use eventually consistent reads** when strong consistency isn't required
**Monitor capacity utilization** regularly and adjust accordingly

❌ Don't:

**Use On-Demand for predictable workloads** - can cost 70% more
**Over-provision capacity** - target 70% utilization for cost efficiency
**Enable PITR on all tables** - only use for critical data
**Ignore Global Tables costs** - replication and cross-region charges add up
**Use Scan operations** when Query would work - much more expensive

🔄 Regular Review Cycle:

**Weekly:** Monitor capacity utilization and throttling events
**Monthly:** Review capacity mode decisions and auto-scaling effectiveness
**Quarterly:** Assess table class optimization and backup strategies
**Annually:** Review Global Tables necessity and regional optimization

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Service Code: AmazonDynamoDB

Last Updated: January 6, 2025

Review Cycle: Monthly