Amazon DynamoDB Pricing: On-Demand & Provisioned Plans

DynamoDB provides flexible, cost-effective pricing options designed for database workloads of any scale. As a fully managed NoSQL database service, DynamoDB's pricing model is based on the actual resources your applications consume - you pay only for what you use, with no minimum commitments. This guide explains the core pricing components, helping data engineers and developers optimize costs while building highly available applications.

DynamoDB Pricing Models

DynamoDB provides two primary capacity modes:

1. On-Demand Capacity Mode

On-demand Capacity Mode lets you pay for actual database operations without capacity planning. DynamoDB automatically manages throughput capacity based on your workload's needs. This mode is ideal for:

• Applications with unpredictable workloads
• New applications where usage patterns are unknown
• Development and testing environments

Pricing Structure (US East Region)

• Write Request Units (WRU): $0.625 per million write request units
• Read Request Units (RRU): $0.125 per million read request units

Real-World DynamoDB Cost Example

Consider a data analytics application that processes user interaction events with varying workload patterns:

Application Characteristics:

• Daily base traffic: 5,000 reads and writes
• Weekly batch processing: 100,000 reads every Sunday
• Monthly data aggregation: 500,000 writes on the 1st of each month
• Average item size: 1 KB for writes, 2 KB for reads

Monthly Usage Analysis:

1. Regular Daily Operations:
   
   • Reads: 5,000 × 30 days = 150,000
   • Writes: 5,000 × 30 days = 150,000
2. Weekly Batch Processing:
   
   • Reads: 100,000 × 4 Sundays = 400,000
3. Monthly Aggregation:
   
   • Writes: 500,000 (once per month)

Total Monthly Operations:

• Total Reads: 550,000 (150,000 daily + 400,000 batch)
• Total Writes: 650,000 (150,000 daily + 500,000 aggregation)

Cost Calculation (US East Region):

1. Write Operations:
   
   • 0.65 million writes × $0.625 per million = $0.41
2. Read Operations:
   
   • 0.55 million reads × $0.125 per million = $0.07
3. Storage (15 GB average):
   
   • Free tier: First 25 GB
   • Storage cost: $0 (covered by free tier)

Total Monthly Cost: $0.48

• Write costs: $0.41
• Read costs: $0.07
• Storage: $0 (within the free tier)

This example demonstrates how on-demand pricing works well for applications with periodic batch processing and varying workload patterns while letting data engineers focus on data processing logic rather than capacity management.

2. Provisioned Capacity Mode

Provisioned Capacity Mode allows precise control over database throughput capacity with optional auto-scaling capabilities. This mode is ideal for applications with predictable workloads or when capacity requirements can be forecasted.

Pricing Structure (US East Region)

• Write Capacity Unit (WCU): $0.00065 per WCU per hour
• Read Capacity Unit (RCU): $0.00013 per RCU per hour

Example:

Consider a data pipeline application with predictable usage patterns:

Application Configuration:

• Region: US East (Ohio)
• Table Class: DynamoDB Standard
• Auto-scaling enabled (70% target utilization)
• Base capacity: 200 WCUs and 200 RCUs
• Item size: 1 KB for writes, 2 KB for reads

Usage Pattern:

• Morning ETL jobs (6 AM - 9 AM): 180 WCUs, 150 RCUs utilized
• Regular operations (9 AM - 6 PM): 100 WCUs, 120 RCUs utilized
• Evening reporting (6 PM - 10 PM): 80 WCUs, 190 RCUs utilized
• Night batch (10 PM - 6 AM): 40 WCUs, 40 RCUs utilized

Cost Calculation:

1. Base Capacity Cost (per hour):
   
   • WCUs: 200 × $0.00065 = $0.13
   • RCUs: 200 × $0.00013 = $0.026
   • Hourly base cost: $0.156
2. Monthly Capacity Cost:
   
   • Total hours: 730 (average month)
   • Monthly base cost: $0.156 × 730 = $113.88
3. Storage (50 GB average):
   
   • Free tier: First 25 GB
   • Billable storage: 25 GB × $0.25 = $6.25

Total Monthly Cost: $120.13

• Capacity costs: $113.88
• Storage costs: $6.25

Read/Write Capacity Units (RCU/WCU) Explained

• Read Capacity Unit (RCU): Represents one strongly consistent read per second for items up to 4 KB or two eventually consistent reads per second.
• Write Capacity Unit (WCU): Represents one write per second for items up to 1 KB.
• Larger item sizes multiply the required RCUs/WCUs. For instance, a 10 KB item read with strong consistency would consume 3 RCUs.

DynamoDB Cost Optimization Strategies

Reducing costs while maintaining performance is a priority for database engineers.

Auto-Scaling Configuration Best Practices

• Enable auto-scaling to adjust RCUs/WCUs dynamically.
• Set realistic minimum and maximum thresholds to prevent over-provisioning.

Choosing Between On-Demand and Provisioned Capacity

• Use on-demand for:
  
  • Startups or unpredictable traffic.
• Use provisioned for:
  
  • Predictable workloads to lock in lower rates.

Data Modeling for Cost Efficiency

• Design efficient schemas to minimize item size.
• Use sparse indexes to reduce storage costs.

Caching Strategies

• Implement DynamoDB Accelerator (DAX) or ElastiCache to offload read operations.
• Example: Reduce read costs by 50% by caching frequent queries.

DynamoDB Cost Monitoring and Control

Tracking and managing expenses ensure sustainable usage.

CloudWatch Metrics for Cost Tracking

• Monitor ConsumedCapacity and ProvisionedCapacity metrics.
• Use alarms to detect spikes in usage.

Setting Up Billing Alarms

Configure billing alerts to notify when spending exceeds predefined thresholds.

Cost Allocation Tags

Tag resources to analyze costs by project, department, or team.

Usage Patterns Analysis

Identify peak periods to adjust capacity or use reserved pricing options.

Common DynamoDB Pitfalls and Solutions

Avoiding common issues can lead to significant cost savings.

Hot Partition Problems and Solutions

• Issue: Overloading a single partition with disproportionate traffic.
• Solution:
  
  • Distribute traffic using a composite key.
  • Implement query randomization strategies.

Over-Provisioning Scenarios

• Issue: Allocating excessive RCUs/WCUs.
• Solution:
  
  • Regularly review capacity usage.
  • Enable auto-scaling.

Data Transfer Cost Traps

• Issue: High cross-region data transfer costs.
• Solution:
  
  • Consolidate regions when possible.
  • Use local reads to minimize cross-region traffic.

Backup and Restore

• Issue: Excessive snapshot costs.
• Solution: Optimize backup frequency and retention policies.

How can Airbyte help Optimize DynamoDB Query Costs?

1. Incremental Data Syncs

Instead of loading entire datasets during each sync, Airbyte’s DynamoDB connector supports incremental updates. This reduces the number of ReadCapacityUnits (RCUs) consumed by only fetching data changes since the last sync, minimizing query overhead.

2. Efficient Data Normalization

Airbyte provides a built-in normalization feature that converts nested JSON structures into relational tables. This reduces complex queries on DynamoDB by preprocessing data into a more query-efficient structure. Simplifying queries reduces the processing time and query costs.

3. Filtering and Selecting Specific Data

Airbyte allows data engineers to filter or select specific columns to sync, reducing the amount of data pulled from DynamoDB. This precise selection ensures that only necessary data is queried, further lowering read costs.

4. Compression and Deduplication

Airbyte can compress or deduplicate data during integration. This reduces the amount of redundant reads from DynamoDB, which directly translates to savings on read capacity.

5. Scheduling and Batch Operations

By allowing users to schedule data syncs at optimal intervals, Airbyte enables the consolidation of multiple small queries into fewer, larger ones. Batch operations are more cost-effective because they maximize data throughput for each query executed.

6. Leveraging Change Data Capture (CDC)

For applications where real-time updates are unnecessary, CDC can track changes in data and fetch only the updated records. This avoids frequent scans of the entire DynamoDB table and reduces query costs significantly.

7. Monitoring and Observability

Airbyte provides detailed logging and monitoring capabilities. This allows users to identify inefficient query patterns or excessive reads in real-time and optimize their sync strategies accordingly.

8. On-Premise and Cloud Support

With its flexibility to run on both Airbyte Cloud and on-premises, the platform ensures that the data transfer doesn't incur additional infrastructure costs, especially for users already operating within AWS.

Practical Example

Scenario: A DynamoDB Table for E-Commerce Orders

• Without optimization, syncing all orders daily would result in high RCUs due to full table scans.
• By using Airbyte’s incremental sync, only new or updated orders are queried, drastically cutting the number of RCUs consumed.

By leveraging Airbyte’s features, teams can ensure that DynamoDB remains performant while keeping operational costs in check.

Conclusion

Understanding DynamoDB's pricing components enables data teams to make informed decisions about capacity planning and cost optimization. By choosing between on-demand and provisioned capacity modes, leveraging auto-scaling, and implementing storage optimization best practices, organizations can efficiently manage database costs while maintaining performance. For specific workload cost estimates, use the AWS Pricing Calculator or contact AWS support.