Amazon DynamoDB Pricing: On-Demand & Provisioned Plans

Data professionals managing Amazon DynamoDB workloads recently witnessed the most significant pricing transformation in the service's history. In November 2024, AWS slashed on-demand throughput costs and reduced Global Tables replication expenses, fundamentally altering the cost-benefit equation for NoSQL deployments. These changes, combined with new features like warm throughput management and configurable maximum capacity, create unprecedented opportunities for organizations to optimize their database expenses while scaling performance. Understanding these evolving pricing structures becomes critical as enterprises migrate from legacy systems and seek to balance cost efficiency with operational flexibility.

What Are the Core DynamoDB Pricing Models?

DynamoDB provides two primary capacity modes, each optimized for different workload patterns and cost-management strategies.

On-Demand Capacity Mode

On-Demand Capacity Mode eliminates capacity planning by automatically scaling throughput based on application demand. Following the November 2024 price reductions, this serverless option became significantly more cost-effective, making it the preferred choice for most variable workloads. This mode excels for:

• Applications with unpredictable traffic patterns
• New applications where usage patterns remain unknown
• Development and testing environments requiring cost flexibility
• Workloads experiencing seasonal or event-driven spikes

Current Pricing Structure (US East)

After the reduction implemented in late 2024:

• Write Request Units (WRU): Reduced pricing per million write request units
• Read Request Units (RRU): Reduced pricing per million read request units

Real-World DynamoDB Cost Example

Consider a data-analytics application processing user-interaction events with varying workload patterns:

Application characteristics

• Daily base traffic: Regular reads and writes
• Weekly batch processing: Additional reads every Sunday
• Monthly data aggregation: Additional writes on the first of each month
• Average item size: Standard sizes for writes and reads

Monthly usage analysis

• Total Reads: Combined Read Volume
• Total Writes: Combined Write Volume

Cost calculation

• Writes: Volume × Rate = Monthly Write Cost
• Reads: Volume × Rate = Monthly Read Cost
• Storage: Data Volume — first portion free → Storage Cost

Total monthly cost: Combined Monthly Cost

This example demonstrates how the revised on-demand pricing suits applications with varying workload patterns while enabling engineers to focus on data-processing logic rather than capacity forecasting.

Provisioned Capacity Mode

Provisioned Capacity Mode provides granular control over database throughput with predictable cost structures. This mode remains optimal for applications with consistent, forecastable workloads where capacity planning delivers cost advantages over on-demand pricing.

Pricing Structure (US East)

• Write Capacity Unit (WCU): Hourly pricing per WCU-hour
• Read Capacity Unit (RCU): Hourly pricing per RCU-hour

Provisioned Capacity Example

Application configuration

• Region: US East (Ohio)
• Table class: DynamoDB Standard
• Auto-scaling enabled (target utilization)
• Base capacity: Configured WCUs and RCUs
• Item size: Standard writes and reads

Usage pattern

Cost calculation

• Base capacity per hour
• WCUs: Units × Rate = Hourly WCU Cost
• RCUs: Units × Rate = Hourly RCU Cost
• Hourly total: Combined Hourly Cost
• Monthly capacity cost
• Hours × Rate = Monthly Capacity Cost
• Storage
• Data Size – first portion free → Calculated Storage Cost

Total monthly cost: Combined Monthly Cost

How Do Read and Write Capacity Units Function?

Understanding DynamoDB's capacity-unit system enables precise cost estimation and performance optimization:

• Read Capacity Unit (RCU): Supports one strongly consistent read per second for items up to a standard size, or two eventually consistent reads for the same size.
• Write Capacity Unit (WCU): Handles one write operation per second for items up to a standard size.

Larger items consume proportionally more capacity units. A larger strongly consistent read requires multiple RCUs (rounded up from the calculated amount), while a larger write operation needs multiple WCUs. This mathematical relationship directly impacts cost calculations and performance planning for data-intensive applications.

What Are the Most Effective DynamoDB Cost-Optimization Strategies?

Auto-Scaling Configuration Best Practices

Enable auto-scaling to dynamically adjust capacity based on actual demand while maintaining cost efficiency. Configure realistic minimum and maximum thresholds to prevent over-provisioning during low-traffic periods while ensuring adequate capacity for peak loads.

Choosing Between On-Demand and Provisioned Capacity

Following the 2024 price reductions, on-demand capacity became cost-competitive for most workloads with variable traffic patterns. Provisioned capacity remains advantageous for steady-state applications where consistent usage patterns enable accurate capacity forecasting and reserved-capacity discounts.

Data Modeling for Cost Efficiency

Design efficient schemas that minimize item sizes and optimize query patterns. Implement sparse indexes to reduce storage costs while maintaining query performance. Consider denormalization strategies that reduce the number of read operations required for common access patterns.

Caching Strategies

Implement DynamoDB Accelerator (DAX) or ElastiCache integration to offload frequent read operations from your primary tables. Effective caching strategies can reduce read costs significantly while improving application response times and reducing load on DynamoDB partitions.

How Can You Monitor and Control DynamoDB Costs Effectively?

CloudWatch Metrics for Cost Tracking

Monitor ConsumedCapacity and ProvisionedCapacity metrics to identify optimization opportunities. Track throttling events and capacity-utilization patterns to inform scaling decisions and capacity-mode selections.

Setting Up Billing Alarms

Configure AWS billing alerts to receive notifications when spending exceeds predefined thresholds. Implement graduated alert levels to provide early warnings before costs become problematic.

Usage-Pattern Analysis

Analyze historical usage data to identify peak periods and optimize capacity allocation. Use this analysis to evaluate reserved-pricing options and determine optimal scaling policies for auto-scaling configurations.

Advanced Cost Optimization with Recent DynamoDB Features

Warm Throughput Management

The November 2024 release introduced warm throughput capabilities, providing real-time visibility into available burst capacity. This feature enables proactive scaling before anticipated traffic surges, preventing costly reactive adjustments during peak periods.

Configurable Maximum Throughput

On-demand tables now support maximum throughput ceilings per table or secondary index, preventing unexpected cost overruns during traffic explosions. When limits are reached, requests are throttled rather than consuming unlimited capacity, providing cost predictability for variable workloads.

Global Tables Cost Optimization

Global Tables pricing saw dramatic reductions in 2024, with replicated-write costs dropping substantially for on-demand and provisioned capacity. These changes make multi-region deployments significantly more cost-effective for applications requiring global distribution and disaster-recovery capabilities.

What Are Common DynamoDB Pitfalls and Their Solutions?

Hot-Partition Problems

Uneven key distribution creates hot partitions that experience throttling despite adequate overall table capacity. Implement composite keys that distribute traffic evenly across partitions or redesign access patterns to avoid concentrated traffic on specific partition-key values.

Over-Provisioning Scenarios

Excessive capacity allocation inflates costs without corresponding performance benefits. Review usage patterns regularly using CloudWatch metrics, enable auto-scaling with appropriate target-utilization percentages, and consider switching to on-demand capacity for workloads with unpredictable traffic patterns.

Data-Transfer Cost Traps

Cross-region data-transfer fees can accumulate unexpectedly in Global Tables deployments or when applications access DynamoDB from distant regions. Optimize region placement to minimize transfer costs and implement local read patterns where eventual consistency is acceptable.

Backup and Restore Expenses

Continuous backups and point-in-time recovery features include storage costs that can become significant for large datasets. Optimize backup-retention policies based on compliance requirements and business-recovery objectives.

Integration with Open-Source Data Platforms for Cost Optimization

Leveraging Airbyte for Efficient DynamoDB Integration

Open-source data-integration platforms like Airbyte provide sophisticated mechanisms for optimizing DynamoDB costs while enabling comprehensive data workflows. Airbyte's DynamoDB connector implements incremental synchronization using DynamoDB Streams for Change Data Capture, reducing read-capacity consumption significantly compared to full-table scans.

Strategic Integration Patterns

Implement CDC-based patterns using DynamoDB Streams to capture item-level changes within the retention windows. Configure batch optimization through compression and intelligent chunking to stay within DynamoDB's item limits while maximizing throughput efficiency.

Enterprise-Scale Cost Management

For enterprise deployments, implement multi-account replication strategies using IAM role chaining to avoid credential sharing while enabling cross-account data pipelines. Leverage post-load transformation capabilities through dbt integration to optimize data structures for DynamoDB access patterns.

How Can Airbyte Help Optimize DynamoDB Query Costs?

1. Incremental Data Syncs – Fetch only changed data via DynamoDB Streams, dramatically reducing RCU consumption.
2. Efficient Data Normalization – Convert nested JSON structures to relational formats, minimizing read operations for downstream analytics.
3. Filtering & Column Selection – Sync only required fields and apply row-level filtering to reduce unnecessary reads and data-transfer costs.
4. Compression & Deduplication – Minimize redundant reads during integration processes while optimizing batch operations.
5. Scheduling & Batch Operations – Consolidate multiple small queries into fewer, larger operations that maximize throughput efficiency.
6. Change Data Capture Implementation – Avoid costly full-table scans when real-time synchronization requirements don't demand immediate consistency.
7. Monitoring & Observability – Detailed operational logs identify inefficient query patterns and optimization opportunities.
8. Flexible Deployment Options – Deploy on Airbyte Cloud, self-managed infrastructure, or hybrid configurations to optimize data-processing costs.

Practical Implementation Example

An e-commerce platform originally performed daily full-table scans of order data, consuming substantial RCUs during batch-processing windows. By implementing Airbyte's incremental-sync capabilities with DynamoDB Streams, the organization reduced read-capacity consumption substantially while achieving near real-time data availability for analytics workflows.

Conclusion

DynamoDB's recent pricing evolution, highlighted by the November 2024 cost reductions and enhanced capacity-management features, fundamentally improves the value proposition for organizations seeking scalable NoSQL solutions. Effective cost optimization requires understanding the interplay between capacity modes, leveraging new features like Warm Throughput Monitoring, and implementing strategic integrations with platforms like Airbyte that reduce operational overhead while maintaining performance.

For organizations evaluating DynamoDB deployments or seeking to optimize existing implementations, focus on workload-appropriate capacity-mode selection, strategic use of new cost-management features, and integration patterns that maximize operational efficiency. The enhanced pricing structure and expanded feature set position DynamoDB as a compelling foundation for modern data architectures requiring both cost efficiency and operational flexibility.

Frequently Asked Questions

When should I choose on-demand vs. provisioned capacity mode?

On-demand is generally best for unpredictable or spiky workloads where traffic patterns are hard to forecast. Provisioned capacity is better suited for stable, predictable workloads where capacity planning can lock in cost savings.

What are the main drivers of DynamoDB costs?

The biggest factors are read and write capacity consumption, data storage, backups, and Global Tables replication. Network transfer fees and hot-partition inefficiencies can also create unexpected costs.

How can Airbyte reduce DynamoDB costs?

Airbyte uses incremental syncs via DynamoDB Streams to avoid expensive full-table scans. It also supports filtering, column selection, batching, and compression to lower read costs while ensuring efficient downstream analytics integration.

Suggested Read: IBM db2 Pricing