Choosing between DynamoDB on-demand vs provisioned capacity can make or break your application’s performance and budget. This guide is for developers, architects, and DevOps teams who need to understand how DynamoDB capacity models work and which option fits their specific workload requirements.
We’ll break down how DynamoDB read write capacity units directly impact your database performance and response times. You’ll learn the real performance trade-offs of on-demand pricing DynamoDB versus the optimization potential of provisioned throughput DynamoDB. We’ll also walk through practical DynamoDB performance optimization strategies and show you how to match your capacity choice to actual usage patterns for better DynamoDB performance tuning results.
Understanding DynamoDB Capacity Models
Core differences between On-Demand and Provisioned capacity
On-Demand capacity automatically scales read and write capacity units based on your application’s traffic patterns, charging per request without requiring capacity planning. Provisioned capacity requires you to specify exact read and write capacity units upfront, offering predictable costs but demanding accurate traffic forecasting. On-Demand handles sudden traffic spikes seamlessly, while Provisioned capacity can throttle requests when limits are exceeded, though it supports auto-scaling to adjust capacity based on CloudWatch metrics.
When each model makes financial sense for your application
Provisioned capacity delivers significant cost savings for applications with predictable, consistent traffic patterns, especially when utilization remains above 40-50% of provisioned capacity. On-Demand pricing works best for new applications with unknown traffic patterns, sporadic workloads, or applications experiencing unpredictable usage spikes. Applications running 24/7 with steady throughput typically see 60-80% cost reductions with Provisioned capacity, while seasonal or event-driven applications benefit from On-Demand’s pay-per-use model without capacity waste.
Key performance implications of your capacity choice
DynamoDB capacity models directly impact application performance through throttling behavior and latency patterns. Provisioned throughput can cause throttling when traffic exceeds configured capacity units, leading to exponential backoff delays and potential application timeouts. On-Demand capacity provides consistent single-digit millisecond latency without throttling concerns, though it may experience brief initialization delays during sudden traffic bursts. Auto-scaling with Provisioned capacity introduces 1-15 minute adjustment delays, while On-Demand scales instantly, making capacity choice critical for DynamoDB performance optimization strategies.
How Read and Write Capacity Units Drive Performance
Breaking down Read Capacity Units and their impact on query speed
DynamoDB read write capacity units directly control how fast your applications can retrieve data. Each Read Capacity Unit (RCU) supports one strongly consistent read per second for items up to 4KB, or two eventually consistent reads. Larger items consume multiple RCUs proportionally – a 12KB item requires 3 RCUs for strongly consistent reads. Query performance scales linearly with allocated RCUs, meaning more capacity equals faster response times when handling concurrent requests.
Understanding Write Capacity Units and throughput limitations
Write Capacity Units (WCUs) determine your application’s data insertion and modification speed. One WCU handles a single write operation per second for items up to 1KB. Items exceeding 1KB consume additional WCUs based on size – a 3KB item uses 3 WCUs. Write throughput limitations become apparent during peak traffic when insufficient WCUs cause request queuing, directly impacting user experience and application responsiveness.
Calculating your actual capacity needs for optimal performance
Accurate DynamoDB capacity planning requires analyzing your application’s access patterns, item sizes, and traffic distribution. Start by measuring average item sizes and peak request rates across different time periods. Factor in read consistency requirements since strongly consistent reads consume double the RCUs compared to eventually consistent reads. Include burst capacity considerations – DynamoDB provides brief periods of higher throughput, but sustained traffic requires proper provisioning.
Avoiding throttling issues that slow down your applications
Throttling occurs when requests exceed provisioned throughput capacity, causing DynamoDB to reject operations and forcing applications to retry. This creates cascading performance issues including increased latency, timeout errors, and poor user experience. Monitor CloudWatch metrics for throttled requests and implement exponential backoff with jitter in your application code. DynamoDB auto scaling helps prevent throttling by automatically adjusting capacity based on traffic patterns, but proper initial capacity planning remains essential for consistent performance.
On-Demand Capacity Benefits and Performance Trade-offs
Automatic scaling eliminates capacity planning headaches
DynamoDB on-demand capacity automatically adjusts to your application’s traffic patterns without manual intervention. You don’t need to predict peak loads or configure auto-scaling policies. The service handles sudden traffic bursts seamlessly, scaling up within milliseconds when demand increases and scaling down during quiet periods. This eliminates the complex capacity planning process that provisioned mode requires, where you must analyze usage patterns and set appropriate read write capacity units.
Pay-per-request pricing reduces costs for unpredictable workloads
On-demand pricing charges only for actual requests consumed, making it cost-effective for applications with sporadic or unpredictable traffic. Unlike provisioned capacity where you pay for allocated throughput regardless of usage, DynamoDB on-demand vs provisioned pricing models show significant savings for workloads with variable demand. Applications experiencing irregular traffic spikes, development environments, or new services with uncertain growth patterns benefit most from this pay-as-you-go approach.
Performance consistency during traffic spikes
On-demand capacity maintains consistent performance even during unexpected traffic surges. The service automatically provisions additional capacity within milliseconds to handle increased load without throttling requests. This DynamoDB performance optimization ensures your applications remain responsive during viral content moments, flash sales, or other traffic spikes. Unlike provisioned mode where sudden increases can trigger throttling until auto-scaling kicks in, on-demand provides immediate capacity expansion.
Latency considerations compared to provisioned capacity
While DynamoDB capacity models both deliver single-digit millisecond latency, on-demand may experience slightly higher latency during initial scaling events. The first request to a new partition region might take a few extra milliseconds as capacity is allocated. Provisioned capacity with pre-warmed throughput typically provides more predictable latency patterns. However, for most applications, this minimal latency difference is negligible compared to the operational benefits of automatic scaling and simplified DynamoDB capacity planning.
Provisioned Capacity Optimization Strategies
Setting Baseline Capacity for Predictable Performance
Establishing the right baseline capacity requires analyzing your application’s traffic patterns and performance requirements. Start by monitoring your current read and write capacity consumption during peak and off-peak hours over several weeks. Set your baseline 20-30% above average consumption to handle traffic spikes without throttling. For applications with predictable daily patterns, configure different baselines for business hours versus overnight periods. Use CloudWatch metrics to identify consumption trends and adjust your baseline accordingly. Remember that underprovisioning leads to throttling, while overprovisioning wastes money – finding the sweet spot requires continuous monitoring and adjustment based on real usage data.
Auto-scaling Configurations That Prevent Bottlenecks
DynamoDB auto scaling dynamically adjusts your provisioned throughput capacity based on actual traffic patterns. Configure target utilization between 70-80% to maintain headroom for sudden spikes while avoiding unnecessary scaling events. Set minimum capacity units to prevent scaling too low during quiet periods, and establish maximum limits to control costs. The scaling cooldown periods should be shorter for scale-up (60 seconds) and longer for scale-down (15 minutes) to respond quickly to increased demand while preventing oscillation. Monitor CloudWatch alarms for consumed capacity and throttling events to fine-tune your auto scaling policies and ensure smooth performance during traffic fluctuations.
Reserved Capacity Savings for Long-term Cost Efficiency
Reserved capacity offers significant cost savings for stable, predictable workloads by allowing you to pre-purchase capacity units at discounted rates. Purchase reserved capacity for your baseline consumption levels – typically 80-90% of your minimum required capacity – while letting auto scaling handle traffic spikes with on-demand pricing. Reserved capacity provides up to 53% savings compared to provisioned capacity, making it ideal for production workloads with consistent traffic patterns. Analyze your capacity consumption history over 3-6 months to identify stable baseline requirements. Mix reserved capacity for predictable loads with auto scaling for variable traffic to optimize both performance and costs effectively.
Real-World Performance Scenarios and Capacity Impact
E-commerce applications with seasonal traffic patterns
E-commerce platforms face dramatic traffic spikes during Black Friday, holiday seasons, and flash sales. Provisioned capacity with auto scaling works best here, allowing you to pre-scale before expected peaks while maintaining cost control during quiet periods. Set your base capacity at 20-30% of peak demand and configure aggressive scaling policies that can double capacity within 2-4 minutes. On-demand pricing becomes expensive during sustained high-traffic events, making provisioned throughput the smart choice for predictable seasonal patterns.
IoT data ingestion requiring consistent write performance
IoT sensors generate steady streams of telemetry data that demand predictable write performance without throttling. Provisioned capacity delivers consistent write capacity units at lower costs for continuous ingestion workloads. Configure write capacity based on your peak sensor count multiplied by data frequency – if 1000 sensors send data every 30 seconds with 1KB payloads, provision 35-40 WCUs to handle bursts safely. On-demand works for development environments or irregular IoT deployments but becomes costly for production-scale continuous ingestion.
Analytics workloads with batch processing requirements
Batch analytics jobs need high read throughput during specific processing windows, then remain idle for hours. On-demand capacity shines here since you only pay for actual read capacity units consumed during job execution. A nightly ETL process might consume 500 RCUs for 2 hours then drop to zero – on-demand eliminates paying for unused provisioned capacity during idle periods. For frequent batch jobs running multiple times daily, provisioned capacity with scheduled scaling provides better cost optimization.
Gaming applications needing low-latency read operations
Gaming applications require sub-10ms response times for leaderboards, player profiles, and matchmaking data. Provisioned capacity delivers consistent performance by reserving dedicated read capacity units, preventing throttling during peak gaming hours. Configure your read capacity 50% above average usage to handle player activity spikes without latency degradation. On-demand can introduce unpredictable response times during traffic surges, making provisioned throughput essential for competitive gaming experiences where milliseconds matter.
Making the Right Capacity Choice for Your Use Case
Workload analysis framework for capacity model selection
Start by examining your application’s traffic patterns over different time periods – daily, weekly, and seasonal variations tell you whether your workload is predictable or sporadic. On-demand capacity works best for unpredictable workloads with sudden spikes, while provisioned capacity suits applications with steady, consistent traffic patterns. Calculate your average read and write capacity requirements by analyzing historical CloudWatch metrics, then determine if your peak usage exceeds average by more than 2x – this threshold often indicates on-demand is more cost-effective. Consider your application’s tolerance for occasional throttling, as provisioned capacity can experience brief throttling during unexpected spikes, whereas on-demand automatically scales to handle traffic bursts.
Migration strategies between On-Demand and Provisioned modes
DynamoDB allows switching between capacity modes once every 24 hours, making migration straightforward but requiring careful planning. When moving from provisioned to on-demand, monitor your costs closely during the first month as billing shifts from reserved capacity to actual consumption – unexpected usage patterns can significantly impact expenses. Migrating from on-demand to provisioned requires analyzing recent consumption data to set appropriate read and write capacity units, starting with 125% of your average requirements to account for traffic variations. Enable auto-scaling immediately after switching to provisioned mode, setting target utilization between 70-80% to balance cost optimization with performance reliability.
Monitoring tools that reveal capacity performance insights
CloudWatch provides essential DynamoDB capacity metrics including consumed read/write capacity units, throttled requests, and system errors that directly impact your DynamoDB performance optimization strategy. The ConsumedReadCapacityUnits and ConsumedWriteCapacityUnits metrics show actual usage patterns, helping you understand whether provisioned throughput DynamoDB settings match real demand. DynamoDB Contributor Insights identifies hot partitions and access patterns that cause performance bottlenecks, while AWS X-Ray traces request flows to pinpoint capacity-related latency issues. Third-party monitoring solutions like DataDog and New Relic offer enhanced visualization and alerting capabilities, making it easier to spot capacity trends that affect your DynamoDB capacity planning decisions and overall application performance.
DynamoDB’s capacity model choice can make or break your application’s performance and budget. On-demand offers the flexibility to handle unpredictable traffic without capacity planning, while provisioned capacity gives you cost control and consistent performance for steady workloads. The key is understanding how read and write capacity units directly impact your database’s responsiveness and your monthly bill.
Your specific use case should drive this decision. If you’re building an application with sporadic traffic or just getting started, on-demand removes the guesswork and scales automatically. For established applications with predictable patterns, provisioned capacity with auto-scaling can deliver better performance at lower costs. Take time to analyze your traffic patterns, budget constraints, and performance requirements before committing to either model – switching later is possible, but getting it right from the start saves both headaches and money.
