AWS DynamoDB Cost Calculator
Estimate DynamoDB-style cost using a simple on-demand-like model: read requests + write requests + storage. Compare baseline vs peak traffic with your effective pricing inputs.
Maintained by CloudCostKit Editorial Team. Last updated: 2026-01-28. Editorial policy and methodology.
Best next steps
Use this calculator for the first estimate, then validate the answer with the closest guide or companion tool.
Inputs
Results
Decide what kind of DynamoDB bill you are modeling
This page is strongest when you already know you want an on-demand-like, request-shaped estimate. If your table behaves more like provisioned capacity, reserved commitments, or global-table replication, treat this calculator as a directional view rather than a final answer.
- Measure reads and writes separately. The bill drifts quickly when one side changes and the other does not.
- Keep storage as a slower-moving baseline and request volume as the bursty driver.
- Note which features sit outside this estimate: backups, Streams, replication, export jobs, and transfer.
What usually moves the bill faster than expected
DynamoDB rarely surprises because of the table name alone. It surprises when the workload shape changes.
- Hot partitions: uneven keys can make write-heavy incidents much more expensive than average months suggest.
- Read amplification: chatty query patterns, scans, and over-fetching can make reads dominate unexpectedly.
- Storage growth: TTL assumptions, historical data retention, and large item payloads quietly push the baseline upward.
- Global features: Streams, global tables, and backups can exceed the core request estimate if used heavily.
How to check the estimate against AWS evidence
- Compare billed read and write activity with CloudWatch or billing-export counts for the same time window.
- Check whether the workload recently changed item size, query pattern, or key distribution.
- Review whether backup storage, export, Streams, or replication charges are being mistaken for core table spend.
- Run separate baseline and peak request cases so launch traffic does not disappear inside one blended monthly average.
Where to focus if one line item dominates
If writes dominate, inspect partition balance, write batching, and unnecessary write amplification. If reads dominate, inspect caching, query design, and scan-heavy access patterns. If storage dominates, the right follow-up is usually retention and growth control, not more tuning of request assumptions.
Next steps
Example scenario
- 2B reads/month, 500M writes/month, 200GB storage - estimate monthly request + storage charges.
- Peak 200% scenario shows what an incident month costs.
Included
- Read request cost from reads/month x $ per 1M requests.
- Write request cost from writes/month x $ per 1M requests.
- Storage cost from GB-month x $/GB-month.
- Optional RPS to monthly request estimator.
- Optional request mix presets.
- Baseline vs peak scenario table for request spikes.
Not included
- Provisioned capacity (RCU/WCU) modeling, auto scaling dynamics, and reserved capacity discounts.
- Streams, backups, data transfer, and monitoring/logging unless you model separately.
How we calculate
- Read cost = (reads per month / 1,000,000) x $ per 1M read requests.
- Write cost = (writes per month / 1,000,000) x $ per 1M write requests.
- Storage cost = GB-month x $ per GB-month.
- Total = read cost + write cost + storage cost.
FAQ
Should I use on-demand or provisioned pricing?
Does item size matter?
What about DynamoDB Streams and backups?
Related tools
Related guides
Disclaimer
Educational use only. Not legal, financial, or professional advice. Results are estimates based on the inputs and assumptions shown on this page. Verify pricing and limits with your providers and documentation.
Last updated: 2026-01-28. Reviewed against CloudCostKit methodology and current provider documentation. See the Editorial Policy .