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DynamoDB vs Aurora is not purely a cost decision. DynamoDB primarily prices requests or provisioned throughput plus storage. Aurora prices compute, storage, and, with Aurora Standard, I/O. Because they meter different resources, there is no universal request-volume threshold where one database always becomes cheaper.
DynamoDB vs Aurora comparison
| Dimension | DynamoDB | Aurora |
|---|---|---|
| Data model | Key-value and document | Relational |
| Query model | Keys, sort keys, indexes | SQL, joins, aggregations |
| Scaling | Managed horizontal scaling | Provisioned or serverless compute |
| Pricing basis | Requests or capacity + storage | Compute + storage + I/O |
| Transactions | ACID with DynamoDB constraints | Relational ACID transactions |
| Multi-Region | Multi-active Global Tables | Global Database with primary writer Region |
| Best fit | APIs, sessions, carts, metadata | Orders, billing, inventory, reporting |
How does DynamoDB vs Aurora pricing work?
DynamoDB pricing
DynamoDB provides on-demand and provisioned capacity modes.AWS’s US East (N. Virginia) DynamoDB Standard examples currently use $0.625 per million write request units and $0.125 per million read request units for the strongly consistent 1 KB read example. Standard storage is $0.25 per GB-month after applicable free-tier storage. The Amazon DynamoDB pricing page explains the current request-unit, storage, table-class, and capacity-mode rules.
AWS also advertises up to 18% savings on eligible on-demand throughput and 12% on provisioned throughput through Database Savings Plans. Reserved provisioned capacity can provide up to 54% savings for one year or 77% for three years on eligible capacity.
For deeper FinOps guidance, see our DynamoDB reserved capacity guide and DynamoDB transactions pricing guide.
Aurora pricing
Aurora Standard charges separately for compute, storage, and database I/O. AWS’s current US East examples use $0.10 per GB-month for Standard storage and $0.20 per million I/O operations. See the Amazon Aurora pricing documentation for current rates and configuration-specific charges.Aurora I/O-Optimized removes read and write I/O charges but uses higher compute and storage pricing. AWS recommends evaluating it for I/O-intensive workloads, particularly when I/O represents more than 25% of Aurora database spend.
For broader relational TCO considerations, see our RDS vs Aurora cost comparison.
How should you model DynamoDB vs Aurora cost?
Do not map one DynamoDB request directly to one Aurora I/O. The services meter different workload characteristics.Practical workload example
Assume an application performs 10 million 1 KB writes and 50 million strongly consistent 1 KB reads per month.This is not a complete DynamoDB bill. Storage, backups, GSIs, transactions, replication, and other features can increase the total.
The same 60 million application operations cannot produce a reliable Aurora estimate without knowing the SQL queries, required compute, cache hit rate, storage I/O, concurrency, and replica configuration.
When should you choose DynamoDB?
Choose DynamoDB when your workload has well-defined access patterns and does not depend heavily on arbitrary relational queries.For multi-Region deployments, also model replicated writes, storage in each Region, and data transfer. Our DynamoDB Global Tables pricing guide covers those additional cost dimensions.
DynamoDB Global Tables are multi-active. Multi-Region eventual consistency (MREC) is the default consistency mode. Multi-Region strong consistency (MRSC) is available only for supported Region sets and configurations.
AWS’s Global Tables consistency guidance notes that MRSC requires exactly three participating Regions, using three replicas or two replicas plus a witness. MRSC also introduces higher write and strongly consistent read latency and does not support DynamoDB transaction APIs.
When should you choose Aurora?
Choose Aurora when your application depends on SQL joins, relational constraints, complex transactions, ad hoc querying, reporting, or PostgreSQL/MySQL compatibility.Aurora Serverless v2 can scale to 0 ACU and automatically pause when supported engine versions are configured with zero minimum ACUs.
That capability is configuration-dependent. Open connections, certain replication configurations, maintenance activities, and unsupported engine versions can prevent auto-pause. AWS’s Aurora Serverless v2 auto-pause guidance documents the current prerequisites and restrictions.
When should you use DynamoDB and Aurora together?
Use both when different application paths have different access requirements.A CQRS-style architecture can use DynamoDB for high-volume operational writes with defined access patterns and Aurora for complex relational queries or reporting. DynamoDB Streams and Lambda can synchronize data into an Aurora query model.
AWS’s CQRS guidance for DynamoDB and Aurora documents this architecture and notes that it typically introduces eventual consistency between the two stores.
The trade-off is additional synchronization logic, data duplication, failure handling, and operational complexity.
How to choose DynamoDB or Aurora step by step
Document critical access patterns. List reads, writes, joins, transactions, and reporting queries.
Identify relational requirements. Required joins or relational integrity can determine the architecture before cost analysis.
Model each service separately. Calculate DynamoDB capacity and Aurora compute, storage, I/O, and replicas.
Test realistic traffic. Include item sizes, query complexity, caching, concurrency, and regional behavior.
Evaluate a hybrid design. Use both only when separate workload requirements justify the added complexity.
Evaluate commitments last. Right-size the architecture before purchasing discounted capacity.
DynamoDB vs Aurora selection checklist
Are the critical access patterns known in advance?
Does the application require SQL joins?
Do transactions span complex relational data?
Are local writes required in multiple Regions?
Have GSIs, backups, and replication been included in DynamoDB cost?
Have Aurora compute, replicas, cache behavior, and I/O been modeled?
Would DynamoDB and Aurora together better separate operational and query workloads?
Has the workload been right-sized before commitments are considered?
How do AWS database commitments affect the decision?
AWS Database Savings Plans reduce eligible database costs by up to 35% in exchange for a consistent hourly usage commitment over a one-year term. AWS says eligible coverage can follow supported workloads across engines, instance families, deployment options, and Regions. See the AWS Database Savings Plans pricing page for current coverage and rates.Database Savings Plans and DynamoDB reserved capacity or RDS Reserved Instances cannot stack on the same workload.
Our AWS Database Savings Plans guide explains commitment sizing, eligible usage, and overcommitment considerations.
How Usage.ai helps manage DynamoDB and Aurora commitments
Architecture should come first. Usage.ai does not change whether DynamoDB or Aurora is technically right for an application. Our role begins after teams understand their workload and eligible commitment baseline.At Usage.ai, we continuously analyze AWS usage across your cloud environment to identify commitment opportunities and optimize coverage as workloads evolve.
With Flex Insured Commitments, teams can typically target 30–40% savings on eligible AWS database and analytics workloads, depending on the service, commitment type, usage profile, and current program eligibility.
If an eligible commitment costs more than the equivalent On-Demand usage, our Cashback Protection can cover the difference, subject to applicable eligibility and program terms. This helps organizations pursue commitment savings while retaining flexibility as workloads evolve.
For DynamoDB and Aurora specifically, we can help analyze eligible spend, size commitments, monitor utilization, and continuously reassess coverage as database usage changes.
Our AWS Database Savings Plans guide explains eligibility, commitment sizing, and overcommitment considerations in more detail.
Bottom line
Choose the database architecture before choosing the discount.DynamoDB is generally the stronger fit for predictable key-value access patterns and horizontally scaled operational workloads. Aurora is the stronger fit for relational SQL and complex queries. A hybrid architecture can be appropriate when the same application genuinely needs both patterns.
Frequently asked questions
Is DynamoDB cheaper than Aurora?
Neither is universally cheaper. DynamoDB cost depends on request or provisioned capacity, storage, indexes, replication, and optional features. Aurora depends on compute, storage, I/O configuration, replicas, and utilization.
Is DynamoDB faster than Aurora?
DynamoDB is designed for predictable single-digit millisecond performance for supported key-value and document access patterns. Aurora performance depends on SQL complexity, indexing, compute, concurrency, and caching.
Can DynamoDB replace Aurora?
Only when required access patterns can be implemented efficiently without the relational capabilities the application needs. Migration usually requires data-model and application-access changes.
Does Aurora Serverless v2 scale to zero?
Yes, on supported engine versions and configurations. The cluster must allow a minimum of 0 ACU, and certain connections or features can prevent automatic pause.
Can DynamoDB and Aurora be used together?
Yes. A CQRS or polyglot-persistence architecture can use DynamoDB for high-volume operational paths and Aurora for relational queries, provided the team accepts synchronization complexity and potential eventual consistency.
