Amazon RDS vs DynamoDB: Differences, Cost & When to Use Each
Choosing between Amazon RDS and Amazon DynamoDB affects how you model data, query it, scale the application, and control cost. RDS is relational, while DynamoDB is a NoSQL database designed around keys and known access patterns.
For teams comparing Amazon RDS vs DynamoDB, this guide focuses on the architecture, performance, and cost trade-offs that determine the right fit.
Quick summary
- Amazon RDS fits relational workloads that need SQL, joins, constraints, and flexible queries.
- DynamoDB fits known, key-based access patterns that benefit from serverless operations and horizontal scaling.
- The Amazon RDS vs DynamoDB pricing comparison depends on workload shape: Amazon RDS is driven mainly by provisioned resources, while DynamoDB cost is driven by requests or capacity, storage, indexes, transactions, and replication.
- Some applications benefit from both: Amazon RDS for relational data and DynamoDB for predictable, high-volume access patterns.
Both are managed AWS database services, but their different data models shape querying, scaling, operations, and cost.
RDS vs DynamoDB: Quick comparison for architecture decisions
| Area | Amazon RDS | DynamoDB |
| Database model | Relational | NoSQL key-value and document |
| Best fit | Related data with flexible or changing query needs | Predictable, key-based access patterns at scale |
| Queries | SQL with joins, aggregations, and flexible filtering | Key-based queries through primary and secondary indexes; scans are also possible |
| Transactions | ACID transactions with relational constraints | ACID transactions across up to 100 items per transaction |
| Scaling | Scale compute and storage; add read replicas for read-heavy workloads | Automatically partitions data; uses on-demand or provisioned throughput |
| Operations | Managed database, but sizing and tuning still matter | Serverless; no instances to manage, but key and capacity design still matter |
| Cost behavior | Driven mainly by compute, storage, I/O, availability, and licensing | Driven mainly by requests or capacity, storage, indexes, and replication |
| Main design risk | Oversizing or hitting relational scaling constraints | Poor key/index design or unplanned access patterns |
What is Amazon RDS?
Amazon Relational Database Service (Amazon RDS) supports IBM Db2, MariaDB, Microsoft SQL Server, MySQL, Oracle Database, and PostgreSQL. AWS manages the infrastructure, while teams still control sizing, storage, indexes, availability, and replicas.
Relational flexibility matters here: related tables and SQL make it easier to support new queries over time.
Our Amazon RDS vs EC2 comparison covers the managed-versus-self-managed trade-off.
What is Amazon DynamoDB?
Amazon DynamoDB is a serverless NoSQL database built around items, primary keys, and secondary indexes, with on-demand or provisioned capacity.
DynamoDB design starts with understanding the main access patterns.
Stormit's Amazon DynamoDB overview covers the service in more detail.
The core difference between Amazon RDS and DynamoDB: Relational vs access-pattern modeling
How data modeling works in Amazon RDS
Consider an ecommerce application with customers, orders, and order items. In Amazon RDS, separate tables can be linked through primary and foreign keys, then joined, filtered, or aggregated in new ways.
If the business later wants customers with more than three orders in 30 days and their average order value, the existing tables can often answer it. That query did not need to be anticipated when the schema was designed.
How data modeling works in DynamoDB
DynamoDB starts with what the application needs to retrieve: a customer by ID, orders by date, or an order with its items.
A customer might use partition key CUSTOMER#123, with orders under sort keys such as ORDER#2026-08-15#456. If the business later needs orders by payment reference, the key design may require a global secondary index, duplicated attributes, or a model change.
Why this difference matters
Amazon RDS generally leaves more room for new queries. With DynamoDB, supporting them is easier when access patterns were planned from the start. A relational query may need tuning or another index; a new DynamoDB access pattern may require an index or model change.
💡 Practical tip: This trade-off matters most when future query requirements are hard to predict.
Amazon RDS vs DynamoDB: Key differences that affect architecture
Query flexibility and access patterns
Amazon RDS supports SQL, joins, aggregations, and indexes, which suits evolving query needs. DynamoDB Query operations start with a partition key value, while sort keys and secondary indexes add other paths. Repeated scans for small result sets usually signal a poor fit.
Amazon RDS is easier to adapt as query needs change. DynamoDB works most efficiently with planned access patterns.
Transactions and data integrity
Relational engines support ACID transactions and constraints such as foreign keys and uniqueness. DynamoDB also supports ACID transactions and conditional writes across up to 100 distinct items in the same account and Region. Relational constraints fit naturally in Amazon RDS. DynamoDB works well when consistency can be handled through transactions and conditional item updates.
Consistency
Amazon RDS follows the selected engine's consistency behavior on the writer, while read replicas can lag. DynamoDB supports eventual and strong reads for supported operations, with global secondary indexes remaining eventually consistent.
Global tables support MREC and MRSC.
⚠️ One limitation: MRSC does not support DynamoDB transaction operations.
Scalability
Amazon RDS scales through DB instances, storage, and read replicas, while write scaling needs more deliberate architecture. DynamoDB distributes data across partitions with on-demand or provisioned throughput.
⚠️ Good partition-key design still matters: concentrated traffic can create hot partitions and throttling.
DynamoDB vs Amazon RDS performance: Compare the actual workload
DynamoDB vs Amazon RDS performance depends on the workload being tested. DynamoDB targets predictable, single-digit millisecond performance for supported access patterns, while Amazon RDS performance depends on the engine, resources, indexes, query plans, and workload.
An IoT state lookup by device ID maps naturally to DynamoDB; a report joining customers, invoices, products, and payments fits a relational database better.
Operations and maintenance
Amazon RDS still requires sizing, upgrades, indexes, connections, and query tuning. DynamoDB removes database instances but shifts attention to keys, throughput, throttling, and observability.
Availability and global distribution
Amazon RDS Multi-AZ DB instances use a standby for failover; Multi-AZ DB clusters for MySQL and PostgreSQL use one writer and two readable instances across three AZs. DynamoDB spans multiple AZs, while global tables add multi-Region, multi-active replication.
📖 For broader resilience guidance, see our AWS high availability architecture guide.
Choosing the right database architecture depends on more than individual features. Stormit can help you evaluate your workload, scalability, resilience, and cost requirements to identify the right AWS database approach.
Amazon RDS vs DynamoDB pricing: What actually drives cost?
An RDS instance price and a DynamoDB request price measure different things, so they cannot be compared in isolation. A useful DynamoDB vs Amazon RDS pricing comparison starts with the architecture the workload actually needs.
How Amazon RDS pricing works
Amazon RDS cost can include compute, storage, IOPS or throughput, Multi-AZ deployments, replicas, backups, data transfer, licensing, and Extended Support. Rightsizing matters in any Amazon RDS vs DynamoDB cost comparison because provisioned resources can keep generating cost during quiet periods.
📖 See Amazon RDS Pricing Explained for a detailed breakdown.
How DynamoDB pricing works
With on-demand capacity, you pay for read and write requests, while provisioned mode charges for configured throughput. Storage, indexes, backups, transactions, global tables, item size, and consistency can also affect cost.
Secondary indexes can increase both storage and write-related usage, so adding an access path can affect the data model and the cost model.
When Amazon RDS may be cheaper
Amazon RDS can be cost-effective for stable relational workloads. If an application relies heavily on joins and reporting, a well-sized deployment may cost less overall than recreating those capabilities through duplicated DynamoDB data, multiple indexes, and extra application logic.
When DynamoDB may be cheaper
DynamoDB can be economical for key-based workloads with variable traffic. A shopping-cart or session workload with sharp peaks can use on-demand capacity without keeping database compute provisioned for peak demand.
In a DynamoDB vs Amazon RDS cost comparison, workload fit still matters, especially how well the access patterns map to DynamoDB.
In one Stormit project, DynamoDB supported a renewable-energy customer's microservices architecture and real-time heat pump sensor data processing. The solution reduced median latency by 20% while improving scalability and optimizing infrastructure costs.
Common RDS vs DynamoDB cost traps
For Amazon RDS, watch for oversizing, unnecessary HA capacity, unused replicas, excess storage or IOPS, and Extended Support.
For DynamoDB, watch for scans, poor key design, unnecessary indexes, overprovisioning, transactions, and multi-Region replication. Our AWS cost optimization guide covers the broader picture.
Moving a relational application to DynamoDB can also add redesign cost.
💡 Practical tip: If the existing relational model already meets the workload's needs, weigh a DynamoDB redesign against its added effort and complexity.
Choosing between Amazon RDS and DynamoDB
If you're deciding when to use DynamoDB vs Amazon RDS, start with how the application models and accesses its data.
Choose Amazon RDS for relational workloads
Amazon RDS is usually the stronger choice when:
- relationships between entities are central to the application;
- you depend on SQL, joins, aggregations, or flexible filtering;
- query requirements are likely to change;
- relational constraints and transactions matter;
- you are migrating an existing relational workload;
- the workload fits a conventional relational scaling model.
For example, a SaaS platform with customers, subscriptions, invoices, and changing reports suits Amazon RDS because those records are related and may need new queries over time.
Choose DynamoDB for predictable access at scale
DynamoDB is a better fit when:
- important access patterns can be defined in advance;
- most operations are key-based reads and writes;
- traffic is high, bursty, or difficult to forecast;
- horizontal scaling matters more than ad hoc querying;
- you want a serverless database without instances to manage;
- the workload benefits from multi-Region, multi-active access.
For example, a session store or shopping cart with predictable key-based reads and traffic spikes is a typical DynamoDB fit.
Scale matters, but DynamoDB also needs to match the application's access patterns.
Can you use Amazon RDS and DynamoDB together?
Yes. An ecommerce platform might keep customers, orders, payments, and inventory in Amazon RDS while DynamoDB handles carts, sessions, or idempotency records. Each database needs clear ownership and a defined source of truth to avoid unnecessary synchronization complexity.
📖 For gradual modernization, see our 6 Rs of AWS migration.
Amazon RDS vs DynamoDB vs Amazon Aurora: A short comparison
Amazon Aurora MySQL and Aurora PostgreSQL are relational engines within Amazon RDS that use distributed, shared storage and Aurora-specific capabilities.
| Choose | Best fit |
| Amazon RDS | Supported relational engines and conventional managed relational architecture |
| Amazon Aurora | MySQL- or PostgreSQL-compatible workloads that benefit from Aurora-specific storage, scaling, or global capabilities |
| DynamoDB | NoSQL workloads built around key-based access and horizontal scale |
For an Amazon RDS vs DynamoDB vs Amazon Aurora decision, first determine whether the workload should be relational. If yes, compare Amazon RDS and Aurora on compatibility, scaling, availability, and cost. Aurora remains a relational choice, while DynamoDB requires a different approach to modeling and accessing data.
Decision framework for Amazon RDS vs DynamoDB
| Question | Lean toward |
| Do joins, relationships, constraints, or complex relational transactions define the workload? | Amazon RDS |
| Are important new query patterns likely after launch? | Amazon RDS |
| Can core access patterns be expressed as efficient key-based operations? | DynamoDB |
| Do you need serverless horizontal scaling for highly variable traffic? | DynamoDB |
| Do you need multi-Region, multi-active writes and the model fits? | DynamoDB |
| Do components have fundamentally different access patterns? | Consider both |
If both remain viable, model the hardest queries, transaction boundaries, data volume, traffic peaks, consistency, and recovery needs. Then compare DynamoDB vs Amazon RDS pricing for each, including redesign costs where relevant.
RDS vs DynamoDB: Let the workload decide
The choice between Amazon RDS and DynamoDB comes down to how the application needs to store, relate, and retrieve data. Amazon RDS supports relational modeling and flexible queries. DynamoDB is designed around known access patterns and serverless, distributed scale.
Start with relationships, access patterns, transactions, consistency, and scale, then compare operations and Amazon RDS vs DynamoDB cost. Some applications will benefit from using each service for different parts of the workload.
At Stormit, an AWS Well-Architected Review can help identify architecture, resilience, and cost optimization opportunities.
FAQs
DynamoDB vs Amazon RDS performance depends on the operation being measured. DynamoDB favors low-latency key-based access, while Amazon RDS handles relational queries and transactions. Test real workloads.
It can, but replacing Amazon RDS with DynamoDB usually involves more than moving the data. Relational schemas often need to be redesigned around DynamoDB keys, indexes, and access patterns, especially when they rely on joins or flexible queries.
DynamoDB vs Amazon RDS cost depends on the workload and architecture. Amazon RDS can suit stable relational workloads, while DynamoDB works well for efficient key-based workloads with variable traffic. Compare the full architecture.
Use DynamoDB when key-based access patterns and serverless horizontal scaling fit the workload. If relationships, changing queries, joins, or constraints matter more, Amazon RDS is usually the better fit.
An AWS Solutions Architect with over 5 years of experience in designing, assessing, and optimizing AWS cloud architectures. At Stormit, he supports customers across the full cloud lifecycle — from pre-sales consulting and solution design to AWS funding programs such as AWS Activate, Proof of Concept (PoC), and the Migration Acceleration Program (MAP).
