DynamoDB vs Amazon Aurora
Is DynamoDB or Aurora right for my workload?
Amazon DynamoDB is a fully managed NoSQL key-value and document store built for single-digit-millisecond performance at any scale, with no fixed schema. Amazon Aurora is a managed relational database that's compatible with MySQL and PostgreSQL, using full SQL with tables, joins, and ACID transactions. Choose DynamoDB for high-scale key-value access; choose Aurora for relational queries.
DynamoDB vs Amazon Aurora at a glance
| Characteristic | DynamoDB | Amazon Aurora |
|---|---|---|
| Data model | NoSQL key-value and document store; schemaless (only the primary key is required) | Relational: structured tables, rows, and columns with a defined schema and relationships |
| Query language | AWS SDK/API operations plus PartiQL, a SQL-compatible query language for select/insert/update/delete | Full SQL, compatible with MySQL or PostgreSQL |
| Joins | No server-side joins; you model access patterns up front (often single-table design) | Native SQL JOIN across tables via the relational engine |
| Indexes | Local and global secondary indexes (LSI / GSI) | Standard relational indexes (primary and secondary) provided by MySQL/PostgreSQL |
| Consistency | Eventually consistent reads by default; strongly consistent reads available per request | Strong consistency with full relational integrity |
| Scaling | Scales out horizontally across partitions; no upper limit on items per table or table size; on-demand or provisioned capacity | Cluster of a writer plus up to 15 low-latency read replicas across three Availability Zones; storage auto-grows to 256 TiB; Aurora Serverless v2 auto-scales capacity |
| Transactions | ACID transactions of up to 100 items (4 MB aggregate) within a single Region | Full multi-statement ACID transactions |
| Pricing model | On-demand or provisioned capacity (read/write units) plus storage | Instance capacity (On-Demand or Reserved) or Aurora Serverless v2, plus storage and I/O |
| Best fit | Web-scale key-value/document workloads, predictable low latency | Relational workloads needing joins, complex queries, and SQL familiarity |
When DynamoDB is the better choice
DynamoDB fits when your access patterns are known and key-based, and you need predictable single-digit-millisecond latency as traffic grows. Per the AWS DynamoDB Developer Guide, it's designed to scale out on distributed hardware with no upper limit on the number of items or total table size, which suits web-scale applications such as social networks, gaming, media sharing, and IoT. It's a good match when you want a serverless operational model with no instances to size, and when a flexible, semi-structured item shape (including JSON) is an advantage. If your workload is intermittent or key-value in nature, DynamoDB's per-request model lets cost track usage closely.
When Aurora is the better choice
Aurora fits relational workloads: data with real relationships, complex ad hoc queries, joins, aggregations, and reporting where SQL is the natural tool. Because Aurora is MySQL- and PostgreSQL-compatible, existing SQL code, tools, and ORMs generally work with little change, and teams already productive in SQL keep their skills. Aurora is a strong choice when strong consistency and relational integrity across many related tables matter, or when you need multi-statement transactions that span a rich schema. AWS also notes the two are often complementary — for example, DynamoDB on the hot path and Aurora for the analytical or reporting path.
Working with DynamoDB
Once you've chosen DynamoDB, DynoTable is a desktop DynamoDB client for browsing, editing, and querying tables without the AWS Console. Because DynamoDB has no server-side joins, expressing queries against your keys and indexes is where most of the day-to-day work goes. The free DynamoDB Expression Builder helps you compose key conditions, filter expressions, and projections and emit the matching SDK, CLI, or PartiQL code. DynoTable is a closed-source commercial application; it reads your standard AWS credentials and talks to DynamoDB directly, so there's nothing to migrate when you switch from another client.
FAQ
Is DynamoDB cheaper than Aurora?
It depends on the workload — neither is universally cheaper. DynamoDB bills for on-demand or provisioned read/write capacity plus storage, so cost tracks request volume and can approach zero when idle. Aurora bills for instance capacity (or Aurora Serverless v2) plus storage and I/O. For intermittent, key-value traffic DynamoDB's per-request model often costs less; for sustained, high-volume relational workloads a provisioned Aurora cluster can be more economical. Model your own read/write patterns before deciding.
Aurora vs DynamoDB for serverless apps?
Both offer serverless-style operation, so the deciding factor is the data model, not the billing model. DynamoDB is serverless by design — no instances to manage, capacity that scales automatically, and per-request pricing — which suits key-value and document access at scale. Aurora Serverless v2 auto-scales a relational engine (and can scale to zero when idle), which suits serverless apps that genuinely need SQL, joins, and relational integrity. Pick DynamoDB for key-value access patterns and Aurora when your app is fundamentally relational.
Related
- Learn: When to use DynamoDB · SQL for DynamoDB
- Build queries fast with the free DynamoDB Expression Builder.
- Download DynoTable for macOS, Windows, or Linux.
Last verified 2026-07-12 against the AWS DynamoDB Developer Guide and Amazon Aurora documentation. Amazon DynamoDB, Amazon Aurora, MySQL, and PostgreSQL are trademarks of their respective owners; referenced here for identification only.