Amazon DynamoDB vs MySQL: Which Is Right for You?

Data model and query approach

DynamoDB is a key-value and document store. You design tables around known access patterns, choosing partition and sort keys carefully because queries are efficient only along those keys and any secondary indexes you define. It does not support ad-hoc joins or arbitrary SQL; instead it rewards careful upfront data modelling with consistent performance regardless of table size. This is a different discipline from relational design and is the single biggest factor in DynamoDB success or frustration.

MySQL is relational. You model normalised tables, query them with full SQL including joins and aggregations, and rely on ACID transactions for correctness. This flexibility is ideal when access patterns evolve, when reporting and ad-hoc queries matter, or when relationships between entities are central. The trade-off is that relational performance at very large scale requires tuning, indexing discipline, and often sharding.

Scaling and operations

DynamoDB's defining strength is operations: there are no servers to manage, capacity scales automatically (on-demand) or to configured limits (provisioned), and global tables replicate across regions for multi-region active-active workloads. Latency stays in the single-digit milliseconds as data grows from megabytes to petabytes. MySQL requires the team to provision, patch, back up, and scale the database; horizontal scale generally means read replicas plus application-level sharding for writes. For unpredictable, very high-throughput key-value workloads, DynamoDB removes work that MySQL leaves to the operator.

Pricing and cost behaviour

DynamoDB bills on read and write request units plus storage, either on-demand (pay per request) or provisioned (pay for reserved capacity). Costs are predictable for steady traffic and can be efficient for spiky workloads on-demand, but poorly modelled access patterns or hot partitions can inflate spend. MySQL is free under the GPL; you pay only for infrastructure and optional support, which often makes it cheaper for moderate, steady workloads. The economics flip in DynamoDB's favour when scale, burst, and the cost of operating a high-availability relational cluster are all high. Pricing verified June 2026; enterprise pricing requires a quote.

Portability and fit

DynamoDB is AWS-only and its data model and APIs do not translate directly to other databases, so adopting it is a deeper commitment to AWS than choosing a portable engine. MySQL runs anywhere and moves between environments with standard tooling. The decision is rarely about which is better in the abstract; it is about workload shape. Use DynamoDB when access patterns are well understood and throughput and scale dominate; use MySQL when relationships, ad-hoc queries, and portability dominate. Many architectures use both, with DynamoDB for high-scale operational data and MySQL for relational and reporting needs.

User sentiment

Buyers frequently note that DynamoDB delivers consistent low latency at scale with almost no operational effort, and they value on-demand capacity and global tables for spiky, multi-region workloads. Reviewers caution that DynamoDB punishes poor data modelling, that ad-hoc queries are difficult, and that costs can surprise teams who do not align tables to access patterns. For MySQL, buyers consistently praise SQL flexibility, joins, mature tooling, and portability, while acknowledging that scaling and high availability are the operator's responsibility and that very high key-value throughput is not its strength. Across both, practitioners advise choosing by workload shape rather than preference, and they commonly pair the two: DynamoDB for high-scale operational data and MySQL for relational, transactional, and reporting workloads where query flexibility matters.

When to choose Amazon DynamoDB

Choose Amazon DynamoDB when you run on AWS, your access patterns are well understood, and you need predictable low latency at very large or highly variable scale with minimal operations. DynamoDB suits high-throughput OLTP key-value workloads, serverless applications, session and event stores, and multi-region active-active designs via global tables. It is the right tool when throughput and operational simplicity matter more than ad-hoc query flexibility and when committing further to AWS is acceptable.

When to choose MySQL

Choose MySQL when you need relational modelling, joins, transactions, and ad-hoc SQL, or when portability across clouds and on-premises matters. MySQL suits applications with evolving access patterns, reporting and analytics needs, and teams that want a widely supported engine with deep tooling and talent availability. It is also the better default when the data is naturally relational and when avoiding AWS lock-in is a strategic priority for the organisation.

Alternatives to both

Related comparison

Continue your research with our MongoDB vs DynamoDB analysis, or browse the full Database Management category for more independent reviews.