Amazon DynamoDB vs PostgreSQL

Data model and query style

DynamoDB is a NoSQL key-value and document database where data is accessed primarily by partition and sort keys, with secondary indexes added for alternative access paths. It does not support joins, and effective DynamoDB design starts from known access patterns rather than a normalised schema. PostgreSQL is a relational database built around tables, joins, foreign keys, and a full SQL dialect, which makes ad hoc queries, reporting, and evolving query needs straightforward. The fundamental difference is direction: DynamoDB asks you to model for the queries you know in advance, while PostgreSQL lets you store normalised data and decide how to query it later.

Scaling and performance

DynamoDB is engineered for predictable single-digit millisecond latency at effectively unlimited scale, automatically partitioning data and, in on-demand mode, absorbing large traffic swings without capacity planning. Global tables provide multi-region active-active replication. PostgreSQL delivers excellent performance for relational workloads and scales reads through replicas, but scaling writes horizontally requires sharding, partitioning, or distributed extensions, which add engineering effort. For workloads such as user sessions, IoT telemetry, shopping carts, or event logs with huge volume and simple access, DynamoDB is purpose-built; for transactional applications with complex relationships, PostgreSQL's relational engine is the better match.

Pricing and cost behaviour

PostgreSQL is free to license, so cost is infrastructure plus the staff time to operate it, which is predictable but includes operational labour. DynamoDB is serverless and billed by usage: on-demand mode charges per read and write request plus storage, while provisioned mode charges for reserved capacity units and can be cheaper for steady, well-understood traffic. DynamoDB cost can rise sharply with very high request volumes or inefficient access patterns, so capacity mode choice and key design matter. Pricing verified June 2026. Enterprise pricing requires a quote for committed-use or large reserved-capacity arrangements.

Operations, ecosystem and lock-in

DynamoDB removes server management entirely: there is no patching, no failover to configure, and no capacity to provision in on-demand mode, which suits small teams and serverless architectures on AWS. The trade-off is lock-in, since DynamoDB's API and design are AWS-specific and migrating away requires re-modelling onto another database. PostgreSQL is portable across clouds and on-premises, has a vast tooling and extension ecosystem, and avoids vendor dependence, but the team owns operations unless it uses a managed PostgreSQL service. Many architectures use both: PostgreSQL for relational core data and DynamoDB for high-scale, simple-access workloads alongside it.

User sentiment

Buyers frequently note that DynamoDB shines when access patterns are known and scale is large: teams report consistent low latency, genuinely hands-off operations, and strong fit with serverless AWS architectures. Recurring criticism centres on the learning curve of single-table design, the absence of joins and flexible queries, cost growth at very high request volumes, and AWS lock-in. PostgreSQL reviewers consistently praise its relational power, SQL familiarity, extensibility, and portability, alongside zero licence cost. The common complaint is that horizontal write scaling and self-managed high availability require engineering effort and expertise. Across both, experienced teams describe the choice as workload-driven rather than competitive: relational, query-rich data belongs in PostgreSQL, while high-throughput, simple-access data belongs in DynamoDB, and large systems often use each where it fits. Sentiment for both is positive, with dissatisfaction usually tracing to using one for a workload better suited to the other.

When to choose Amazon DynamoDB

Choose Amazon DynamoDB when the workload runs on AWS, demands predictable low latency at large scale, and has access patterns you can define in advance, such as session stores, carts, telemetry, gaming state, or event logs. Its serverless model removes operations and on-demand capacity absorbs spiky traffic without planning. Global tables suit multi-region active-active needs. Buyers should invest in access-pattern and key design up front, accept the lack of joins and ad hoc querying, model costs against on-demand versus provisioned capacity, and treat AWS lock-in as a deliberate trade for operational simplicity.

When to choose PostgreSQL

Choose PostgreSQL when data is relational, queries are complex or evolving, and you need joins, transactions, and analytical flexibility across any environment. It fits transactional applications, reporting, and systems where a normalised schema and ad hoc querying matter more than extreme write scale. PostgreSQL's portability and extension ecosystem suit teams that want to avoid lock-in or run multi-cloud. Buyers should ensure they have operational capacity for high availability and tuning, or adopt a managed PostgreSQL service, and plan partitioning or distributed extensions early if very high write throughput is expected.

Alternatives to both