DynamoDB vs PostgreSQL
DynamoDB and PostgreSQL solve different problems. DynamoDB is a serverless NoSQL database optimised for scale and predictable latency; PostgreSQL is a mature relational database optimised for flexible querying and data integrity. This page compares them factually so you can choose by workload, not by hype.
What is the difference between DynamoDB and PostgreSQL?
DynamoDB is a fully managed, serverless key-value and document NoSQL database that delivers single-digit-millisecond performance at any scale but has no JOIN operator, so you model data around known access patterns. PostgreSQL is an open-source object-relational SQL database with full joins, a rich type system and ACID transactions, designed for ad-hoc relational queries. Choose by access pattern, scale and query flexibility.
DynamoDB vs PostgreSQL at a glance
| Characteristic | DynamoDB | PostgreSQL |
|---|---|---|
| Data model | NoSQL — supports both key-value and document models; flexible, per-item schema | Object-relational — tables, rows and columns with a defined, enforced schema |
| Query language | Purpose-built API (Query/Scan/GetItem) plus PartiQL, a SQL-compatible language for SELECT/INSERT/UPDATE/DELETE | Full SQL; PostgreSQL 18 conforms to at least 170 of the 177 mandatory SQL:2023 Core features |
| Joins & relations | No JOIN operator; AWS recommends denormalizing your data model | Full relational joins, foreign keys and constraints across tables |
| Indexes | Global and local secondary indexes let you query on an alternate key | B-tree, hash, GiST, SP-GiST, GIN, BRIN, plus partial, expression and covering indexes |
| Consistency | Eventually consistent by default; strongly consistent reads optional; native ACID transactions | ACID-compliant since 2001; strong consistency by default via MVCC |
| Scaling | Horizontal — partitions data automatically; serverless on-demand mode scales up and down (to zero) | Vertical scaling plus read replicas for read scale-out; write scale-out needs partitioning or external tooling |
| Transactions | Server-side ACID transactions across one or more items and tables, subject to per-request limits | Full multi-statement transactions, savepoints (nested transactions) and MVCC concurrency |
| Hosting / managed | Serverless, AWS-only; on-demand or provisioned capacity billing | Open source, self-host anywhere; managed options include Amazon RDS for PostgreSQL and Aurora PostgreSQL-Compatible Edition |
| Best-fit workloads | High-scale operational apps with known access patterns (carts, sessions, leaderboards, event data) | Relational and analytical workloads needing joins, ad-hoc queries and strong data integrity |
When DynamoDB is the better choice
Pick DynamoDB when your access patterns are known and your priority is predictable latency at scale rather than ad-hoc querying:
- You need consistent single-digit-millisecond reads and writes whether you have hundreds or hundreds of millions of users.
- You want a serverless database with no servers to patch or capacity to plan, and on-demand billing that scales to zero when idle.
- Your workload is key- or item-oriented (user profiles, sessions, shopping carts, leaderboards, event streams) and fits DynamoDB's partition/sort-key model.
- You want multi-Region, multi-active replication and a high availability SLA without building your own replication.
The trade-off: no joins, and you must design your keys around the queries you will run. Rework of access patterns after the fact is harder than in SQL.
When PostgreSQL is the better choice
Pick PostgreSQL when query flexibility and relational integrity matter more than horizontal scale:
- You need joins, aggregations and ad-hoc queries across normalised tables, and your access patterns will keep evolving.
- You want full SQL, a rich type system (JSON/JSONB, arrays, geospatial via PostGIS), and enforced constraints for data integrity.
- Your dataset and traffic fit a vertically scaled primary with read replicas, or you are comfortable adding partitioning/sharding tooling for write scale-out.
- You want to self-host, avoid single-vendor lock-in, or run a managed Postgres on the cloud of your choice.
The trade-off: you own more of the scaling and operational story than with a serverless NoSQL database, especially for very high write throughput.
Working with DynamoDB
Once you have chosen DynamoDB, DynoTable is a desktop client
built specifically for it. It gives you a native GUI for browsing and editing
items, a SQL Workbench that expresses relational-shaped queries — joins,
GROUP BY, aggregates — within DynamoDB's access-pattern rules by compiling
them down to DynamoDB's own Query/Scan operations, and an AI assistant that
runs on your own AWS Bedrock credentials so your schema and data stay in your
account. If you are coming from Postgres and miss writing SQL, the
SQL for DynamoDB guide and
PartiQL vs SQL guide explain what maps across and what
does not.
For quick one-off queries, the free DynamoDB Expression Builder generates correct key conditions, filter expressions and update expressions with the right reserved-word and attribute-name handling — no install required.
FAQ
Can DynamoDB replace PostgreSQL?
Not in general — they target different workloads. DynamoDB can replace PostgreSQL for high-scale operational applications with well-defined access patterns, where its serverless scaling and predictable latency are the priority. It is a poor fit where you rely on joins, ad-hoc relational queries or a constantly evolving query surface, which are PostgreSQL's strengths. Many teams run both: PostgreSQL for relational and analytical work, DynamoDB for the high-throughput key-access paths.
Does DynamoDB support SQL or joins?
DynamoDB supports PartiQL, a SQL-compatible query language for SELECT, INSERT, UPDATE and DELETE against your tables, but it does not support the JOIN operator — AWS recommends denormalising your data instead. PostgreSQL supports full SQL including joins across tables. See the DynamoDB JOIN guide for how to model relationships without a native join.
Is DynamoDB cheaper than PostgreSQL?
It depends on the workload, so treat cost as something to model rather than assume. DynamoDB bills per read/write request and stored data (on-demand or provisioned capacity) and scales to zero when idle, which can be cheaper for spiky or low-baseline traffic. A managed PostgreSQL such as Amazon RDS or Aurora generally bills for provisioned compute and storage that runs continuously, which can be more cost-effective for steady, query-heavy workloads. Estimate both against your real access patterns before deciding.
Related
- Learn: SQL for DynamoDB · PartiQL vs SQL · Joins in 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 the official PostgreSQL documentation. PostgreSQL is a trademark of the PostgreSQL Community Association; referenced here for identification only.