DynamoDB vs MongoDB: choosing a NoSQL database
Amazon DynamoDB is AWS’s fully managed, serverless NoSQL database that runs only on AWS, while MongoDB is the popular open-source document database you can run anywhere — including fully managed via MongoDB Atlas on any major cloud — so the choice often comes down to AWS lock-in versus portability as much as the databases themselves. Both belong to the same broad NoSQL family, and both trade the rigid, joined structure of a relational database for flexibility and scale, but they arrived at that goal from different directions. DynamoDB was built from the ground up as an AWS-native service, tuned for predictable performance at any scale with almost no operational work on your part. MongoDB was built as an independent, portable document database that a provider then wrapped in managed hosting. This article compares how they model data, how portable each one is, what “managed” means for each, and how to reason about the choice — without pretending one simply beats the other.
What each database is
DynamoDB is AWS’s key-value and document NoSQL database: you define a primary key, AWS handles the servers, partitioning and scaling behind it, and you pay for the throughput and storage you consume. It is a serverless service in the fullest sense — there is no instance to size or patch, and it only exists inside AWS. See our dedicated explainer on what Amazon DynamoDB is for the service itself.
MongoDB is a document database: records are stored as flexible, JSON-like documents whose fields can vary between records, which is the general NoSQL approach covered in our relational-vs-NoSQL-databases guide. You can self-host it on any infrastructure, or run it fully managed via MongoDB Atlas, which is offered on AWS, Azure and Google Cloud alike. AWS also offers Amazon DocumentDB, a separate service built to be compatible with MongoDB’s API — useful if you want MongoDB-style documents but prefer to stay inside AWS’s own managed-service ecosystem.
The key differences
Set side by side, the practical differences are these:
- Data model — DynamoDB is primarily key-value with document support and is built around designing your access patterns up front; MongoDB is a fuller document model with richer, more ad-hoc querying out of the box.
- Portability and lock-in — DynamoDB runs only on AWS, so choosing it is also choosing AWS for that workload. MongoDB runs anywhere, so a MongoDB-based application can, in principle, move providers without a database rewrite.
- Managed options — DynamoDB is inherently serverless and managed; there is no unmanaged version. MongoDB can be self-managed, run on your own servers, or used fully managed through Atlas — you choose how much operational work to keep.
- Operational model — DynamoDB’s scaling and capacity are largely automatic and AWS-tuned. MongoDB’s scaling (sharding, replica sets) is a concept you and your tooling manage, even when Atlas is doing the underlying work.
- Ecosystem — DynamoDB integrates tightly with the rest of AWS (Lambda, IAM, Streams). MongoDB has a large independent driver and tooling ecosystem that works the same way regardless of which cloud, or none, is underneath it.
When DynamoDB tends to be the better fit
DynamoDB suits teams already committed to AWS who want a NoSQL database with as little operational overhead as possible, and who can design their access patterns in advance — DynamoDB rewards knowing how you will query the data before you build the table. It is a natural fit for serverless architectures built on Lambda and API Gateway, where the rest of the stack is already AWS-native and consistent, predictable performance matters more than flexible ad-hoc queries.
When MongoDB tends to be the better fit
MongoDB suits teams who want to avoid being tied to one cloud provider, who need richer querying over the document model than DynamoDB is designed for, or who are migrating an existing application that already speaks MongoDB’s API. It is also the more familiar starting point for developers coming from other document databases, since its query language and tooling are the same whether the database sits on a laptop, on-premises, or in any cloud’s managed offering.
It depends on your priorities, not a benchmark
Neither database is objectively “faster” or “better” in a way worth quoting — both are capable, mature systems built for demanding, large-scale workloads, and any specific performance comparison depends heavily on the workload, the access pattern and how well the table or collection is designed. The honest question is what you are optimising for: minimal operational work and deep AWS integration points towards DynamoDB; portability and a richer, more familiar document model points towards MongoDB. Some organisations even run both, choosing per service based on which properties matter most for that particular workload.
Certification study
DynamoDB is exam-relevant across AWS certifications — the Solutions Architect Associate expects you to recognise when a NoSQL, AWS-native database fits a scenario, and the Developer Associate goes further into designing DynamoDB tables and access patterns. The Data Engineer Associate covers DynamoDB as one of several data stores you might pull from in a pipeline. MongoDB itself is not part of AWS certification syllabuses — where it appears, it is usually as the general “document database” concept behind a scenario, which our relational-vs-NoSQL-databases guide covers in more depth.
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