MongoDB or DynamoDB: which NoSQL database to choose?

MongoDB and DynamoDB are two NoSQL databases that genuinely compete for the same decision, and differ in almost everything else. MongoDB stores documents in JSON form and queries them with a rich language: filters on any field, aggregations, indexes added when the need shows up. It runs wherever you want, on a machine in your own server room, inside a container or on a managed service. DynamoDB is the managed database from AWS, built around key-based access, and it asks for a different design in return: access patterns are decided up front, because the primary key and any local secondary indexes are born with the table and cannot be added later. The managed service also exists only inside AWS: DynamoDB Local is meant for development and testing, not for running anything in production elsewhere. Both belong to the family described in relational, NoSQL, vector or graph, but the resemblance ends there.

Two clarifications clear away as many misconceptions. Global secondary indexes can be added to a live table, but they project a copy of the data, with their own cost and consistency: they correct a wrong guess, they do not remove the need to make one. And compatibility is not the service: ExtendDB, the Apache 2.0 project with which AWS opened its APIs in 2026, lets a DynamoDB-shaped workload run elsewhere, for now in an early release, but it does not carry the managed service's guarantees or scale.

Portability or managed integration

This is the decision the feature comparison hides. The managed service does not move: choosing it means accepting that this piece of the application cannot change providers without changing the operational guarantees it relies on, a textbook case of vendor lock-in. That is not a defect in itself: an organization already living inside AWS gets identity, encryption, backups and scaling in return, with nothing to administer. MongoDB runs anywhere, and that portability is its main argument. The flip side is the license, and it is the point the English comparisons barely touch: since 2018 the Community Edition server has been distributed under the SSPL, a source-available license the Open Source Initiative does not recognize as open source, because it puts obligations on anyone offering that software as a managed service. Anyone choosing MongoDB "because it is open source" is working from outdated information. The clause does not hit a company using the database inside its own application, but the distinction has to be read rather than assumed: the license families are in software license.

An enterprise example

A spare-parts distributor keeps its product catalog on MongoDB: records with different attributes per product family, searches over combinations marketing invents every quarter, an engine that has to answer questions nobody anticipated. On the telemetry from warehouse forklifts, by contrast, the question is always the same, the history of one vehicle over a time range, and the volume grows without limit: there DynamoDB inside an architecture that is already AWS costs less operational effort. The decision was not "which database is better", but which of the two workloads could tolerate being tied to one provider. The catalog feeds the website and will end up elsewhere; the telemetry stays where it is produced.

Why it matters for decision makers

There are three questions to ask, and none of them is about performance. Are this application's queries known in advance, or will they change with the business? Does this component need to be able to leave the cloud it sits in, and within how many months? Do our resellers host the product on behalf of third parties, meaning are we really outside the license clause? Anyone who needs transactions with ACID guarantees will find them in both, with different scope and cost, so that is not the deciding criterion. The criterion is where you want the constraint to live: in the code you write or in the contract you sign.