Understanding the Key Differences: AWS DynamoDB vs MongoDB

In the realm of NoSQL databases, AWS DynamoDB and MongoDB stand out as two of the most popular choices. Both databases offer unique features and capabilities, making them suitable for a variety of applications. However, understanding their differences is crucial for developers and businesses to make an informed decision. Let’s dive into a detailed comparison.

Service Model

DynamoDB is a fully managed NoSQL database service offered by Amazon Web Services (AWS). It's serverless, meaning users are freed from managing the underlying infrastructure.

MongoDB, on the other hand, is an open-source NoSQL database. Users can self-host MongoDB on their own servers or opt for MongoDB Atlas, its fully-managed cloud version.

Data Model

DynamoDB uses a key-value and document data model designed for high performance, even at large scales.

MongoDB primarily focuses on a document-oriented approach, allowing for more complex data structures stored in JSON-like documents.


DynamoDB shines in scalability. It's highly scalable without any manual intervention, automatically adjusting to workload demands.

MongoDB is also scalable but requires more manual setup, especially in self-hosted scenarios. MongoDB Atlas, however, offers auto-scaling features similar to DynamoDB.


When it comes to querying capabilities, MongoDB leads with its rich query language and indexing capabilities, suitable for complex queries and aggregations. DynamoDB’s querying is more basic, focusing on key-value queries and simple filtering.


Both databases offer different consistency models. DynamoDB provides options for strong and eventual consistency, while MongoDB generally offers eventual consistency, with stronger options available in certain configurations.

Pricing Model

DynamoDB’s pricing is based on the read/write throughput and the storage used. It also has an on-demand pricing model for more flexibility. MongoDB’s pricing varies based on the deployment model, with self-hosted costs tied to the underlying infrastructure and MongoDB Atlas pricing based on resource usage.

Use Cases

DynamoDB is ideal for applications needing high performance and scalability with less complex querying needs, such as gaming, IoT, and mobile apps. MongoDB is more suited for applications requiring rich data structures and complex queries, like content management systems and e-commerce platforms.

Integration and Ecosystem

DynamoDB integrates seamlessly with other AWS services, making it a go-to choice for applications embedded in the AWS ecosystem. MongoDB’s open-source nature means it has a broad compatibility range and a large community, offering diverse integration possibilities.

Coding Example

MongoDB Python Example

First, here's a simple example showing how to connect to a MongoDB database, insert a document, and retrieve documents:

from pymongo import MongoClient

# Connect to MongoDB (replace 'mongodb_uri' with your MongoDB URI)
client = MongoClient('mongodb_uri')

# Select the database and collection
db = client['example_db']
collection = db['example_collection']

# Insert a document
document = {"name": "John Doe", "age": 30, "city": "New York"}

# Retrieve all documents
for doc in collection.find():

DynamoDB Python Example

Now, here's an example for AWS DynamoDB using the boto3 library:

import boto3

# Initialize a DynamoDB client (AWS credentials needed)
dynamodb = boto3.resource('dynamodb')

# Select the table
table = dynamodb.Table('example_table')

# Insert an item
item = {"id": "123", "name": "Jane Doe", "age": 25, "city": "Los Angeles"}

# Retrieve an item
response = table.get_item(Key={'id': '123'})
item = response.get('Item', {})

In these examples:

For MongoDB, we use pymongo, the native Python driver for MongoDB, to connect to the database, insert, and retrieve documents.

For DynamoDB, we use boto3, the AWS SDK for Python, to interact with the DynamoDB service. This includes inserting and retrieving items.


The choice between AWS DynamoDB and MongoDB hinges on your project's specific needs. Consider factors like scalability, data complexity, budget, and existing infrastructure before deciding. Both databases have their strengths, and understanding these will guide you towards the right choice for your application.

Remember, the database you choose plays a pivotal role in the success of your application, so choose wisely!