MongoDB and DynamoDB excel as NoSQL databases, each with its unique advantages. DynamoDB offers simplicity and efficiency, ideal for straightforward applications. MongoDB stands out for complex data structures, advanced queries, and robust indexing capabilities. Choose based on your project’s specific needs.
MongoDB vs DynamoDB: Which NoSQL database is right for you?
12 mins read
Oct 31, 2025
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Data storage flexibility can be one of the most important keys to your productivity as a software developer, and NoSQL databases are a great facilitator of that flexibility. They allow for high-speed data management and scalability, which makes them ideal for a variety of data structures and modern use cases, such as web applications and cloud-based services.
Today, we’ll go over the pros and cons of two services and discuss which you should use depending on your project goals and backend use cases. Given the number of NoSQL database services available, we’re narrowing our focus to two of the most popular: MongoDB and Amazon’s DynamoDB.
What is a NoSQL database?#
NoSQL stands for “non-SQL” or “not only SQL.” Structured Query Language (SQL) is a programming language developed in the early 1970s to allow programmers to interface with relational database management systems (RDBMS). This interface allowed them to organize data into tables that were then linked based on shared data.
In the era of Big Data, however, increasing demand for faster and more disparate use of large data sets led to the development of NoSQL or non-relational databases. Without the need to connect tables, developers could use NoSQL databases to divide search-query and data-storage workloads across multiple database servers, instead of relying on a single server. NoSQL databases have not rendered relational databases obsolete but simply provided a viable alternative for relevant use cases.
NoSQL databases are also considered distributed, meaning that data is stored on multiple local or remote servers. This means that, if some piece of the database is taken offline, the rest of the database remains functional and accessible.
Types of NoSQL databases#
Broadly speaking, there are four major types of NoSQL databases:
- Key-value: Considered the simplest type, this is a schema-less data model that houses data in a dictionary of key-value pairs, meaning each item has a key and a value. You often see this kind of database used in web applications that need to store and cache user-session data, such as for gaming or shopping apps.
- Document-oriented: This model stores data as documents, usually in JSON, XML, or BSON (binary JSON) formats, with each document containing pairs of fields and values. These values come in various types, such as arrays, Booleans, numbers, objects, and strings. This type ensures that data stays together when used in applications. It also gives you increased flexibility, as you don’t need to make sure data schemas are uniform across all documents.
- Wide-column: This database arranges data into columns, which allows you to access only the data you need without wasting memory. This method of organization is considered more complex than other types of NoSQL databases. These can be useful for storing data across multiple servers.
- Graph: This model organizes data in nodes and edges. Nodes are any person, place, or object in the database, and edges define the relationship between nodes. For example, if you were to put your family tree in a graph database, an edge could categorize the relationship between you and your brother as “siblings.”
What is MongoDB?#
MongoDB is an open-source, document-oriented NoSQL database from MongoDB Inc. that began development in 2007. It uses BSON-formatted documents and files to store and manage data. MongoDB supports field, range query, and regular-expression search functions through this system.
Like other document-oriented databases, MongoDB doesn’t require matching data schemas across documents, which can provide flexibility that other databases lack. In addition, MongoDB Atlas is cloud-agnostic, meaning you could feasibly deploy it on any cloud provider (AWS, Google Cloud, or Microsoft Azure) or on premise without much issue. MongoDB is also compatible with a wide range of programming languages, including C, C#, Python, Ruby on Rails, Rust, and Scala, increasing provisioning options.
MongoDB is ideal if your project might need to scale quickly to meet increasing demands. Being schema-less, MongoDB supports adding document data to the database rapidly, which can speed up development. This is great if your project’s schema is prone to frequent changes, as it means you don’t have to reformat every document in the database after every change.
MongoDB is not without its drawbacks. Its schema-free design is flexible but also means you might experience some difficulty performing complex data transactions. In some cases, this can lead to data loss or corruption. And its document size is limited to 16MB, meaning larger data sets have to be broken up to fit in the database properly.
Get hands-on with MongoDB today.#
Try one of our 300+ courses and learning paths: MongoDB: The Complete Guide.
MongoDB Atlas and the evolution of serverless NoSQL#
MongoDB has changed significantly since 2022. Beyond its self-managed deployments, the fully managed MongoDB Atlas platform now offers serverless instances, which automatically scale compute and storage based on workload. This eliminates the need to pre-provision capacity, making it more cost-effective for unpredictable traffic patterns or early-stage products.
Atlas also supports multi-region global clusters, which let you place data closer to users and reduce latency worldwide. You can even choose specific regions for read or write operations, enabling regulatory compliance and performance optimization.
Another major advancement is support for time-series data and vector search. This makes MongoDB a powerful choice for modern applications such as IoT analytics, semantic search, and recommendation systems — use cases that were traditionally outside its scope.
What is DynamoDB?#
DynamoDB is Amazon’s proprietary NoSQL database service. First released in 2012, DynamoDB supports two types of NoSQL data: key-value and document-oriented. It has similar functionality to MongoDB, though it is only usable through Amazon Web Services (AWS), making it less than ideal for users who aren’t working with AWS services.
Given how common AWS is, there are plenty of reasons to use Amazon DynamoDB. Though less flexible in where you can deploy it, DynamoDB has smoother integration with other tools and services found in AWS. MongoDB allows users tighter control over their databases, but it also requires users to actively manage their configurations and infrastructure, which can take up precious development time. DynamoDB, however, uses AWS to manage updates, scaling, and availability, making the setup process quick and simple. You also won’t have to worry about keeping servers and backups running. Finally, AWS DynamoDB’s out-of-the-box security features are solid, thanks to being part of AWS and not being directly accessible via the internet.
Unfortunately, DynamoDB has more issues than just its limited deployment capability. Its querying capabilities aren’t great, and it only allows three data types for primary keys: string, number, and binary. (It does support many different data types for other attributes within a table.)
Furthermore, DynamoDB uses a throughput model for pricing and data processing by default. With this model, you estimate your read/write capacity needs before the service can appropriate the resources. In cases where you don’t really know your expected read/write volumes, you might under- or overestimate your needs, which can lead to batch-processing failures. Of course, you can use the on-demand pricing model to work around this, and DynamoDB will automatically accommodate your workloads as they ramp up or down. But that comes with the risk of bill spikes if your project’s traffic ends up higher than initially expected.
Get hands-on with AWS today.#
Try one of our 300+ courses and learning paths: Become an AWS Professional.
Advanced DynamoDB features you should know#
DynamoDB has also evolved to meet the demands of large-scale, mission-critical applications. Some key features worth highlighting include:
DAX (DynamoDB Accelerator): An in-memory caching layer that can improve read performance by up to 10x.
Adaptive capacity: DynamoDB now automatically redistributes throughput across partitions, preventing “hot” partitions from causing throttling.
Global Tables v2: Multi-region, multi-master replication now supports faster cross-region writes and automatic conflict resolution.
PartiQL: A SQL-compatible query language for DynamoDB, making it easier for teams familiar with relational databases.
On-demand capacity mode: DynamoDB can automatically scale read and write capacity without manual provisioning.
These updates make DynamoDB more flexible and performant than earlier versions, significantly reducing operational overhead while improving scalability.
MongoDB
- Can be hosted on premise or on any public or private cloud
- Difficult to set up, configure, and maintain
- Disables authentication process by default, which can lead to security issues if not configured properly
- Available on Linux, OS X, Solaris, and Windows operating systems
- Written in C++ and supports a wide range of programming languages
- Allows server-side scripting through JavaScript
- Maximum document size of 16 MB
- Has its own query language that provides advanced querying capabilities
- Offers a variety of data types
DynamoDB
- Can only be run on AWS cloud. No on-premise options
- Simple to set up and configure through AWS
- Accessible only through AWS’s Identity and Access Management (IAM) feature, which makes it very secure by default
- Available only through AWS hosting
- Written in Java and supports a much smaller number of programming languages
- Allows server-side processing through DynamoDB Streams and the AWS Lambda service
- Maximum document size of 400 KB
Queryoperation allows key-value and range queries, and scans provide another option for reading items in a table- Offers three data types for primary keys and other data types for non-key attributes
Indexing and query strategies compared#
Query capabilities are often the deciding factor when choosing between MongoDB and DynamoDB. Both databases now offer more advanced options than before, but they approach indexing differently:
MongoDB supports rich secondary indexes, compound indexes, and full-text search. You can query on any field without major schema changes, which is ideal for dynamic applications.
DynamoDB uses Global Secondary Indexes (GSI) and Local Secondary Indexes (LSI) to extend query capabilities. While these require more upfront design, they can deliver predictable performance and scale for well-structured access patterns.
Understanding these trade-offs is critical. MongoDB offers flexibility with minimal upfront planning, while DynamoDB rewards careful data modeling with predictable performance at scale.
Consistency, replication, and durability#
Modern applications require strong guarantees around data consistency, availability, and recovery — areas where both databases have improved significantly.
MongoDB offers tunable consistency levels, from strong to eventual, and supports automatic failover with replica sets. Atlas also provides multi-region writes, enabling higher availability and lower latency.
DynamoDB supports strongly consistent reads and eventual consistency, with automatic replication across availability zones. Global Tables v2 allows multi-region writes, reducing latency and enabling geo-distributed workloads.
Durability and fault tolerance are now comparable in both databases, but DynamoDB’s seamless integration with AWS’s infrastructure can offer a reliability advantage for existing AWS users.
Vector search and modern use cases#
The rise of AI and recommendation systems has made vector search a must-have feature for many applications. MongoDB now supports Atlas Vector Search, which integrates semantic search directly into the database. This allows you to store, index, and query embeddings alongside other document data.
DynamoDB doesn’t natively support vector search but can integrate with AWS OpenSearch or Amazon Kendra to provide similar functionality. For applications involving LLMs, personalized recommendations, or semantic queries, MongoDB has a clear edge thanks to its built-in support.
Data modeling, partitioning, and scaling guidance#
Data modeling is one of the most critical aspects of using any NoSQL database effectively. Both MongoDB and DynamoDB follow different philosophies:
MongoDB uses a flexible schema design that allows nested documents and arrays, making it easy to evolve your schema as requirements change.
DynamoDB, on the other hand, requires you to think in terms of access patterns upfront. Choosing a good partition key is essential to avoid hot partitions and performance bottlenecks.
Understanding these design principles early will save you from costly migrations and ensure optimal performance as your application scales.
Security, governance, and compliance#
Security has become a top priority in database design, and both platforms have strengthened their offerings:
MongoDB offers field-level encryption, role-based access control, auditing, and VPC peering. Atlas also provides compliance with GDPR, HIPAA, SOC 2, and other industry standards.
DynamoDB integrates tightly with AWS IAM for access control, supports encryption at rest and in transit, and provides VPC isolation and CloudTrail logging.
The differences are subtle but important. MongoDB offers more granular control over data governance, while DynamoDB benefits from deeper integration with AWS’s security ecosystem.
Real-world operational considerations#
Beyond features, real-world operations — such as migration, backup, and maintenance — are critical to your database choice.
Migration: MongoDB provides tools like mongomirror and Atlas Live Migration Service. DynamoDB can use AWS Database Migration Service (DMS) for migrating from SQL or NoSQL sources.
Backups and restores: Both databases now offer point-in-time recovery and automated backup solutions.
Monitoring and observability: Atlas integrates with tools like Prometheus and Datadog, while DynamoDB integrates with Amazon CloudWatch and AWS X-Ray.
Considering these operational aspects early can help you choose the database that best fits your team’s experience and infrastructure.
Wrapping up and next steps#
We’ve talked a lot today about the pros and cons of MongoDB and Amazon DynamoDB and differences between the two services, but the ultimate question is, “Which one is right for you?” That answer depends on your project’s specific needs and resources.
For fast, efficient access to key-value data, consider DynamoDB, which shines in high-performance scenarios. Need a flexible data model with support for intricate queries? MongoDB can be your go-to. And if you’re already in the Amazon ecosystem, DynamoDB seamlessly integrates with it, making it a solid choice.
If you’re already using AWS, DynamoDB’s simpler setup process and better integration tools make it the ideal choice in most scenarios. While MongoDB can be deployed on AWS the same as on any other cloud provider, it isn’t designed with AWS in mind like DynamoDB is. DynamoDB is also simply easier to use. While feature-rich, MongoDB has a bit of a learning curve, which can take up precious development time.
If you want to build a multi-cloud infrastructure or simply use the database on-premise, MongoDB is the best choice between the two. In truth, it’s the only choice in these cases since DynamoDB is only usable through AWS.
That said, this was only a sampling of these NoSQL databases. There’s so much more to learn and explore beyond MongoDB and DynamoDB through Educative courses and learning paths. If you want to discover more about DynamoDB and AWS, be sure to check out our Become an AWS Professional learning path. And our MongoDB: The Complete Guide course is ideal if you want to become an expert in all things Mongo.
Happy learning!
Continue learning about databases#
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Which is better, DynamoDB or MongoDB?
Which is better, DynamoDB or MongoDB?
Written By:
Zephin Livingston
