If picking the best database management system (DBMS) were a road trip, both MongoDB and DynamoDB would get you there. Ultimately, it comes down to choosing whether you need the specialized efficiency of DynamoDB within Amazon Web Services (AWS) or the “off-road” flexibility of platform-agnostic MongoDB.
What do road trips and off-road antics have to do with deciding between MongoDB vs. DynamoDB?
In the next few minutes, we’ll share the key differences between the two leading NoSQL database systems, helping you choose the best fit for your needs before spending any money.
What Is Amazon DynamoDB?
With Amazon DynamoDB, you get a fully managed, serverless NoSQL database that offers low latency (single-digit milliseconds) and auto-scaling that supports high-demand applications. This makes DynamoDB a popular choice for web and mobile apps requiring low-latency data access at any scale.
Image: DynamoDB features
The platform is also popular for these capabilities:
- Document and key-value data models (flexible schema) make it versatile for applications with complex data retrieval or real-time processing needs.
- Automatic scaling adjusts capacity based on workload. DynamoDB can handle over 10 trillion daily requests, accommodating sudden traffic spikes without manual intervention.
- It’s optimized for cost-efficiency, with a pay-per-request pricing model that ensures users only pay for the read and write operations performed, a significant advantage over traditional databases with fixed infrastructure costs.
- It also includes robust security, built-in data backup, and global replication, offering high availability and durability across multiple regions.
For database professionals, DynamoDB’s integrations with other AWS services, such as AWS Lambda and Amazon Kinesis, enable seamless data streaming, event-driven workflows, and extensive data analytics.
These features, along with strong security protocols and ease of use, make DynamoDB an appealing choice for handling large-scale, distributed data needs in modern applications.
What Is MongoDB?
MongoDB is a document-oriented, NoSQL database that handles structured, semi-structured, and unstructured data. Consider these capabilities:
Image: MongoDB features
As opposed to traditional relational databases, MongoDB organizes data in flexible, JSON-like documents (Binary JSON or BSON), which can have varying structures within the same collection.
This ensures you can adapt easily to changing data schemas without requiring extensive restructures.
- This schema-less design makes MongoDB ideal for applications with rapidly evolving data models, like content management systems, real-time analytics, and e-commerce platforms.
- Horizontal scaling through sharding distributes data across multiple servers for efficient load balancing and high availability in high-traffic applications. This scaling is seamless without downtime, a significant improvement over traditional relational databases.
- MongoDB Atlas, its fully managed cloud service, further improves scalability by distributing data across cloud providers like AWS, Azure, and Google Cloud. Atlas is the direct alternative to Amazon DynamoDB.
- MongoDB works anywhere — local servers, mobile devices, cloud services, and more. Amazon DynamoDB is limited to Amazon Web Services.
- It also offers specialized indexing, including geospatial indexing, and an aggregation framework that supports complex data transformations. This makes it ideal for real-time data processing, location-based services, and analytics-heavy applications.
Database professionals often choose MongoDB for its high performance, speed, and flexibility, especially in projects requiring horizontal scaling and fast data retrieval. And its robust API support across multiple programming languages also makes it a versatile choice for modern, cross-platform applications.
DynamoDB Vs. MongoDB: Key Differences To Know Before You Switch
Here’s the deal. Undoubtedly, MongoDB and DynamoDB have advantages over traditional relational database systems. The question is, what do you do if you’re deciding between DynamoDB and MongoDB?
Here’s a side-by-side comparison of DynamoDB vs MongoDB to help you decide which DBMS best suits your requirements.
|
MongoDB |
Amazon DynamoDB | |
|
Database type |
Multi-purpose NoSQL database |
Serverless NoSQL database |
|
Data model and schema |
Document-oriented (data is organized in collections, comparable to tables in relational database systems) Schemaless design accommodates structured, semi-structured, and unstructured data types Schema-on-read, no strict schema |
Primarily key-value oriented with support for documents Flexible schema (schema-on-write, defined during table creation); relies on partition and sort keys |
|
Data types |
Regular JSON BSON
|
Number Binary String |
|
Database management |
Depends on the version;
|
Fully managed, serverless DBMS, minimizing manual infrastructure configuration and maintenance DynamoDB only runs on serverless instances |
|
Deployment |
Anywhere:
|
Cloud only Only available on Amazon Web Services (AWS) |
|
Open-source or proprietary |
Community Server edition is open-source and free MongoDB Atlas offers a free (M0) tier and paid tiers with advanced capabilities MongoDB Enterprise Advanced is “source-available”; and licensed under the Server Side Public License (SSPL), not the General Public License (GPL), so it requires a commercial license to offer MongoDB as a service Not fully open-source |
Not free or open-source — this is a proprietary AWS service However, it offers a Free Tier with limited read/write capacity and 25GB of storage for introductory testing and development |
|
Supported languages |
Officially supported:
The MongoDB community also offers more ways to use additional languages |
Officially supported:
|
|
Indexing |
MongoDB can handle up to 64 mutable indexes for each collection. Dynamically changing the structure of the document. Indexes strongly consistent with the underlying data. Always returns the latest results. Supports any query pattern by creating unique, hash, array, partial, TTL, compound, geospatial, text, sparse, and wildcard indexes No additional charge for indexing your data MongoDB allows defining secondary indexes on any field, at any time, even for deeply nesting array elements |
DynamoDB handles up to 20 mutable global indexes per table (not inconsistent with the underlying data) and up to 5 local indexes (strongly consistent while creating tables; not modifiable after table creation) Supports only hash or hash ranges. Supported Global Secondary Indexes (GSI) and Local Secondary Indexes (LSI) have limits on their number and flexibility Indexes are sized, provisioned, and billed separately from the data Global secondary indexes may only be declared on top-level item elements. No indexing for sub-documents or arrays, limiting complex queries |
|
Querying |
Rich query language (MongoDB Query Language); ranges, single keys, joins, graph traversals, faceted search, geospatial queries, and more Features advanced aggregation stages that resemble UNIX pipes to handle data analytics |
Key-value queries (primary-key can have only a maximum of two attributes, which limits query flexibility). DynamoDB Query API. Or, PartiQL Querying only available in global secondary indexes (GSI) and local secondary indexes |
|
Transactions |
1,000 operations/transaction; executed within 60 seconds, by default |
Up to 100 writes/transaction |
|
Performance and monitoring |
Scalability is horizontal with sharding. Autoscaling is only available for MongoDB Atlas. Otherwise, it is a manual effort in the self-managed editions |
Supports auto-scaling with read/write capacity units |
|
Data security and integrity |
Data security is self-managed for Community and Enterprise editions, heaping that workload on your team. Security is built-in for MongoDB Atlas ACID transactions apply to documents, backups, and indexes (across multiple collections and documents) Strongly consistent Data validation support with native schema governance |
Data encryption at rest. Security built-in with AWS IAM policy management The ACID standard only applies to table data (limited to single-table operations) Eventually consistent No data validation (handled in application) |
|
Supported document sizes |
Up to 16 MB |
Up to 400kb |
|
Memory and Storage |
Uses WiredTiger storage engine to allocate memory based on your system’s RAM. In self-hosted editions of MongoDB, you’ll have some control over this allocation MongoDB Atlas scales resources based on demand, like Amazon DynamoDB |
Uses SSD-based storage with automatic compression Automatically scales resources based on demand |
|
Backups |
Self-managed for self-hosted editions. Built-in for MongoDB Atlas On-demand, snapshots, or continuous backups Backups are queryable. No need to restore data to search backups |
Built-in and automatic, powered by AWS multi-region infrastructure On-demand or continuous backups No queryable backups. |
|
Pricing |
Flexible based on usage for MongoDB Atlas On-premises (self-hosted) pricing based on RAM sizing or number of nodes |
Flexible, based on usage |
Table: A side-by-side comparison of DynamoDB vs MongoDB
DynamoDB Vs. MongoDB: Use Cases
MongoDB claims to be the “complete data developer platform.” It does back that up with its flexible schema, open-source/source available/fully managed offerings, and drivers for all leading programming languages.
With these features and others, MongoDB is well-suited to real-time analytics, geospatial applications, and agile development as well as handling unstructured data and integrating rapidly-changing data from multiple sources in diverse environments (not just on AWS).
Amazon DynamoDB, on the other hand, is ideal if you want a high-performing, NoSQL DBMS to handle your AWS database requirements. It also works natively with other AWS data and serverless platforms, including RedShift, Kinesis, and Lambda.
DynamoDB’s design is best suited to applications that require high-volume transaction processing, real-time analytics, multi-region replication, and autoscaling to handle millions of requests per second (such as gaming applications).
To truly understand how each platform can work for your specific use case, you’ll want to test-drive each. It’ll be worth it.
Pricing Comparison Of DynamoDB Vs. MongoDB
Your DynamoDB or MongoDB costs will reflect what each platform offers — and there are quite a few differences to be aware of here.
Take MongoDB pricing.
MongoDB offers various pricing tiers:
- A free and open-source Community Edition.
- A fully managed service (MongoDB Atlas). MongoDB Atlas pricing is based on cluster size. This includes factors such as RAM, storage, and compute resources. You pay for resources allocated per hour, with costs varying by cloud provider (AWS, Azure, GCP) and region. The serverless tier charges based on the number of operations you perform, such as $0.10 per million reads.
MongoDB Atlas pricing
- An Enterprise Edition for on-premises deployment. Pricing is generally predictable but can become expensive for large-scale applications due to the fixed costs associated with dedicated resources. You can also incur additional charges for backups and data transfers.
MongoDB Enterprise pricing
Check out our no-BS MongoDB pricing guide here if you’re like us and want to understand what you’re paying for (rather than just paying your monthly bill without knowing how MongoDB pricing works).
Now, compare that to Amazon DynamoDB pricing.
DynamoDB provides two primary pricing options:
- Provisioned Capacity (where users specify read/write capacity in advance). Billing is hourly for the provisioned read and write units.
- On-Demand Capacity (where users pay per request based on actual usage). Billing is based on the number of read and write requests processed.
Additionally, DynamoDB charges for data storage, backup options, and data transfer. We share all about Amazon DynamoDB costs in a detailed yet snackable DynamoDB pricing guide here.
DynamoDB’s pricing can be more variable, especially in On-Demand mode, which is ideal for applications with unpredictable workloads. The catch is that it can lead to higher costs during peak usage if not monitored and managed properly. We’ll share a single platform to help monitor and manage your MongoDB and DynamoDB costs in a few moments. Meanwhile…
Which Database Should You Choose?
Remember when we compared DynamoDB and MongoDB to choose the right vehicle for a road trip?
To continue with the analogy, MongoDB is more like a rugged off-road vehicle: highly adaptable, flexible, and capable of handling diverse terrains. It thrives in multi-cloud or hybrid environments, supports complex queries and data models, and can store various data types, including geospatial and time series data.
MongoDB’s flexible schema design also makes it a top choice for applications requiring frequent model changes and complex aggregations. So, if your journey involves diverse conditions and you need the flexibility to adapt quickly, MongoDB may be the better choice here.
DynamoDB, on the other hand, is like a fully serviced, long-haul truck: limited in amenities but optimized for heavy loads, reliability, and predictability across the AWS terrain, which it’s built specifically for.
DynamoDB will handle your high traffic and scale seamlessly within that ecosystem. Its design is tailored for applications that require consistent, fast read/write speeds with minimal maintenance. It’s the straightforward choice for AWS-heavy applications where simple, cost-effective scaling is a priority.
A Smarter Way To Monitor, Track, And Optimize Your Cloud Costs In One Place
It’s one thing to choose DynamoDB or MongoDB; it’s another to make the most of the platform. One key to maximizing your database ROI is to prevent overspending.
As promised, one cloud cost optimization platform helps you aggregate, analyze, understand, and share insights without overwhelming you with complexity.
With CloudZero, you get:
- A single source of truth for all database and data cloud cost insights. Bring together MongoDB, Amazon DynamoDB, Databricks, Snowflake, New Relics, and Datadog spend in one place for seamless, complete-picture analysis.
- A single view of all cloud costs from different clouds: AWS, Azure, GCP, and Oracle Cloud. This eliminates the cost blind spots that often lead to surprise bills.
- Use our MongoDB integration or DynamoDB integration to seamlessly connect, pull, and analyze your database spend in a single pane of glass.
- Allocate 100% of your MongoDB and DynamoDB costs in minutes to a few hours – no matter how complex your environment is.
- Break down your MongoDB and DynamoDB costs into highly digestible and actionable cost intel, including Cost per Customer, Request, Service, Feature, and more.
- Allocate shared costs so you can tell who, what, and why your database costs are changing and so you can fix it.
- Get timely, noise-free, and contextual cost anomaly alerts to prevent overspending.
You’ll be in good company, too. CloudZero is helping ambitious brands like Uptly (saved $20 million), New Relic, and Drift (saved over $3 million) reduce cloud costs and maximize returns. Our customers say CloudZero pays for itself on average within three months. You can try CloudZero risk-free. 
to experience CloudZero for yourself.
The Cloud Cost Playbook
The step-by-step guide to cost maturity
