Quick verdict: Choose DynamoDB if you’re building on AWS, know your main access patterns, and want a managed database without maintaining servers. Consider MongoDB Atlas if your application needs flexible queries, aggregations, or managed hosting across AWS, Azure, and Google Cloud. Neither is a universal winner on performance or price: test your actual queries, consistency requirements, and peak traffic before committing. Self-managed MongoDB is a separate operational choice, not the same proposition as Atlas: your team takes responsibility for server upkeep and security configuration. Start with your workload, then compare the cost of a configuration that actually supports it.
So you’re at the point of your project where the discussion of SQL vs. NoSQL is old news, and NoSQL is the path forward.
Now you’re in the market for the actual product you’re going to use as your database. Since you’re reading this article, you have probably narrowed it down to 2 contenders: DynamoDB and MongoDB.
Let’s explore the details of each one of these products so you can make an informed decision.
Below I’m going to cover the following:
- The differences between DynamoDB and MongoDB along with the advantages/disadvantages of each one.
- Tips on choosing the right option for your particular needs.
| Decision point | DynamoDB | MongoDB Atlas |
|---|---|---|
| Managed hosting | AWS | AWS, Azure, Google Cloud |
| Query design | Partition keys, sort keys, alternate indexes | Flexible queries, compound indexes, aggregations |
| Maximum record size | 400 KB item | 16 MiB BSON document |
| Consistency | Read and deployment options matter | Read/write concerns and read preference matter |
| Billing basis | Requests or provisioned capacity, plus storage | Deployment tier and configuration |
| Performance check | Key distribution, throttling, capacity | Query efficiency, indexes, capacity |
How does DynamoDB differ from MongoDB?
While DynamoDB and MongoDB aim to solve the same problem, store data in a NoSQL fashion, they approach that goal differently.
Let’s look at the main differences between them that you need to be aware of to make the right choice for you or your business.
Infrastructure management
The first difference is infrastructure management.
MongoDB Community Server is source-available under the SSPL, not an OSI-approved open-source license, whereas DynamoDB is a service provided by Amazon via Amazon Web Services (AWS).
What this means in practice is that, when it comes to infrastructure management, you’re looking at a completely different picture than you would from a development perspective.
While DynamoDB is only available as part of AWS, MongoDB can be deployed in the hosting environment of your choice. This is especially important if you’re worried about vendor lock-in problems.
On the other hand, with DynamoDB you don’t have to worry about provisioning and keeping servers running. You still configure recovery and access controls and monitor usage.
MongoDB Atlas is a managed commercial service available on AWS, Azure, and Google Cloud. It handles server upkeep, giving you an alternative to operating MongoDB yourself, although you still choose capacity, permissions, network policies, and recovery settings.
Installation and setup
Because it’s a fully managed system, getting things up and running with DynamoDB is a breeze, whereas self-managed MongoDB can be challenging to install and set up. With Atlas, you can create a managed deployment instead of installing MongoDB and maintaining its underlying servers.
Security
When it comes to security, both products offer strong controls, but your responsibilities depend on the deployment. Being a part of AWS, the AWS Identity and Access Management (IAM) permission model applies to DynamoDB, as it does with AWS services like EC2, RDS, and others.
DynamoDB has internet-accessible HTTPS service endpoints that require authorization; this does not make your tables publicly readable. VPC endpoints provide private network paths.
MongoDB Atlas provides secure defaults, including authentication, TLS, encryption at rest, and network controls. Customers still configure appropriate permissions and network policies. With self-managed MongoDB, your team is responsible for configuring authentication, encryption, network restrictions, and ongoing security maintenance.
Database structure
MongoDB stores its information in a JSON-like, flexible-schema BSON format.
The main stored objects are called documents, which are grouped into collections.
In MongoDB you don’t need to create a collection before inserting new documents into it, making it really fast to put to use.
A very quick example would be running the following code straight from the command line client:
db.users.insertOne( { 'name': 'John Doe' } )This code would insert a new document into the “users collection” if available, or create it otherwise.
In the case of DynamoDB, its databases use some terminology similar to relational databases; you have tables that contain items which in turn have attributes. Although, not every item in a table must have the same number of attributes.
In order to use a table, you must first create it and define a primary key.
You can create tables and insert items in DynamoDB through an SDK, the AWS CLI, the console, or service APIs. An SDK is not mandatory, and the table terminology does not imply support for relational joins.
Document size
Another difference between DynamoDB and MongoDB is document size. DynamoDB will allow a maximum of just 400 KB per item, including attribute names and values, while MongoDB has a limit of 16 MiB per BSON document.
If you need to store big objects with DynamoDB, AWS recommends using another service, such as S3. Bear in mind that while this solution can work, it introduces extra requests and coordination between the database item and the stored object. DynamoDB transactions cannot atomically update an S3 object alongside an item.
Querying data
DynamoDB supports more than key-value lookups. Its Query operation requires a partition-key value and can optionally restrict results using a sort-key condition. Global secondary indexes (GSIs) support alternate access patterns, while Scan reads across a table or index. Filters run after reading, so discarded items still consume read capacity.
However, you can leverage complementary AWS services, such as Elastic MapReduce, to achieve aggregation and other sorts of more sophisticated queries.
MongoDB has its own query language, which allows developers to analyze data in a wide variety of ways (including limited joins, geospatial, and aggregations).
MongoDB’s aggregation pipelines and compound indexes help support these queries, but suitable indexes and capacity still matter. DynamoDB GSIs also add storage and write work, which should be included in your design and budget.
Imagine your order history page needs “orders for this customer between two dates.” In DynamoDB, you could use customerId as the partition key and a sortable date plus order identifier as the sort key. That makes the date range part of the key condition rather than a filter. In MongoDB, a compound index on customerId and orderDate is a natural candidate to test for the same query.
Now imagine a product catalog where categories keep gaining attributes and shoppers need new combinations of filters. Both databases can store varying attributes, but storing them isn’t the same as querying them efficiently. MongoDB’s query capabilities make it worth considering here. With DynamoDB, map each important lookup to a key or index and price that design before committing.
Data types
DynamoDB supports number, string, binary, Boolean, null, list, map, and sets of strings, numbers, or binary values. Only primary-key attribute types are restricted to string, number, or binary.
MongoDB has a more extensive set of data types, including int, long, date, timestamp, double, decimal128, and ObjectId. Geospatial support is a query and indexing capability, not a separate BSON data type.
Data integrity
DynamoDB supports eventually consistent and strongly consistent reads on tables and local secondary indexes. GSI and Stream reads are eventually consistent. An eventually consistent read may temporarily return stale data; it does not mean a successful write was lost. DynamoDB also supports multi-item ACID transactions across tables in the same AWS account and Region.
Global tables require a separate consistency decision. Multi-Region eventual consistency (MREC) replicates asynchronously, and transaction atomicity is local to the originating Region. Multi-Region strong consistency (MRSC) requires exactly three Regions, using three replicas or two replicas plus a witness. MRSC does not support transaction operations, TTL, or local secondary indexes; its GSIs remain eventually consistent.
MongoDB’s guarantees depend on read concern, write concern, and read preference, rather than every read being strongly consistent by default. Majority reads return majority-committed data; linearizable reads with majority writes can provide real-time ordering for a single document. MongoDB also supports multi-document transactions on replica sets and sharded clusters, with snapshot read concern needed for a consistent multi-shard snapshot.
DynamoDB vs. MongoDB Performance
When it comes to performance, there is no universal winner: access patterns, indexes, item sizes, consistency settings, and allocated capacity affect the result.
If you’re going to be running many complex queries involving more than one table/collection, you’ll most likely be better off using MongoDB when its native query capabilities fit the workload. Using additional services with DynamoDB can add requests and data movement, but the latency depends on the architecture rather than an unavoidable penalty for every query.
Test representative queries under expected peak traffic and consistency settings. Watch for hot partition keys and throttling in DynamoDB, and inefficient queries, missing indexes, or insufficient capacity in MongoDB.
Don’t stop at average latency. Track p95 and p99, the thresholds covering 95% and 99% of observed request times, for each important operation. Those slower requests matter when you’re setting response-time targets. Measure from your application and include retries, rather than reporting only successful first attempts.
Use this checklist to keep your benchmark useful:
- Match expected dataset size, record sizes, and read/write mix, including your largest common responses.
- Exercise normal traffic, peak concurrency, and uneven demand for particular customers or products.
- Use the indexes and consistency guarantees you intend to deploy, including transactions where required.
- Record p95/p99 alongside throughput, errors, throttling, and the tested configuration’s cost.
Treat the result as evidence for that workload and configuration, not a ranking that automatically applies to every application.
DynamoDB vs. MongoDB Cost
DynamoDB can be more expensive for some workloads, but MongoDB does not universally provide savings in terms of service fees. DynamoDB on-demand billing meters requests, while provisioned billing charges for allocated capacity; Atlas charges depend on its deployment tier and configuration.
But keep in mind, if you use self-managed MongoDB, it will be up to your own infrastructure team to keep everything running smoothly (monitoring the services, scaling storage up and down, etc.). While that approach provides more fine-grained control, the cost of additional data team salaries can quickly surpass your AWS bill.
However, MongoDB also offers add-on support services, which may be a better (and potentially cheaper, depending on the agreement and your staffing needs) option than handling everything internally.
The following prices are current as of October 2026. DynamoDB figures are for Standard tables using on-demand capacity in US East (N. Virginia); Atlas prices depend on provider, region, and configuration.
| Service or usage | Price |
|---|---|
| DynamoDB writes | $0.625 per million write request units |
| DynamoDB reads | $0.125 per million read request units |
| DynamoDB storage | $0.25 per GB-month |
| Atlas Free | $0, with 512 MB storage |
| Atlas Flex | $8 to $30 per 30 days, with 5 GB storage and shared resources |
| Atlas Dedicated | Advertised from $0.08/hour, or $57.60 for an illustrative 720 hours |
For standard, nontransactional operations, a write of up to 1 KB consumes one write request unit, and a strongly consistent read of up to 4 KB consumes one read request unit. An eventually consistent read uses half that read capacity.
For a 30-day variable-traffic prototype with 1 million such writes, 5 million such strong point reads, and 1 GB of average billed storage, the DynamoDB subtotal is $1.50: $0.625 for writes, $0.625 for reads, and $0.25 for storage. Atlas Free is too small for that dataset; Flex is an option only if the workload fits its storage and operations limits.
For a steady production workload with 100 million such writes, 500 million such strong point reads, and 100 GB of billed storage, the subtotal is $150: $62.50 for writes, $62.50 for reads, and $25 for storage. Provisioned capacity is also worth pricing for predictable traffic. An adequate Atlas Dedicated tier cannot be inferred from monthly request counts, and the advertised starting price is not a quote for this workload.
Both examples ignore all free allowances and credits, including eligible DynamoDB free storage, and exclude indexes, transactions, backups, replication, transfer, and tax. Atlas backup, transfer, and customized storage charges may also apply. Neither calculation establishes equivalent performance between the services.
Before using those subtotals as your budget, check three common pitfalls:
- Item size: Don’t treat every DynamoDB request as one unit. Price the actual item sizes and read consistency you expect.
- Indexes and replicas: Include GSI storage and write work, plus your chosen replication configuration, rather than budgeting only for base data.
- Atlas resource sizing: Test the compute and storage configuration your queries need. Monthly request totals alone don’t establish an adequate tier.
How should you decide between DynamoDB and MongoDB?
So far, you’ve learned the main differences between DynamoDB and MongoDB along with the advantages and disadvantages of each one. Now it’s time to put that information to use and apply it to your particular situation.
If your business already relies on AWS services, then DynamoDB is probably a good option for you, provided its access patterns and consistency options fit your application. It’ll be easy to deploy, maintain, and integrate with your current infrastructure.
If, on the other hand, you’re planning on putting together a multi-cloud infrastructure, then MongoDB is worth considering. Its hosting choices offer flexibility, although moving between environments still takes planning.
Of course, you can find yourself in neither one of these scenarios, which calls for a more detailed analysis. Here are some things to consider:
Skill level
First of all, you should assess the skill level of your team members and consider the following questions:
- Will they be able to implement a MongoDB?
- Will they be able to make sure it runs smoothly 24×7?
If you answer no to those questions, then you should give managed options such as DynamoDB or MongoDB Atlas a deeper look.
Another option you might want to consider is MongoDB Atlas (a managed MongoDB service similar to DynamoDB).
Speed
Also, take into account how much speed your application needs and which consistency guarantees each operation requires. This isn’t a choice between speed and trustworthy data: both products offer consistency controls, and stale reads are different from lost writes. Compare latency under the guarantees you actually need rather than assuming either database must take seconds to return reliable results.
Complexity
Last but not least, you have to evaluate how complex your queries will be. If your application won’t perform complex queries, DynamoDB may be a good fit; that doesn’t rule out MongoDB if its document model, tooling, or hosting options suit your needs.
To turn that into a shortlist, consider these hypothetical workloads:
- AWS order-history service: Start by evaluating DynamoDB when customer-and-date lookups dominate and you can define the other access patterns upfront.
- Evolving retail catalog: Start by evaluating MongoDB Atlas when changing attribute filters and aggregations are central, then validate indexes and capacity.
- Document application requiring your own hosting environment: Consider self-managed MongoDB if your team can operate it securely. If you don’t need that control, compare Atlas before taking on server maintenance.
Something to consider
Regardless of your choice between MongoDB and DynamoDB, if you want to do proper data analysis, consider whether operational queries are enough or whether a separate analytics platform would help.
Exports or change-data pipelines can feed BI tools and dashboards without making the operational database handle every reporting workload. Account for data freshness, pipeline maintenance, and additional storage and processing costs.
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