DynamoDB vs DocumentDB: Choosing the Right AWS NoSQL Database

When building applications on AWS that require NoSQL databases, two popular options are Amazon DynamoDB and Amazon DocumentDB. While both are fully managed AWS services, they serve different use cases and have different architectures, pricing models, and compatibility considerations.

This article provides an in-depth comparison of these two AWS NoSQL database services to help you make an informed decision based on your specific requirements.

Overview: DynamoDB vs DocumentDB at a Glance

Feature	Amazon DynamoDB	Amazon DocumentDB
Type	Serverless NoSQL key-value and document store	MongoDB-compatible document database
Architecture	Serverless, fully managed	Cluster-based with instances
Compatibility	Proprietary API	MongoDB 3.6/4.0 compatible API
Scaling	Automatic, unlimited	Manual instance scaling + storage auto-scaling
Pricing Model	Pay-per-request or provisioned capacity	Instance-based pricing
Data Model	Key-value with document support	Document-oriented (BSON)
Query Language	DynamoDB API, limited PartiQL	MongoDB Query Language
Primary Use Case	High-scale applications with simple queries	MongoDB workloads with complex queries

Service Overview & Core Concepts

DynamoDB: AWS’s Native NoSQL Database

Amazon DynamoDB is a fully managed, serverless NoSQL database service that AWS built from the ground up. Key characteristics include:

Truly serverless: no instances or clusters to manage
Automatic scaling with no capacity planning required
Single-digit millisecond response times at any scale
Seamless regional and global replication options
Designed for high availability (99.999% uptime SLA)
Integrated with AWS IAM for security
Ideal for applications requiring simple, predictable access patterns

DocumentDB: AWS’s MongoDB-Compatible Service

Amazon DocumentDB is AWS’s MongoDB-compatible document database service. Key characteristics include:

Cluster-based architecture with instances (similar to Amazon RDS)
MongoDB compatibility (API compatibility with MongoDB 3.6 and 4.0)
Storage automatically scales with your data
Computing resources (instances) require manual scaling
Multi-AZ deployment for high availability
Designed to simplify migration from MongoDB to AWS
Suitable for workloads requiring complex document queries

Data Model & API Differences

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DynamoDB Data Model

DynamoDB uses a key-value and document data model:

Tables contain items (similar to documents)
Each item must have a primary key:
- Simple primary key (partition key only)
- Composite primary key (partition key + sort key)
Maximum item size of 400KB
Limited query flexibility (primarily key-based access)
Secondary indexes for additional access patterns
No schema enforcement beyond the primary key
Attribute types include string, number, binary, Boolean, and collections

The DynamoDB API is proprietary, with operations like:

// Example: Getting an item by key
const params = {
  TableName: 'Users',
  Key: {
    'UserId': 'user123'
  }
};
dynamodb.getItem(params, function(err, data) {
  // Handle response
});

DynamoDB recently added PartiQL support for SQL-like queries, but with significant limitations compared to full SQL or MongoDB’s query language.

DocumentDB Data Model

DocumentDB uses MongoDB’s document-oriented data model:

Collections contain documents (BSON format)
Documents can be up to 16MB in size
Flexible schema (documents in a collection can have different structures)
Rich query language for complex filtering and aggregation
Support for array operations, nested document queries
Robust indexing options
Native support for geospatial data

DocumentDB uses the MongoDB API, making it familiar to MongoDB users:

// Example: Finding documents with a filter
db.users.find({
  age: { $gt: 21 },
  status: "active",
  "address.city": "Seattle"
})

This query flexibility is a key differentiator, allowing complex data access patterns that would be challenging to implement in DynamoDB.

Performance & Scalability

DynamoDB Performance

DynamoDB’s performance characteristics include:

Consistent, predictable performance at any scale
Single-digit millisecond response time
Automatic partitioning across storage nodes
Virtually unlimited throughput and storage capacity
Performance tied to capacity provisioning or on-demand usage
Optimized for high-throughput, low-latency operations
Global Tables for multi-region low-latency access

Scalability in DynamoDB is handled automatically:

No manual scaling operations required
Scales up and down based on traffic
No performance degradation when scaling
Automatically rebalances partitions as data grows

DocumentDB Performance

DocumentDB’s performance characteristics include:

Performance depends on instance types selected
Vertical scaling by changing instance types
Horizontal scaling with read replicas (up to 15)
Storage auto-scales up to 64TB
Performance may vary based on query complexity
Not as predictable at extreme scale as DynamoDB
More query flexibility at the cost of some performance

Scaling DocumentDB requires more planning:

Manual scaling of instances (T3, R4, R5, R6g)
Elasticity limited by instance boundaries
Need to monitor and adjust instance sizes
No automatic compute scaling based on load

Consistency & Transactions

DynamoDB Consistency

DynamoDB offers two consistency models:

Eventually Consistent Reads (default):
- Lower cost and higher throughput
- May return stale data
- Best for non-critical reads
Strongly Consistent Reads:
- Always returns the most up-to-date data
- Higher latency and cost
- Best for operations requiring the latest data

DynamoDB also supports ACID transactions for multi-item operations, allowing atomic updates across multiple items and tables.

DocumentDB Consistency

DocumentDB provides a MongoDB-compatible consistency model:

Read preference options (primary vs secondary reads)
Write concern settings for durability
Causal consistency for session-based operations
Transaction support similar to MongoDB 4.0

This model will be familiar to MongoDB users but requires more understanding of the various consistency settings compared to DynamoDB’s simpler approach.

Operational Considerations

DynamoDB Operations

DynamoDB’s operational profile is extremely simple:

Zero administration - no servers, instances, or clusters
No software to install, patch, or maintain
Automatic backups with point-in-time recovery
Monitoring via Amazon CloudWatch
On-demand capacity eliminates capacity planning
Automatic scaling without manual intervention

This operational simplicity is a major advantage for teams wanting to focus on application development rather than database management.

DocumentDB Operations

DocumentDB has a more traditional operational model:

Requires instance selection and sizing
Cluster configuration (primary + replicas)
Instance failures require automatic or manual failover
Patching and maintenance windows
Backup management (though automated)
Performance tuning and index optimization

While still easier than self-hosted MongoDB, DocumentDB requires more operational attention than the completely serverless DynamoDB.

Pricing Models

DynamoDB Pricing

DynamoDB offers two pricing models:

On-Demand Capacity Mode:
- Pay-per-request pricing
- No capacity planning required
- Automatically scales up/down
- More expensive per request
- Great for variable or unpredictable workloads
Provisioned Capacity Mode:
- Specify read/write capacity units
- Lower cost for predictable workloads
- Auto-scaling available
- Reserved capacity for further discounts
- Capacity planning required

Additional costs include storage, data transfer, backups, and optional features (Global Tables, etc.).

DocumentDB Pricing

DocumentDB uses an instance-based pricing model:

Costs based on instance hours
Instance types determine compute pricing
Storage billed per GB-month
I/O operations charged separately
Backup storage beyond free tier
Data transfer costs

This model is similar to RDS pricing and typically has a higher minimum cost than DynamoDB’s on-demand pricing, but may be more cost-effective for steady, high-throughput workloads with predictable patterns.

MongoDB Compatibility

DynamoDB MongoDB Compatibility

DynamoDB has no direct MongoDB compatibility:

Different APIs and SDKs
Different query language
Different data modeling approaches
Migration from MongoDB to DynamoDB requires significant application changes
Some third-party tools can help map between the two

DocumentDB MongoDB Compatibility

DocumentDB was designed specifically for MongoDB compatibility:

Compatible with MongoDB 3.6 and 4.0 drivers and tools
Supports many MongoDB API operations and query patterns
Native support for MongoDB shells and utilities
Designed to minimize code changes when migrating from MongoDB
Some MongoDB features are not supported or behave differently

This compatibility makes DocumentDB the natural choice for existing MongoDB workloads being migrated to AWS, though it’s worth noting that DocumentDB is not 100% compatible with all MongoDB features.

Development Experience

DynamoDB Developer Experience

Developing with DynamoDB involves:

AWS SDK in your preferred language
AWS CLI for operations
AWS Console for management
NoSQL Workbench for data modeling
PartiQL for SQL-like queries (with limitations)
DynamoDB Local for local development

Third-party tools like Dynomate enhance the DynamoDB developer experience with:

Visual query building
Data visualization and exploration
Schema and table management
Performance monitoring
Multi-account support

DocumentDB Developer Experience

The DocumentDB development experience is more like working with MongoDB:

MongoDB drivers and libraries
MongoDB Shell (mongo)
MongoDB tools for data management
Familiar MongoDB query syntax
Support for MongoDB migration tools
MongoDB Compass (with some limitations)

Use Cases: When to Choose Each Database

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When to Choose DynamoDB

DynamoDB is generally the better choice when:

You need extreme scalability and performance:
- Internet-scale applications
- Applications with unpredictable traffic spikes
- Use cases requiring consistent single-digit millisecond latency
Your access patterns are simple and well-defined:
- Key-based lookups
- Range queries on a known sort key
- Simple filter operations
You want a truly serverless database:
- Zero operational overhead
- Pay-per-request pricing
- Automatic scaling without intervention
Your application is AWS-native:
- Integration with Lambda, API Gateway
- IAM-based security model
- Event-driven architectures with DynamoDB Streams

When to Choose DocumentDB

DocumentDB is generally the better choice when:

You’re migrating from MongoDB:
- Existing MongoDB applications
- Teams with MongoDB expertise
- Minimal code changes desired
You need complex query capabilities:
- Rich query expressions
- Complex document filtering
- Aggregation pipelines
- Geospatial queries
Your data model is document-centric and complex:
- Deeply nested documents
- Frequently changing schema
- Need for rich indexing options
You prefer MongoDB’s query language and tools:
- Familiarity with MongoDB Shell
- Use of MongoDB drivers
- Integration with MongoDB ecosystem tools

Migration Considerations

Migrating to DynamoDB

If you’re considering migrating to DynamoDB:

Plan for data modeling changes (single-table design is common)
Restructure queries around key-based access patterns
Consider denormalization for query efficiency
Use AWS Database Migration Service (DMS) for help
Evaluate AWS AppSync for GraphQL on top of DynamoDB
Consider progressive migration for large applications

Migrating to DocumentDB

If you’re considering migrating to DocumentDB:

For MongoDB migrations, use standard MongoDB tools
Test for compatibility issues with your MongoDB version
Review performance characteristics with your workload
Ensure instance sizing is appropriate for your needs
Check for unsupported MongoDB features
Consider AWS Database Migration Service (DMS) for other sources

Tools for Management and Monitoring

DynamoDB Tools

AWS provides several tools for working with DynamoDB:

AWS Management Console
AWS CLI
AWS SDKs
CloudWatch for monitoring
NoSQL Workbench for data modeling

Third-party tools like Dynomate offer enhanced capabilities for:

Visual exploration and query building
Performance analysis
Schema management
Data import/export
Multi-account management

DocumentDB Tools

For DocumentDB, you can use both AWS tools and MongoDB-compatible tools:

AWS Management Console
AWS CLI
MongoDB Shell
MongoDB Compass (some features)
MongoDB utilities
CloudWatch for monitoring

Cost Comparison Example

To illustrate pricing differences, consider a simple comparison:

Example Scenario: An application with 20 million reads and 5 million writes per day, storing 100GB of data.

DynamoDB On-Demand Cost (Approximate):

Reads: 20M × $0.25/1M = $5.00/day
Writes: 5M × $1.25/1M = $6.25/day
Storage: 100GB × $0.25/GB = $25.00/month
Monthly total: ~$365

DocumentDB Cost (Approximate):

db.r5.large instance: ~$219/month
Storage: 100GB × $0.10/GB = $10/month
I/O: Variable based on usage
Monthly total: ~$250-350 depending on I/O usage

The cost comparison shows that for predictable, moderate workloads, DocumentDB might be more cost-effective, while DynamoDB’s on-demand pricing offers better scaling to zero and handling of unpredictable traffic.

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Conclusion: Making Your Decision

The choice between DynamoDB and DocumentDB comes down to several key factors:

Query complexity: If you need complex queries and MongoDB compatibility, DocumentDB is likely better
Operational model: If you prefer zero operational overhead, DynamoDB’s serverless model is superior
Scaling requirements: For unlimited automatic scaling, DynamoDB excels
Pricing model: For variable or low-volume workloads, DynamoDB’s on-demand pricing offers advantages
MongoDB migration: For existing MongoDB applications, DocumentDB provides an easier transition path

Both services are excellent NoSQL database options within the AWS ecosystem, but they solve different problems:

DynamoDB shines for high-scale, low-latency applications with well-defined access patterns and serverless architectures
DocumentDB excels for MongoDB workloads requiring complex queries and flexible document structures

For teams that choose DynamoDB, tools like Dynomate can significantly improve the developer experience, making it easier to work with DynamoDB’s unique data model and access patterns through intuitive visual interfaces and advanced management features.