DynamoDB vs DocumentDB: Which NoSQL Database Fits?

Choose DynamoDB for key-based application access with managed partitioning and request-based on-demand billing. Choose Amazon DocumentDB when MongoDB-compatible drivers, document queries and aggregation pipelines fit the application better. Both store flexible documents, but their query models, transaction boundaries and scaling options differ; DocumentDB is not simply hosted MongoDB, and DynamoDB is not limited to flat key-value pairs.

DynamoDB and DocumentDB: an overview

DynamoDB is a managed key-value and document database. Items can contain nested maps and lists without every item having the same attributes. The primary key and indexes define efficient access patterns, such as fetching an order or listing recent activity for one customer.

DocumentDB stores documents and exposes MongoDB-compatible APIs. Queries and indexes can address document fields, and aggregation pipelines support richer transformations than a DynamoDB key query. This can fit content management, customer profiles and applications already built around MongoDB clients.

Both remove hardware management. They do not remove data modeling: DynamoDB needs access-pattern design, while DocumentDB needs appropriate indexes, query plans and capacity. Calling one service more scalable simply because it is a key-value store overlooks DocumentDB elastic clusters and its other deployment options.

DynamoDB vs DocumentDB at a glance

MongoDB compatibility: check the version and feature

The current DocumentDB compatibility guide lists MongoDB 4.0, 5.0 and 8.0 compatibility. DocumentDB 8.0 also supports applications built using MongoDB API versions 6.0 and 7.0. This describes API compatibility, not an identical MongoDB server implementation.

Review the functional differences and run the application's real queries before migrating. An application connecting successfully does not prove that every aggregation operator, index, retry option or transaction behaves as expected.

Feature support also varies within DocumentDB. The engine feature matrix distinguishes instance-based versions from elastic clusters. Elastic clusters use a MongoDB 5.0-compatible wire protocol and do not inherit all DocumentDB 8.0 features. Treat migration to them as a separate compatibility exercise.

For an existing MongoDB application, inventory driver versions, commands, index types, change-stream consumers and recovery requirements. Replay representative workloads in a test environment and verify both results and execution plans. If you need MongoDB itself, compare that requirement explicitly rather than assuming the AWS product name guarantees it.

Performance and latency

DynamoDB

DynamoDB suits requests that identify a small set of items through keys. A key lookup and a scan are not interchangeable performance tests. If an endpoint filters a large table after reading it, redesign the key/index access pattern or move that query to a more suitable engine.

DynamoDB secondary indexes support additional access paths but add storage and write work. DAX can cache eligible repeated reads, while native vector indexes support similarity search on on-demand tables. Neither feature removes the need to understand the application's ordinary item access patterns.

DocumentDB

DocumentDB query performance depends on indexes, available memory, query shape and concurrency. Read replicas can distribute eligible reads, but adding them does not automatically increase writer capacity or make an unindexed query efficient.

For example, a profile search across several fields may be simpler to express in DocumentDB. If that same application primarily retrieves one profile by user ID, DynamoDB may be a simpler operational fit. Benchmark those two endpoints separately instead of repeating a service-wide requests-per-second claim as a guarantee.

Replica reads can lag the writer. Make the application's freshness requirements explicit and select a suitable read preference. An administrative search screen may tolerate lag that a read-after-write confirmation cannot.

Availability, durability and transactions

DynamoDB replicates data across Availability Zones and provides optional multi-Region global tables. DynamoDB transactions provide atomic multi-item operations within their documented account and Region boundary. MRSC global tables do not support those transaction APIs.

DocumentDB separates compute from shared storage and supports replicas and failover. Deploy replicas across Availability Zones if the recovery design needs them, and test client reconnection during failover. A shared storage layer does not eliminate connection interruption or application retries.

Supported instance-based DocumentDB versions provide multi-document transactions. Elastic clusters do not support ACID transactions. This distinction can determine the architecture: a workflow that atomically updates several collections cannot assume it will work unchanged after moving to elastic clusters.

Scalability and storage limits

DynamoDB on-demand capacity handles variable demand without specifying provisioned throughput; provisioned mode supports explicitly configured capacity and auto scaling. Hot keys and quotas still matter. A 400 KB item limit also constrains whether an entire business document belongs in one item.

DocumentDB offers several separate ways to scale:

• Provisioned instance sizing changes compute and memory.
• Read replicas add capacity for eligible reads.
• DocumentDB Serverless adjusts instance capacity automatically for variable workloads.
• Elastic clusters distribute sharded collections across shards and have a different feature set.

Elastic clusters are therefore not just an auto-scaling switch on an ordinary cluster. Pick a shard key that spreads load and supports the queries, and check restrictions on indexes, transactions, change streams and recovery.

Current DocumentDB quotas distinguish deployment and engine limits; DocumentDB 8.0 supports cluster storage up to 256 TiB. Its 16 MiB document limit also differs from DynamoDB's item limit. Avoid choosing a database from maximum size alone: query behavior and working-set size usually become relevant earlier.

Security

DynamoDB encrypts data at rest by default, with AWS owned, AWS managed and customer managed key options. IAM and resource-based policies control access to tables and streams.

DocumentDB uses VPC networking, security groups, database authentication and TLS. Configure application credentials separately from permissions to administer the AWS resource. Avoid exposing the cluster broadly just to simplify a developer connection.

DocumentDB storage encryption is a cluster-creation choice. The console and CLI have different defaults, so declare the setting explicitly in infrastructure code. Encryption applies to the cluster's storage and related backups; changing an unencrypted deployment requires a supported snapshot/restore migration rather than toggling an existing cluster.

Backups and restore

DynamoDB offers on-demand backups and PITR with a configurable 1-to-35-day window. Restore creates a new table. Review indexes and application settings as part of the recovery process rather than treating a successful restore API call as the completed recovery.

For instance-based DocumentDB, continuous backups use a configured 1-to-35-day retention period, and manual snapshots support longer retention. The retention period is not always 35 days. AWS Backup can manage supported DocumentDB backups centrally.

Elastic clusters have different recovery capabilities: the documented limitations exclude point-in-time restore and AWS Backup integration. Validate the selected deployment's backup and restore process before migrating production data.

Pricing

DynamoDB pricing uses request units for on-demand throughput and capacity units for provisioned throughput. Add storage, index writes, backups and replication. Use the pricing calculator with actual item sizes and request patterns.

DocumentDB pricing includes provisioned compute or Serverless capacity, storage and backup. Standard storage charges database I/O separately; I/O-Optimized changes the compute/storage rates and includes database I/O. Elastic clusters use compute measured in vCPUs plus storage and backup dimensions.

Compare idle cost, peak capacity, replicas and I/O together. A request-based database may fit sporadic traffic, while a document engine can avoid application-side query work. Neither is automatically cheaper for every startup or large organization.

Use cases and using the services together

DynamoDB fits order access, player state, device status and other predictable high-volume requests. DocumentDB fits document-centric applications needing compatible query APIs, flexible field queries and aggregation. Both can store evolving record shapes; schema flexibility alone does not decide between them.

If separate components need both, define one authoritative owner for each dataset. A change-driven projection can copy selected data into the other system, but duplicate delivery, lag, deletion and replay become application responsibilities. Do not add two databases merely to avoid designing an index.

For uploaded documents, PDFs and images, S3 remains the usual payload store. DocumentDB or DynamoDB can hold metadata and object references. DynamoDB vs S3 explains why storing a file differs from querying its business attributes.