DynamoDB vs MongoDB in 2025 - Key Differences, Use Cases

Choosing the right database for a given application is always a problem for data engineers. Two popular NoSQL database options that frequently come up are AWS DynamoDB and MongoDB. Both offer scalability and flexibility but differ significantly in their architecture, features, and operational characteristics. This blog provides a comprehensive comparison to help you make an informed decision.

What is Amazon DynamoDB?

Amazon DynamoDB is Amazon’s fully managed, serverless NoSQL service. It supports both key–value and document data, scales automatically, and delivers single-digit millisecond response times at any size. Features like global tables, on-demand scaling, and tight integration with AWS services make it a go-to for high-scale workloads.

Key Strengths:

• Fully managed service: No server to manage. DynamoDB automatically partitions data and scales throughput, eliminating operational overhead.
• Low-latency at scale: It is designed for consistent millisecond latency for reads and writes, even under heavy load.
• Deep AWS integration: It natively integrated with Lambda, API Gateway, Kinesis, CloudWatch, and IAM, simplifying building serverless architectures.
• Global replication: Its global table offers multi-region, active-active replication that automatically keeps multiple copies of a DynamoDB table in sync across different AWS Regions.

Pricing:
DynamoDB has two pricing modes: On‑Demand (pay per request) and Provisioned (buy read/write capacity units). On-demand is simple for unpredictable or spiky traffic, while provisioned is more cost-efficient for steady high throughput.

For storage, the first 25 GB per month is free, and then $0.25 per GB per month is charged.

Additional costs apply for backup, global tables, change data capture, etc.

What is MongoDB?

MongoDB is a document database that stores data as BSON (binary JSON) documents. It’s flexible, schema-optional, and supports rich queries, secondary indexes, and powerful aggregation pipelines. You can self-host it or use MongoDB Atlas, the managed service that runs on AWS, Azure, or GCP.

Key Strengths:

• Flexible Data Model: Documents allow for embedding and nested structures, accommodating complex and evolving data.
• Various ad-hoc queries: It supports a wide range of queries, including field-based queries, regular expressions, and geospatial queries.
• Rich indexing & analytics: It supports compound, text, geospatial, wildcard and partial indexes. Aggregation pipeline enables complex transformations and analytics inside the DB.
• ACID Transaction: It supports multi-document ACID transactions (since v4.0), ensuring data consistency even if the driver has unexpected errors.

Pricing:
MongoDB Enterprise charges for the infrastructure costs (servers, storage, networking) on your chosen platform.

MongoDB Atlas (managed service) has a free tier, shared tiers, and dedicated clusters billed hourly (pay‑as‑you‑go). Pricing depends on cloud provider, instance family, vCPU/RAM, storage, backup retention, and data transfer.

DynamoDB vs MongoDB At a Glance

Feature	DynamoDB	MongoDB
Type	Fully managed NoSQL database (AWS)	Document NoSQL database
Deployment	AWS only	On-premise / MongoDB Atlas (managed on multiple cloud providers)
Data Model	Key-value and document	Document
Max Document Size	400 KB per item	16 MB per document
Query Language	Primary key lookups, range queries, secondary indexes; limited aggregation	Support ad-hoc queries, joins, and advanced aggregation pipeline
Scalability	Automatic partitioning and scaling	Manual or automated scaling via sharding and replica sets
Consistency	Eventually consistent by default, optional strong consistency; multi-item ACID transactions	Tunable consistency levels; multi-document ACID transactions
Performance	Single-digit millisecond response time	Varies based on configuration
Security	Integrated with AWS IAM	Role-Based Access Control
Multi-Region Support	Built-in via global tables (active-active)	Atlas Global Clusters or custom sharding
Integration	Deep AWS integration	Broad ecosystem, multi-cloud support
Vendor Lock-in	High (AWS only)	Lower (run on multiple clouds or on-prem)

Core Features Comparison

Data Model & Query

DynamoDB:

• Employ a key-value store with support for document structures.
• Optimized for fast lookups based on the primary key.
• Global and local secondary indexes for additional access paths.
• Limited aggregation support.

MongoDB:

• A document-oriented database where data is stored in BSON documents within collections.
• Expressive query language that supports many operators.
• Powerful aggregation pipelines allow for complex in-database transformations.

Scalability and Performance

DynamoDB:

• Automatic horizontal scaling of both storage and throughput.
• Single-digit millisecond latency at any scale.
• Handle huge throughput with AWS-managed partitioning.

MongoDB:

• Scale via sharding and replica sets.
• Efforts required for setting up and managing sharding.
• Performance depends on query patterns, indexing, and the chosen consistency level.

Consistency

DynamoDB:

• Eventually consistent reads by default or strongly consistent reads at a cost of higher latency.
• ACID transactions across one or more tables within a single AWS region.

MongoDB:

• Offer various read concerns to control the consistency and isolation of read operations.
• ACID transactions for multi-document operations.

Availability

DynamoDB:

• Automatic multi-AZ replication within a region.
• Automatic regional failover.
• Global tables for automated multi-region, active-active replication.

MongoDB:

• Replica sets provide high availability, requiring one primary node and multiple secondary nodes.
• Manual or semi-automatic failover depending on configuration. Atlas automates in managed clusters.
• Atlas Global Clusters enable zone sharding to partition data and pin it to specific regions.

How to Choose between them?

There’s no universal winner. Both are mature, battle-tested products. You may consider the following cases:

Choose DynamoDB if:

• You are all-in on AWS. DynamoDB integrates seamlessly with other AWS services, making it a natural choice for serverless services built within the AWS ecosystem.
• Your query patterns are simple and predictable. The ideal use case for DynamoDB is fetching data using a known primary key. It's not designed for complex, ad-hoc queries.
• You prefer minimal operational burden. DynamoDB is fully managed by AWS, minimizing the operational overhead.

Real-world case: How Disney+ scales globally on Amazon DynamoDB

Choose MongoDB if:

• You require complex querying and data aggregation. MongoDB's rich query language and aggregation pipelines are good for perfoming data searches and analysis.
• You need a flexible schema. MongoDB's document model easily accommodates data structure changes.
• You want deployment flexibility. MongoDB can be run on-premises, on any cloud provider (AWS, GCP, Azure), or as a fully managed service via MongoDB Atlas.

Real-world case: How Novo Nordisk accelerates time to value with GenAI and MongoDB

Stream Data to DynamoDB and MongoDB Easily

In real-world architectures, DynamoDB and MongoDB don’t exist in isolation. They’re part of a larger data ecosystem that needs to move information in and out in real time.

This is where BladePipe fits perfectly. As a real-time, end-to-end data replication tool, it supports 40+ out-of-the-box connectors. It captures data changes (CDC) from multiple sources and continuously sync them into DynamoDB or MongoDB with sub-second latency. This ensures both databases always have fresh, consistent data without manual ETL jobs or complex pipelines. Both on-prem and cloud deployment is supported.

With BladePipe, teams only need to focus on building applications, not moving data.