DynamoDB vs. MongoDB: A Comprehensive Contrast of NoSQL Giants

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September 11, 2025

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10 min read

Summarize with ChatGPT

DynamoDB and MongoDB are solid NoSQL databases with unique strengths and applications. While DynamoDB is a managed database service offered by AWS, designed for scalability and speed, MongoDB is a versatile, open-source, document-based database.

This guide provides an in-depth comparison, spotlighting each platform's nuances, pros, cons, and ideal scenarios.

What is DynamoDB?

Amazon DynamoDB is a fully managed AWS service designed to meet the demands of modern, high-velocity applications. It supports key-value and document data models, accommodates a variety of data structures, and offers seamless scalability and consistent performance.

DynamoDB:

Employs a distributed architecture that partitions data across multiple servers (nodes).
Uses primary keys (partition key, or partition + sort key) and a hash function to distribute data evenly across partitions.
Supports eventual consistency (with optional strong consistency) and ACID-compliant transactions for complex multi-item updates.

Key Features & Strengths

Managed Service: Fully managed—no hardware provisioning or maintenance.
Scalability: Automatic scaling of read/write capacity units with over 10 trillion queries daily capacity.
High Availability: Built-in multi-region replication and backup.
Predictable Performance: Single-digit-millisecond latencies with tunable consistency.
Hash-Range Indexes: Supports hash and hash-range indexes for efficient querying.

What is MongoDB?

MongoDB is a leading open-source NoSQL database that excels in flexibility, scalability, and versatility. MongoDB stores data in flexible BSON (binary JSON) documents, allowing efficient management of semi-structured and unstructured data.

MongoDB:

Employs a distributed architecture using shards (subsets of data) across servers.
Offers rich querying capabilities and horizontal scaling.
Provides built-in schema validation while supporting dynamic, schema-less development.
Ensures high availability through replication.

Key Features & Strengths

Document Model: JSON-like documents with support for arrays and nested data.
Dynamic Schemas: Agile development without rigid upfront schema design.
Querying & Aggregation: Powerful queries, geospatial queries, and complex aggregations.
Horizontal Scalability: Sharding distributes data across clusters.
Indexing: Multiple index types for high-performance queries.

What Are the Latest Developments in DynamoDB and MongoDB Performance and Features?

Recent innovations in both DynamoDB and MongoDB have significantly enhanced their capabilities for modern data workloads, with major improvements in cost optimization, performance, and AI integration.

DynamoDB's 2024-2025 Enhancements

Cost reduction: The 50% price reduction for on-demand throughput and 67% cost cuts for global tables, effective November 2024, eliminate historical barriers for serverless architectures. A 10 TB dataset with 100K monthly writes now costs approximately $1,200 monthly under on-demand mode, down from $2,400.
Multi-region consistency: The introduction of multi-region strong consistency resolves the "last write wins" conflict risk in eventual consistency models, guaranteeing linearizability across geographies.
Adaptive scaling: Instant adaptive capacity automatically balances partitions during traffic spikes, allowing hot partitions to scale to 3,000 RCUs and 1,000 WCUs without throttling.
Analytics integration: The zero-ETL integration with SageMaker Lakehouse enables real-time analytics via Apache Iceberg tables, with Change Data Capture synchronizing data within minutes rather than hours. This supports use cases like fraud detection and live inventory forecasting without operational overhead.

MongoDB's Performance Breakthrough

Performance improvement: MongoDB 8.0 represents a significant performance leap with 36% faster read performance and 32% higher mixed workload throughput compared to version 7.0. This improvement stems from write replication overhaul, reduced majority-write latency via parallel journaling, and dedicated performance engineers targeting query execution hotspots.
Vector search capabilities: The Atlas Vector Search enhancements support binary and scalar quantization, reducing vector storage by 3× versus float32 while enabling hybrid search that combines vector similarity with metadata filters. Dedicated search nodes isolate vector and search workloads from transactional databases, preventing resource contention and accelerating queries by 8× at scale.
Security and platform expansion: MongoDB has also implemented mandatory MFA security enhancements for all Atlas accounts and expanded multi-cloud support with Azure Private Link and GCP integration for Atlas Data Federation.

How Do DynamoDB and MongoDB Compare Across Key Technical Dimensions?

DynamoDB (AWS) boasts seamless scalability and high availability for low-latency applications, while MongoDB provides flexible document storage, robust querying, and dynamic schemas. The decision hinges on specific requirements:

Data Model

DynamoDB organizes data in tables; items (rows) are identified by a primary key. Attributes can vary, but schema changes require table recreation.
MongoDB stores data in BSON documents within collections, allowing nested structures and dynamic schemas—ideal for evolving data requirements.

Deployment

DynamoDB: Fully managed—AWS handles infrastructure, scaling, and maintenance.
MongoDB: Self-host on your infrastructure or use MongoDB Atlas (managed cloud).

Scalability

DynamoDB: Automatically adjusts capacity; partitions data horizontally.
MongoDB: Sharding distributes data across nodes for high scalability.

Pricing

DynamoDB: Pay for reads, writes, and storage (on-demand or provisioned capacity).
MongoDB: Community Edition is free; MongoDB Atlas offers dedicated and serverless plans.

How Do the Security and Enterprise Features of DynamoDB and MongoDB Compare?

Security and privacy capabilities represent a critical differentiator for enterprise-level solutions, with DynamoDB and MongoDB taking distinct architectural approaches to data protection and compliance.

Encryption and Data Protection

DynamoDB enforces server-side encryption at rest for all tables using AWS-owned keys by default, with options for AWS-managed or customer-managed keys via KMS. This encryption extends to global and secondary indexes, streams, and backups without exception. For data in transit, DynamoDB mandates TLS 1.2+ through HTTPS endpoints, while client-side encryption remains optional via the DynamoDB Encryption Client.

MongoDB operates through a tiered encryption model with optional encryption at rest (enterprise feature), TLS for transit, and distinctive client-side field-level encryption allowing pre-insertion data encryption. MongoDB 7.0's breakthrough queryable encryption permits equality searches on fully randomized ciphertext without decryption, a capability absent in DynamoDB's paradigm.

Access Control and Governance

DynamoDB's access governance centers on AWS Identity and Access Management (IAM), where policies define granular permissions through conditions like dynamodb:LeadingKeys to restrict item access by partition key. This facilitates attribute-level security but requires explicit policy authoring.

MongoDB employs role-based access control with scoped privileges and authentication mechanisms including SCRAM, x.509 certificates, and LDAP integration. MongoDB Atlas augments this with network access controls and IP whitelisting, providing layered defense against unauthorized entry.

Compliance and Certification

MongoDB Atlas maintains comprehensive compliance coverage, including ISO 27001, ISO 27018, PCI DSS, HIPAA, GDPR, FedRAMP Moderate, HDS (France), and TX-RAMP (Texas). Independent audits validate Atlas controls against HIPAA Security Rules and HITECH breach requirements.

DynamoDB supports HIPAA compliance when customers execute AWS's BAA, alongside SOC 2 alignment through features like encrypted backups and CloudTrail integration for audit trails. However, MongoDB's broader certification spectrum provides greater regulatory coverage for global enterprises.

What Are the Key Performance Considerations for DynamoDB and MongoDB?

DynamoDB

Index design and partition keys are critical—uneven data distribution can create "hot" partitions and throttling. Strong consistency adds latency. Recent improvements include burst capacity optimization, which retains 300 seconds of unused capacity for sudden traffic surges, allowing a table provisioned at 500 WCUs to burst to 1,800 WCUs temporarily.

DynamoDB can process over 10 trillion requests per day with peak throughput rates exceeding 20 million requests per second. The platform delivers consistent single-digit millisecond latency for items under 4 kilobytes, with the DynamoDB Accelerator (DAX) providing microsecond-level caching capabilities for frequently accessed data.

MongoDB

Schema design, document structure, and index usage heavily influence performance. Poorly chosen sharding keys or deeply nested documents can slow queries. MongoDB 8.0's performance improvements include reduced majority-write latency via parallel journaling and regression testing that automatically detects micro-optimization losses.

MongoDB's distributed database architecture enables over 100,000 read and 50,000 write operations per second, with the performance improvements in version 8.0 delivering 36% faster read throughput and 32% overall performance improvement for typical web applications according to Yahoo! Cloud Serving Benchmark testing.

Caching and Query Patterns

DynamoDB: DAX provides in-memory caching; efficient use of partition/sort keys maximizes performance.
MongoDB: The WiredTiger cache and in-memory storage engine accelerate read-heavy workloads.

How Do the Development Ecosystems of DynamoDB and MongoDB Differ?

DynamoDB

Query Language: Simple, key-based queries with PartiQL support for SQL-like syntax; complex filtering done in application code.
SDKs & Tools: AWS SDKs, CloudFormation, Amplify.
Integration: Deeply integrated with AWS services (Lambda, S3, Step Functions).

MongoDB

Query Language: MongoDB Query Language (MQL) supports filters, text search, and aggregations.
SDKs & Tools: Official drivers for major languages; MongoDB Atlas UI; rich plugin ecosystem.
Integration: Deploy on any cloud or Kubernetes; strong open-source community.

How Do You Choose Between MongoDB and DynamoDB for Your Project?

Data Model: Choose MongoDB for flexible, nested structures; DynamoDB for simpler key-value workloads.
Scalability: DynamoDB offers seamless auto-scaling with recent 50% cost reductions; MongoDB scales via sharding (Atlas simplifies this).
AWS Integration: Tight AWS integration favors DynamoDB; cross-cloud deployment favors MongoDB.
Latency & Throughput: DynamoDB provides single-digit millisecond latency at massive scale with improved adaptive capacity.
Cost & Licensing: MongoDB Community is free; DynamoDB costs are usage-based with significant recent price reductions.
AI/ML Integration: DynamoDB offers zero-ETL integration with SageMaker Lakehouse; MongoDB provides native vector search capabilities.

How Does Airbyte Enhance Data Integration with DynamoDB and MongoDB?

A database is only one piece of a modern data stack—seamless integration is equally vital. Airbyte transforms data integration for DynamoDB and MongoDB users through its open-source platform with 600+ connectors, addressing critical data movement challenges with specialized NoSQL capabilities.

Advanced DynamoDB Integration

Airbyte's DynamoDB integration eliminates manual pipeline development through simplified configuration workflows while overcoming AWS permission complexities. The platform automatically handles DynamoDB's attribute type constraints, detecting incompatible data types and applying configurable fallback strategies like coercion, nullification, or error logging. Recent improvements include enhanced error handling for partial failure scenarios and real-time throughput monitoring dashboards.

For DynamoDB users, Airbyte enables cross-account replication through IAM role chaining and supports incremental sync via timestamp-based change detection. The platform processes data through idempotent upsert operations, making it ideal for maintaining data consistency across development and production environments.

Revolutionary MongoDB Capabilities

Airbyte's MongoDB integration features a revolutionary schemaless mode introduced in December 2023, enabling dynamic document structure handling without schema enforcement. This addresses historical challenges with polymorphic collections where traditional schema enforcement previously discarded significant portions of documents.

The platform's Change Data Capture implementation leverages replica set oplogs with three-phase synchronization: initial snapshot with parallel collection scanning, continuous oplog tailing, and checkpointing with resume tokens. Performance benchmarks indicate MongoDB CDC achieves approximately 9 megabytes per second throughput, with multi-database support in v2.0 allowing enterprises to segment sync jobs by business unit databases within a single cluster.

Strategic Integration Benefits

Centralized Data: Consolidate multiple sources for unified analytics across NoSQL and relational databases.
Ecosystem Flexibility: Ingest data from databases, SaaS applications, files, and more through 600+ connectors.
Cost Optimization: Open-source foundation eliminates licensing costs while pre-built connectors reduce development overhead.
Real-Time Insights: Combine DynamoDB's low-latency data with MongoDB's analytical capabilities through automated pipelines.
Enterprise Security: SOC 2, HIPAA compliance with end-to-end encryption and role-based access control.

Airbyte's containerized microservices architecture provides database-specific optimization while maintaining horizontal scalability, enabling dedicated containers for DynamoDB and MongoDB connectors with custom dependencies. This approach delivers 10× implementation efficiency gains compared to manual coding while supporting hybrid deployment across cloud and on-premises environments.

Make the Right NoSQL Choice for Your Use Case

Both Amazon DynamoDB and MongoDB are powerful—but their strengths lie in different areas. If you need seamless scalability, low-latency performance, and tight AWS integration, DynamoDB is the natural fit, especially with recent cost reductions and zero-ETL analytics capabilities. For teams prioritizing schema flexibility, advanced querying, cross-platform deployment, and AI/ML integration through vector search, MongoDB delivers unmatched versatility.

Frequently Asked Questions

What is the main difference between DynamoDB and MongoDB?

DynamoDB is a fully managed NoSQL service exclusive to AWS that emphasizes automatic scaling and low-latency performance, while MongoDB is an open-source document database that offers flexible deployment across any infrastructure with rich querying capabilities and dynamic schemas.

Which database is more cost-effective for large-scale applications?

Cost-effectiveness depends on usage patterns and deployment preferences. DynamoDB's recent price reductions make it competitive for high-throughput AWS workloads, while MongoDB's free Community Edition and flexible Atlas pricing can be more economical for organizations with varying workloads or multi-cloud requirements.

Can DynamoDB and MongoDB handle real-time analytics workloads?

Both databases support real-time analytics with different approaches. DynamoDB offers zero-ETL integration with analytics platforms and microsecond caching through DAX, while MongoDB provides native aggregation pipelines and vector search capabilities for complex analytical operations.

How do the security features of DynamoDB vs MongoDB compare?

DynamoDB provides comprehensive AWS-integrated security with mandatory encryption and IAM-based access control, while MongoDB offers broader compliance certifications and unique features like queryable encryption. MongoDB Atlas maintains more extensive regulatory coverage for global enterprises.

Which database is better for AI and machine learning applications?

Both platforms support AI workloads effectively but with different strengths. MongoDB's native vector search and flexible document model excel for unstructured AI data, while DynamoDB's AWS integration provides seamless ML pipeline integration with SageMaker and other AWS AI services.

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About the Author

Jim Kutz brings over 20 years of experience in data analytics to his work, helping organizations transform raw data into actionable business insights. His expertise spans predictive modeling, data engineering and data visualization, with a focus on making analytics accessible and impactful for stakeholders at all levels.