DynamoDB vs. MongoDB: A Comprehensive Contrast of NoSQL Giants

For MongoDB, the schema design, structure of documents, and proper index use are vital—improperly indexed queries can slow down execution as the database scans large portions of data. 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.

In a world where data is generated at an unprecedented rate, the importance of NoSQL databases has grown exponentially. The global NoSQL market achieved a valuation of $11.9 billion in 2024, representing explosive growth projected to reach $81.9 billion by 2033 with a remarkable compound annual growth rate of 23.9%. Unlike traditional relational databases, NoSQL databases offer diverse data models and can handle vast amounts of data in varying data types.

Amazon DynamoDB and MongoDB are two renowned non-relational databases. DynamoDB, a fully managed service by Amazon Web Services (AWS), emphasizes high availability and seamless scaling. MongoDB, an open-source platform, boasts a powerful document data model and extensive querying capabilities.

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 serves over 1 million customers globally, with notable performance benchmarks including customers like Amazon Ads Measurement issuing 95 million requests per second to over 4 petabytes of data.

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. With over 54,500 customers as of January 31, 2025 and commanding 44.90% market share within the NoSQL database segment, 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

Amazon has introduced substantial cost reductions and performance improvements that democratize high-scale NoSQL workloads. 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.

The introduction of multi-region strong consistency resolves the "last write wins" conflict risk in eventual consistency models, guaranteeing linearizability across geographies. Meanwhile, instant adaptive capacity automatically balances partitions during traffic spikes, allowing hot partitions to scale to 3,000 RCUs and 1,000 WCUs without throttling.

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

MongoDB reported fourth quarter fiscal 2025 total revenue of $548.4 million, representing a 20% year-over-year increase, with full-year fiscal 2025 total revenue reaching $2.01 billion, up 19% year-over-year. Atlas revenue grew 24% year-over-year and now represents 71% of total revenue.

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.

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.

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 & 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.

What Are the Real-World Use Cases and Implementation Examples for Each Database?

DynamoDB

• Zoom: Scaled globally to meet pandemic-era traffic demands with DynamoDB's automatic scaling capabilities.
• Disney+: Ingests billions of daily customer interactions using DynamoDB's high-throughput capabilities.
• Dropbox: Built a scalable metadata store with DynamoDB, cutting storage costs by 5.5× through efficient data organization.
• TMAP achieved a 43% cost reduction by migrating their driver safety score workload from Oracle RAC to DynamoDB.
• Amazon Ads processes more than 100 billion events while storing over 4 petabytes of data, with peak traffic reaching 90 million reads and 5 million writes per second.

MongoDB

• Cathay Pacific digitized in-flight documents using MongoDB's flexible document model, saving $12.7M over five years.
• GE Healthcare managed IoT device lifecycle with MongoDB, achieving an 83% reduction in retrieval time.
• Verizon accelerated 5G customer experiences using MongoDB's real-time capabilities and flexible schema.

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 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.

Whichever you choose, ensure you have a robust data-integration strategy in place so your insights keep pace with your data. Airbyte's specialized NoSQL capabilities and recent enhancements make it the ideal platform for maximizing the value of your DynamoDB or MongoDB deployment.

About the Author

Engineering Team of Airbyte
Move Data Anywhere, Anytime.