DynamoDB vs. MongoDB: The Ultimate 2026 Guide for Your Big Data Needs

Making sense of big data is a major hurdle for businesses today. You have vast amounts of information from web scraping, user interactions, and market trends. But how do you store and manage it effectively? Choosing the right database is one of the most critical decisions you’ll make. It can be the difference between actionable insights and a chaotic mess.

Many businesses struggle to find a database that can handle their massive data volumes. This is where NoSQL databases like Amazon DynamoDB and MongoDB come in. Both are powerful, fully managed, serverless options. This means you don’t have to worry about managing hardware, updates, or maintenance. However, they have key differences that make them suitable for different needs. Understanding these distinctions is crucial to picking the right tool for your data-intensive projects.

This guide will break down the complexities of DynamoDB and MongoDB in simple, easy-to-understand terms. We’ll explore their core differences, ideal use cases, and how to choose the best fit for your company’s data strategy in 2026.

Deployment Flexibility: Where Can You Run Your Database?

One of the most significant differences between DynamoDB and MongoDB is where you can deploy them. This flexibility can be a deciding factor, especially for companies with specific infrastructure requirements or multi-cloud strategies.

Amazon DynamoDB: The AWS Native

DynamoDB is a proprietary service offered exclusively by Amazon Web Services (AWS). This means you can only use it within the AWS cloud environment. While this ensures seamless integration with other AWS services, it also locks you into a single cloud provider. If your business is already heavily invested in AWS, DynamoDB can be a natural and powerful choice.

Key takeaways for DynamoDB deployment:

• Exclusively available on Amazon Web Services.
• No on-premise deployment options for production environments.
• Ideal for businesses committed to the AWS ecosystem.

MongoDB: The Multi-Cloud Champion

MongoDB, on the other hand, offers much greater deployment flexibility. It’s an open-source database, which means you can run it anywhere. You can install it on your own servers (on-premise), on a developer’s laptop, or in any major public cloud.

For a fully managed experience similar to DynamoDB, MongoDB offers MongoDB Atlas. Atlas is a database-as-a-service (DBaaS) that can be deployed on AWS, Microsoft Azure, and Google Cloud Platform (GCP). This multi-cloud capability is a huge advantage for businesses that want to avoid vendor lock-in or have a hybrid cloud strategy.

Key takeaways for MongoDB deployment:

• Deployable anywhere: on-premise, laptops, and all major cloud providers.
• MongoDB Atlas offers a fully managed, multi-cloud experience.
• Provides the freedom to choose your cloud provider or run a hybrid setup.

For more information on the growing trend of Database-as-a-Service, check out this insightful article from Forbes.

Cost Comparison: What’s the Price of Power?

Determining the exact cost of either DynamoDB or MongoDB isn’t straightforward. Both platforms have complex pricing models that depend on your specific usage. They also both offer free tiers, which are great for testing and development.

DynamoDB Pricing Model

DynamoDB’s pricing is based on a pay-as-you-go model with two capacity modes: on-demand and provisioned.

• On-Demand Capacity: You pay per read and write request. This is ideal for unpredictable workloads with traffic spikes, as it automatically scales to meet demand.
• Provisioned Capacity: You specify the number of reads and writes per second your application needs. This can be more cost-effective for predictable, high-traffic applications.

Additional costs for DynamoDB can include data storage, backups, global tables for multi-region replication, and data transfer. AWS provides a pricing calculator to help you estimate your monthly bill.

MongoDB Atlas Pricing Model

MongoDB Atlas also has a usage-based pricing structure. The cost depends on several factors, including:

• Your chosen cloud provider (AWS, Azure, or GCP).
• The region where your database is hosted.
• The amount of storage and RAM you need.
• The complexity and volume of your data operations.

For many large-scale applications, MongoDB Atlas can be more cost-effective in the long run, especially when you factor in the flexibility and advanced features it offers. Like AWS, MongoDB provides a detailed pricing calculator to help you estimate costs.

While initial startup costs for DynamoDB might seem lower, MongoDB’s extensive features like varied indexing strategies and data validation can provide more value for complex applications, even with potentially higher initial costs.

Security: Protecting Your Valuable Data

Data security is non-negotiable, especially when dealing with sensitive information gathered from web scraping and other data extraction methods. Both DynamoDB and MongoDB offer robust security features, but their approaches and integrations differ.

DynamoDB: Fortified by the AWS Ecosystem

DynamoDB benefits from the comprehensive security infrastructure of AWS. It provides several layers of protection for your data.

• Encryption by Default: All data stored in DynamoDB is encrypted at rest by default. It also encrypts data in transit.
• IAM Integration: DynamoDB integrates seamlessly with AWS Identity and Access Management (IAM). This allows you to create granular access control policies, ensuring users and applications only have the permissions they need.
• VPC Endpoints: You can connect to DynamoDB from your Virtual Private Cloud (VPC) using VPC endpoints. This ensures that your data traffic does not traverse the public internet, adding an extra layer of security.
• Customer-Managed Keys: For enhanced control, you can use your own encryption keys through the AWS Key Management Service (KMS).

MongoDB: Flexible and Robust Security Measures

MongoDB also provides a comprehensive set of security features to protect your data, regardless of where it’s deployed.

• Role-Based Access Control (RBAC): MongoDB uses RBAC to control access to your data. You can define roles with specific permissions and assign them to users.
• Encryption: MongoDB Atlas provides encryption for data at rest and in transit. You can also leverage the key management services of your chosen cloud provider (AWS KMS, Azure Key Vault, or Google Cloud KMS) to manage your own encryption keys.
• IP Whitelisting: You can restrict access to your database to a list of trusted IP addresses, preventing unauthorized connections.
• Auditing: MongoDB Enterprise and Atlas offer advanced auditing capabilities, allowing you to track and log all actions performed on your database.

It’s important to note that historically, some security issues with MongoDB arose from users failing to enable authentication by default. However, modern versions and MongoDB Atlas have made it much easier to implement strong security from the start.

For a deeper dive into database security best practices, this guide from the Cybersecurity and Infrastructure Security Agency (CISA) is an excellent resource.

Data Models and Flexibility: Structuring Your Information

The way a database stores and structures data is fundamental to its performance and capabilities. Both DynamoDB and MongoDB are NoSQL databases, but they have different data models that cater to different use cases.

DynamoDB: A Powerful Key-Value and Document Store

DynamoDB is primarily a key-value store, but it also supports document-like data structures using JSON. This makes it incredibly fast for simple lookups based on a primary key.

However, DynamoDB has some limitations:

• Item Size Limit: A single item (similar to a row in a traditional database) cannot exceed 400KB.
• Limited Data Types: Compared to MongoDB, DynamoDB supports a smaller range of data types. For instance, it lacks a dedicated date type, which means developers have to handle date conversions on the client-side.

These limitations make DynamoDB highly efficient for its intended use cases, such as user profiles, session data, and real-time bidding, where fast key-based access is crucial.

MongoDB: A Flexible Document-Oriented Database

MongoDB is a document-oriented database that stores data in flexible, JSON-like documents called BSON (Binary JSON). This model is highly intuitive for developers as it maps directly to objects in modern programming languages.

MongoDB’s data model offers several advantages:

• Larger Document Size: MongoDB supports documents up to 16MB in size.
• Rich Data Types: It supports a wide variety of data types, including dates, different numeric types, and more.
• Schema Flexibility: While you can enforce a schema if needed, MongoDB’s flexible schema allows you to evolve your data structures over time without costly database migrations.
• Advanced Queries: MongoDB’s rich query language allows for complex queries, including geospatial queries, graph traversals, and full-text search.

This flexibility makes MongoDB suitable for a broader range of applications, from content management systems to real-time analytics and complex data processing pipelines.

Backup and Recovery: Safeguarding Against Data Loss

Having a solid backup and recovery strategy is essential for business continuity. Both DynamoDB and MongoDB provide reliable options to protect your data.

DynamoDB Backup Capabilities

DynamoDB offers two main types of backups:

• On-Demand Backups: You can create full backups of your tables at any time. These are useful for long-term archival and compliance.
• Point-in-Time Recovery (PITR): PITR provides continuous backups of your table data, allowing you to restore your table to any point in time during the last 35 days. This is crucial for protecting against accidental writes or deletes.

While these features are robust, restoring a DynamoDB table can sometimes require manual recreation of certain configurations that are not part of the backup.

MongoDB Atlas Backup Features

MongoDB Atlas also provides comprehensive backup solutions:

• Continuous Backups: Atlas continuously backs up your data, enabling point-in-time recovery.
• Cloud Provider Snapshots: You can also create filesystem snapshots for quicker backups and restores.
• Queryable Backups: A standout feature of MongoDB Atlas is the ability to query your backup snapshots directly. This is incredibly useful for analyzing historical data without impacting your live production database.

The queryable backup feature gives MongoDB an edge in terms of data analysis and operational flexibility.

Indexing: The Key to Fast Queries

Indexing is crucial for database performance. It allows the database to find data quickly without scanning every single record. DynamoDB and MongoDB have different approaches to indexing, which can impact both performance and cost.

Indexing in DynamoDB

In DynamoDB, you have primary keys and secondary indexes.

• Primary Keys: Every item in a DynamoDB table is uniquely identified by a primary key.
• Secondary Indexes: DynamoDB offers two types of secondary indexes: Local Secondary Indexes (LSIs) and Global Secondary Indexes (GSIs). These allow you to query your data using attributes other than the primary key.

However, indexing in DynamoDB comes with some complexities:

• Provisioned Separately: Indexes in DynamoDB are provisioned, sized, and charged for separately from your data tables. This can add to the management overhead and cost.
• Potential for Stale Data: Global Secondary Indexes are eventually consistent, which means there can be a slight delay before new data is reflected in the index. This can result in applications reading stale data in some scenarios.

Indexing in MongoDB

MongoDB offers a more flexible and developer-friendly approach to indexing.

• Flexible Indexing: You can create indexes on any field in your documents, including fields within arrays and nested documents. MongoDB supports various index types, such as compound, geospatial, and text indexes.
• No Extra Cost for Indexes: Creating indexes in MongoDB does not incur a separate cost. The indexes are stored with the data.
• Strong Consistency: Secondary indexes in MongoDB are consistent with the underlying data, ensuring that your queries always return the most up-to-date information.

MongoDB’s indexing capabilities are generally considered more powerful and easier to manage, providing stronger data consistency for a wider range of query patterns.

Learn more about effective database indexing strategies from this detailed article on MongoDB’s official blog.

Making the Right Choice for Your Business

Choosing between DynamoDB and MongoDB depends heavily on your specific needs, existing infrastructure, and long-term goals.

Choose DynamoDB if:

• Your business is deeply integrated into the AWS ecosystem.
• Your application requires extremely low-latency reads and writes for simple key-value lookups.
• You have a predictable workload that can benefit from provisioned capacity.
• You prefer a fully managed service with seamless auto-scaling.

Choose MongoDB if:

• You need the flexibility to deploy on-premise, in a hybrid environment, or across multiple cloud providers.
• Your application deals with complex, semi-structured, or evolving data.
• You require rich and flexible querying capabilities, including geospatial and text search.
• You want to avoid vendor lock-in and have more control over your database environment.
• Your use case involves advanced analytics or AI, where features like MongoDB Atlas Vector Search can be beneficial.

Frequently Asked Questions (FAQs)

1. What is the main difference between DynamoDB and MongoDB?

The primary difference lies in their deployment flexibility and data model. DynamoDB is an AWS-exclusive, primarily key-value database, while MongoDB is a multi-cloud, document-oriented database that can be deployed anywhere.

2. Is DynamoDB cheaper than MongoDB?

DynamoDB can have lower initial costs, especially for simple workloads. However, for complex applications at scale, MongoDB Atlas can be more cost-effective due to its rich feature set and flexible pricing across different cloud providers.

3. Can I migrate from DynamoDB to MongoDB?

Yes, migration is possible. MongoDB provides tools and documentation to help you migrate your data from DynamoDB to MongoDB Atlas. However, it requires careful planning due to the differences in their data models and query languages.

4. Which database is better for web scraping and data extraction?

For large-scale web scraping and data extraction, MongoDB’s flexible schema and rich querying capabilities are often a better fit. The ability to store complex, nested JSON documents without a predefined schema makes it easier to handle the varied and often unpredictable structure of web data. Its powerful aggregation framework also simplifies the process of transforming and analyzing the extracted data.

5. What are the performance differences between DynamoDB and MongoDB?

DynamoDB offers consistent, single-digit millisecond latency for key-value operations at any scale. MongoDB’s performance is also excellent and can be optimized for various workloads through proper indexing and sharding. For complex queries and data analysis, MongoDB often outperforms DynamoDB due to its more powerful query engine.

6. How do DynamoDB and MongoDB handle scalability?

DynamoDB provides seamless, automatic scaling of throughput and storage. MongoDB scales horizontally through a process called sharding, which distributes data across multiple servers. MongoDB Atlas simplifies the management of sharded clusters.

7. Are there any free tiers available for DynamoDB and MongoDB?

Yes, both DynamoDB and MongoDB Atlas offer generous free tiers. DynamoDB’s free tier includes 25 GB of storage and enough read/write capacity for many small applications. MongoDB Atlas also provides a free tier with shared resources that is great for learning, prototyping, and small-scale projects.

Unlock the Power of Your Data with Hir Infotech

Choosing the right database is a crucial step, but it’s only the beginning. To truly leverage the power of your data, you need a partner with deep expertise in data solutions. At Hir Infotech, we specialize in helping mid to large companies manage their data effectively, from web scraping and data extraction to building robust and scalable data pipelines.

Whether you choose DynamoDB, MongoDB, or another data solution, our team of experts can help you design, implement, and manage a data infrastructure that drives business growth. We understand the challenges of dealing with big data and can provide tailored solutions to meet your unique needs.

Ready to transform your data into a competitive advantage? Contact Hir Infotech today for a free consultation and let’s build a data strategy that works for you.

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