Fauna
vs DynamoDB
Why Customers Choose Fauna’s Truly Serverless Database over DynamoDB
Key Differentiators
Why application teams choose Fauna over DynamoDB
Data Model
Move beyond the constraints of a simple KV store with Fauna’s full support for relational and document access patterns, including native joins, ACID transactions, and unlimited indexes delivered on top of flexible JSON document storage.
Development Agility & Velocity
Simplify developer reasoning & operability with full support for relational access patterns, online operations, & native strong consistency - eliminating complex transaction management & overhead.
Performance & Scale
Reduce multiple round-trips in DynamoDB to a single operation in Fauna - decreasing total net latency. True serverless scaling with a native multi-region and multi-active architecture, without the burden of partition-aligned capacity planning and scaling.
Total Cost of Ownership
Avoid the complexity and overhead of managing multiple data copies, costly transactions & Single Table Design, and operational inefficiencies created by limited querying capability. With Fauna’s True Serverless model, you reduce the costs and complexity of over-provisioning and managing partition-based systems, ensuring your focus remains on innovation, not infrastructure.
“Fauna balances the read times of a NoSQL database with the reduced round trips and data footprints of a relational database. The result was that our core data logic lives in the database layer, reducing complex denormalization logic that used to live in Lambda Functions. As an added bonus, because FQL is basically JavaScript, our front-end devs can easily contribute.”
Tayler Kemsley
Senior Software Engineer
Administration & Deployment
MongoDB Atlas
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Administration & Deployment
Use Cases Where Fauna Out-Performs DynamoDB
Fauna excels in environments where modern application requirements demand flexibility, relational access patterns, and operational simplicity.
Applications with Changing Access Patterns
Fauna’s flexible, document-relational model allows for dynamic changes in access patterns without re-architecting your data model or rebuilding indexes and tables. DynamoDB’s rigid single-table design makes it difficult to adapt to changing requirements, often leading to costly migrations or performance trade-offs.
Applications Requiring Relational Features
Fauna's ability to handle dynamic, modifiable queries and mutations within a relational framework simplifies building sophisticated data models. Fauna supports both embedded and normalized data relationships with native joins and ACID transactions, making it a perfect fit for applications that need relational data models. In contrast, DynamoDB’s simple key-value store forces developers to manage mutations and relationships in application code, adding complexity and bloat.
Applications Requiring Multi-tenancy
With Fauna’s native multi-tenancy support, developers can manage tenant isolation with logical databases and API-level scoped permissions, significantly reducing operational complexity. DynamoDB requires separate tables per tenant, increasing infrastructure overhead and management complexity as the number of tenants scales.
Applications Requiring Multi-region Strong Consistency
Fauna offers automatic multi-active replication with strong consistency by default, eliminating the need for costly add-ons or complex configurations required by DynamoDB for global tables and consistent reads.
Fauna vs DynamoDB
Organizations familiar with DynamoDB love Fauna because it expands the serverless experience beyond deployment, while layering on a document-relational model that supports a broader set of access patterns and use cases. While DynamoDB is a rigid, single-purpose tool, Fauna supports evolving use cases with its general purpose operational database. With a multi-active serverless engine, Fauna eliminates the operational overhead associated with DynamoDB’s partition and scaling management, providing an out-of-the-box solution that supports dynamic workloads and evolving access patterns.
Data Model
DynamoDB
Key/value store. Built for single-document access patterns - doesn’t allow for dynamic relationships. Mostly inflexible Single Table Design – change in access patterns difficult to accommodate. Complex table/index design + no table joins + no embedded indexes.
Document-relational. Data stored in JSON documents with native joins, relational indexes, and ACID transactions. Embed or normalize - providing optionality for any schema model or access pattern. Online changes for table schema/indexes as application & access patterns change.
MongoDB Atlas
Administration & Deployment
Administration & Deployment
DynamoDB
Serverless deployment, but has provisioned and on-demand capacity modes. Provisioned mandates predictable & consistent workloads. Partition-aligned capacity planning mandates infrastructure awareness, forcing your design to accommodate DynamoDB limitations.
Fully serverless. Any scale or usage pattern without performance or cost challenges or needing to provision capacity. All features natively available, over multiple regions, with strong consistency.
MongoDB Atlas
Administration & Deployment
Cost model
DynamoDB
Cost model explodes as features added (indexes, multi-region replication, consistency)
Inclusive cost model. (Optimized for multi-region, at-scale, complex systems)
MongoDB Atlas
Administration & Deployment
Resiliency & Availability
DynamoDB
Native single-region.
Native multi-region.
MongoDB Atlas
Administration & Deployment
Transactions
DynamoDB
Possible, but limited with cost, complexity, and performance ramifications. Batch individual statements to pass/fail together, at additional expense & potential performance implications. Cannot read & write in the same transaction (read-modify-write).
Full transactionality native in the database with no performance impact. Multi-document, encoded business logic, in-line or via custom functions Can read-modify-write (read your own write).
MongoDB Atlas
Administration & Deployment
Schema Support
DynamoDB
Schemaless. Need to impose schema validation on the client-side - leading to bloated application code & potential performance impact.
Schema flexibility & enforcement. Evolve schema and enforce constraints as applications mature and scale. Constraints, computed fields, & type enforcement complemented by zero-downtime migrations.
MongoDB Atlas
Administration & Deployment
Consistency Model
DynamoDB
Eventually consistent by default. Upgrading to consistent reads affects costs and performance.
Strongly consistent by default. Strictly serializable; ACID transactions across regions, without performance concessions.
MongoDB Atlas
Administration & Deployment
Functions
DynamoDB
No server-side functions & limited operability. Need to use AWS Lambda or application code. Primarily supports simple access patterns.
Server-Side Functions. Create functions natively in the database to encapsulate business logic. Simplify application code, reduce interactions, & eliminate costs of external functions.
MongoDB Atlas
Administration & Deployment
Tenancy
DynamoDB
Native single-tenant. Significant effort required to design scalable, efficient, and secure multi-tenant system.
Native multi-tenant. Databases as logical containers. No limit in number. Instantly available. API delivery with tokens simplifies scoped permissions.
MongoDB Atlas
Administration & Deployment
Performance
DynamoDB
Commonly requires multiple round trip interactions to complete application operations. This drives up the total time to completion.
Between server-side functions, multi-step operations, reading-own-writes and queries with joins, Fauna allows for single trip operations - ultimately reducing net operation time as well as application code bloat.
MongoDB Atlas
Administration & Deployment
Authentication
MongoDB Atlas
Traditional account-based. Static, long-lived security contexts with limited access
Token-based. Ability to enforce identity-based authorization.
MongoDB Atlas
Administration & Deployment
Consistency Model
MongoDB Atlas
Schema validation. Limited options for data validation. Schema applied at write-time
MongoDB Atlas
Schema enforcement. Evolve schema and enforce constraints as applications mature and scale. Constraints, computed fields, & type enforcement for full control.
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Administration & Deployment
Administration & Deployment
MongoDB Atlas
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Administration & Deployment
Administration & Deployment
MongoDB Atlas
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
MongoDB Atlas
Administration & Deployment
DynamoDB
Data Model
Key/value store. Built for single-document access patterns - doesn’t allow for dynamic relationships. Mostly inflexible Single Table Design – change in access patterns difficult to accommodate. Complex table/index design + no table joins + no embedded indexes.
Document-relational. Data stored in JSON documents with native joins, relational indexes, and ACID transactions. Embed or normalize - providing optionality for any schema model or access pattern. Online changes for table schema/indexes as application & access patterns change.
Administration & Deployment
Serverless deployment, but has provisioned and on-demand capacity modes. Provisioned mandates predictable & consistent workloads. Partition-aligned capacity planning mandates infrastructure awareness, forcing your design to accommodate DynamoDB limitations.
Fully serverless. Any scale or usage pattern without performance or cost challenges or needing to provision capacity. All features natively available, over multiple regions, with strong consistency.
Cost model
Cost model explodes as features added (indexes, multi-region replication, consistency)
Inclusive cost model. (Optimized for multi-region, at-scale, complex systems)
Resiliency & Availability
Native single-region.
Native multi-region.
Transactions
Possible, but limited with cost, complexity, and performance ramifications. Batch individual statements to pass/fail together, at additional expense & potential performance implications. Cannot read & write in the same transaction (read-modify-write).
Full transactionality native in the database with no performance impact. Multi-document, encoded business logic, in-line or via custom functions Can read-modify-write (read your own write).
Schema Support
Schemaless. Need to impose schema validation on the client-side - leading to bloated application code & potential performance impact.
Schema flexibility & enforcement. Evolve schema and enforce constraints as applications mature and scale. Constraints, computed fields, & type enforcement complemented by zero-downtime migrations.
Consistency Model
Eventually consistent by default. Upgrading to consistent reads affects costs and performance.
Strongly consistent by default. Strictly serializable; ACID transactions across regions, without performance concessions.
Functions
No server-side functions & limited operability. Need to use AWS Lambda or application code. Primarily supports simple access patterns.
Server-Side Functions. Create functions natively in the database to encapsulate business logic. Simplify application code, reduce interactions, & eliminate costs of external functions.
Tenancy
Native single-tenant. Significant effort required to design scalable, efficient, and secure multi-tenant system.
Native multi-tenant. Databases as logical containers. No limit in number. Instantly available. API delivery with tokens simplifies scoped permissions.
Performance
Commonly requires multiple round trip interactions to complete application operations. This drives up the total time to completion.
Between server-side functions, multi-step operations, reading-own-writes and queries with joins, Fauna allows for single trip operations - ultimately reducing net operation time as well as application code bloat.
“We needed a database that could support a distributed, multi-tenant architecture with robust ABAC and user controls. We looked at Mongo and Dynamo, but only Fauna delivered it all without extensive engineering.”
Arjun Bhatnagar
CEO @ Cloaked
Switch to Fauna Today!
Interested in a Proof of Concept with Fauna? Receive $2500 in AWS credits for qualified AWS customers.
Get started building with Fauna
Explore resources that can help get you up and running in minutes.
Multi-tenant SaaS Sample App
Learn how to build a multi-tenant, multi-region SaaS app without ops using Fauna and AWS
BUILD THE SAMPLE APP
New to Fauna Query Language?
This guide can help you get started with FQL in under 10 minutes.
READ MORE
Workshops
Learn how to build complete applications using technology like AWS, Cloudflare, and more.
EXPLORE THE WORKSHOPS
FAQs
Have other questions? Feel free to contact us, or browse our documentation.
Ready to get started?
Launch a new app, modernize an existing app, and scale seamlessly across regions with Fauna.
Ready to get started? Launch a new app, modernize an existing app, and scale seamlessly across regions with Fauna.
Ready to get started? Launch a new app, modernize an existing app, and scale seamlessly across regions with Fauna.
Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.
