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Fauna
vs MongoDB

Why customers choose Fauna’s Document Relational-Database over MongoDB

DOWNLOAD THE COMPARISONCONTACT US

Key Differentiators

Why application teams choose Fauna over MongoDB

Infrastructure & Scaling Simplicity

Eliminate the undifferentiated operational overhead associated with building and scaling your database.

Data Model and Querying Power

Add the power of relations to a distributed document database.

Modern & Scalable Connectivity

Simplify application architectures with Fauna’s HTTPS API delivery model.

Advanced Security

Elevate data protection with user-grained, dynamic, and real-time access control.

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 Mongo

Fauna excels in environments where modern application requirements demand flexibility, scalability, and cost optimization.

Applications Requiring Document Storage with Relational Access Patterns

Fauna stores data as JSON documents, but maintains native relational features like joins and schema enforcement, making it perfect for applications storing different shapes of data with sophisticated access patterns. MongoDB struggles with these use cases due to its lack of native joins, schema enforcement, and reliance on pre-compiled views or embedded patterns.

Applications with Changing Access Patterns

Fauna’s document-relational model is ideal for applications where access patterns evolve over time. With native support for relations, Fauna enables developers to adjust data models and queries without having to re-architect or denormalize the data, providing unparalleled flexibility for complex applications. MongoDB, on the other hand, relies heavily on fixed access patterns and embedded schemas, making changes more costly and difficult to manage.

Applications with Serverless or API-driven Architectures

Fauna’s fully serverless model is delivered as an HTTPS API and scales effortlessly for serverless and edge applications, with no need for connection pooling or complex middleware. In contrast, MongoDB’s node-based architecture introduces scaling limitations and operational overhead for serverless environments. MongoDB announced the deprecation of its Data API, limiting native connectivity options for these architectures.

Applications with Multi-region or Edge Workloads

With Fauna’s native multi-active architecture, it's designed for apps that need low-latency, strongly consistent reads and writes across multi-region locations. MongoDB, especially Atlas, requires more complex configurations for multi-region support, manual sharding, and sacrifices consistency for performance.

Applications with Multi-tenant Workloads

Fauna’s multi-tenant architecture simplifies building secure, tenant-isolated apps with dynamic ABAC (attribute-based access control). MongoDB lacks this out-of-the-box, often requiring custom engineering and third-party tools for multi-tenancy and fine-grained access control.

Fauna vs Atlas

Organizations choose Fauna over MongoDB Atlas because it combines the relational qualities of joins, and schema enforcement, and ACID transactions with the flexibility of a document database, delivered as a fully serverless cloud API – ensuring seamless scalability and zero operational overhead.

Administration & Deployment

MongoDB Atlas

Hosted. Skill, effort, & resources required to design and manage clusters (sizes, configurations & features to achieve performance/costs).

Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.

MongoDB Atlas

Administration & Deployment

Engine

MongoDB Atlas

Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.

Next generation distributed architecture. Fauna’s Distributed Transaction Engine (DTE) allows for optimal scaling, resource efficiency & performance without engineering resources.

MongoDB Atlas

Administration & Deployment

Cluster Scaling

MongoDB Atlas

Vertical with manual horizontal scaling. Requires complex sharding strategy.

Horizontal scaling. Auto-provisioned with no sharding.

MongoDB Atlas

Administration & Deployment

Auto-Scaling

MongoDB Atlas

Hourly.

On-demand.

MongoDB Atlas

Administration & Deployment

Replication Model

MongoDB Atlas

Native single-region clusters require add-on features and architectural configuration & planning for multi-region deployments & failover.

Auto-replication across three locations.

MongoDB Atlas

Administration & Deployment

Resiliency & Availability

MongoDB Atlas

Native single-region clusters require add-on features and architectural configuration & planning for multi-region deployments & failover.

Native multi-region w/ active-active reads & writes, auto-routing, & strong consistency for automatic availability & resilience.

MongoDB Atlas

Administration & Deployment

Data Model

MongoDB Atlas

Document. No native joins. Strongly recommends utilizing “embedded” patterns and avoiding normalization. 

Document-relational. Data stored in JSON documents with native joins and full relational support. Embed or normalize - providing optionality for any schema model or access pattern. 

MongoDB Atlas

Administration & Deployment

Schema Support

MongoDB Atlas

Schema validation. Limited options for data validation. Schema applied at write-time.

Schema enforcement. Evolve schema and enforce constraints as applications mature and scale. Constraints, computed fields, & type enforcement for full control.

MongoDB Atlas

Administration & Deployment

Consistency Model

MongoDB Atlas

Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.

Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.

MongoDB Atlas

Administration & Deployment

Tenancy

MongoDB Atlas

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

Connectivity

MongoDB Atlas

Session-pooling. Stateful sessions requiring sized pools.

HTTPS API. Stateless & sessionless for secure & scalable connectivity.

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

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

Engine

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

Next generation distributed architecture. Fauna’s Distributed Transaction Engine (DTE) allows for optimal scaling, resource efficiency & performance without engineering resources.

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

Cluster Scaling

MongoDB Atlas

Vertical with manual horizontal scaling. Requires complex sharding strategy.

MongoDB Atlas

Horizontal scaling. Auto-provisioned with no sharding.

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

Auto-Scaling

MongoDB Atlas

Hourly.

MongoDB Atlas

On-demand.

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

Replication Model

MongoDB Atlas

Native single-region clusters require add-on features and architectural configuration & planning for multi-region deployments & failover.

MongoDB Atlas

Auto-replication across three locations.

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

Resiliency & Availability

MongoDB Atlas

Native single-region clusters require add-on features and architectural configuration & planning for multi-region deployments & failover.

MongoDB Atlas

Native multi-region w/ active-active reads & writes, auto-routing, & strong consistency for automatic availability & resilience.

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

Data Model

MongoDB Atlas

Document. No native joins. Strongly recommends utilizing “embedded” patterns and avoiding normalization. 

MongoDB Atlas

Document-relational. Data stored in JSON documents with native joins and full relational support. Embed or normalize - providing optionality for any schema model or access pattern. 

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

Schema Support

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

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

Consistency Model

MongoDB Atlas

Eventually consistent by default. Strong consistency comes with performance impact and design challenges.

MongoDB Atlas

Strictly serializable; strongest level of consistency by default across regions, without performance concessions.

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

Tenancy

MongoDB Atlas

Native single-tenant. Significant effort required to design scalable, efficient, and secure multi-tenant system.

MongoDB Atlas

Native multi-tenant. Databases as logical containers. No limit in number. Instantly available. API delivery with tokens simplifies scoped permissions.

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

Connectivity

MongoDB Atlas

Session-pooling. Stateful sessions requiring sized pools.

MongoDB Atlas

HTTPS API. Stateless & sessionless for secure & scalable connectivity.

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

Authentication

MongoDB Atlas

Traditional account-based. Static, long-lived security contexts with limited access.

MongoDB Atlas

Token-based. Ability to enforce identity-based authorization.

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

MongoDB Atlas

Administration & Deployment

Hosted. Skill, effort, & resources required to design and manage clusters (sizes, configurations & features to achieve performance/costs).

Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.

Engine

Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.

Next generation distributed architecture. Fauna’s Distributed Transaction Engine (DTE) allows for optimal scaling, resource efficiency & performance without engineering resources.

Cluster Scaling

Vertical with manual horizontal scaling. Requires complex sharding strategy.

Horizontal scaling. Auto-provisioned with no sharding.

Auto-Scaling

Hourly.

On-demand.

Replication Model

Native single-region clusters require add-on features and architectural configuration & planning for multi-region deployments & failover.

Auto-replication across three locations.

Resiliency & Availability

Native single-region clusters require add-on features and architectural configuration & planning for multi-region deployments & failover.

Native multi-region w/ active-active reads & writes, auto-routing, & strong consistency for automatic availability & resilience.

Data Model

Document. No native joins. Strongly recommends utilizing “embedded” patterns and avoiding normalization. 

Document-relational. Data stored in JSON documents with native joins and full relational support.  Embed or normalize -  providing optionality for any schema model or access pattern. 

Schema Support

Schema validation. Limited options for data validation. Schema applied at write-time.

Schema enforcement. Evolve schema and enforce constraints as applications mature and scale. Constraints, computed fields, & type enforcement for full control.

Consistency Model

Eventually consistent by default. Strong consistency comes with performance impact and design challenges.

Strictly serializable; strongest level of consistency by default across regions, without performance concessions.

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.

Connectivity

Session-pooling. Stateful sessions requiring sized pools.

HTTPS API. Stateless & sessionless for secure & scalable connectivity.

Authentication

Traditional account-based. Static, long-lived security contexts with limited access.

Token-based. Ability to enforce identity-based authorization.

Fauna vs Atlas Serverless

Fauna's automatic scaling, data model flexibility, and global distribution provides a robust and flexible solution that outperforms the limited Atlas Serverless offering.

Scale

MongoDB Atlas

Optimized for small & simple use cases. Atlas Serverless does not support multi-region deployments, multi-cloud deployments, sharded deployments, global clusters, triggers, Atlas Search, and a variety of aggregation pipeline functions. 

Fully available, at-scale. Natively serverless. Multi-region, strongly consistent and low latency with joins, change streams out of the box.

MongoDB Atlas

Administration & Deployment

Features

MongoDB Atlas

Limited. Many Atlas (dedicated cluster) features unavailable, or come with strict limits. i.e. Connection pool size, DB count, no Global Clusters or Sharding or multi-region, data < 1TB, no change streams, max 50 databases, max 500 connections, etc.

Production ready out of the box for sophisticated use cases. Multi-region replication, strong consistency, full relational capabilities, auto-routing and low latency – all delivered as an API.

MongoDB Atlas

Administration & Deployment

Cost profile

MongoDB Atlas

Expensive to scale. Node-based cluster model passes along costs, with premium applied, for maintaining under-utilized resources.

Optimized for scale. Large multi-tenant cluster enables optimal resource allocation, eliminating over-provisioning.

MongoDB Atlas

Administration & Deployment

Connectivity

MongoDB Atlas

Connection pool based. Mandates architectural considerations between front and back end. Incompatible with modern, API-first architectures.

API delivery based. Unlimited scale, direct access (no middleware), simplified architecture, auto-routing, lowest geographical latency. Great for edge or serverless use cases.

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

Administration & Deployment

MongoDB Atlas

MongoDB Atlas

Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.

Serverless. No ops. All features natively available, over multi-region, with consistency. Scale-ready & configuration-free serverless.

Administration & Deployment

Atlas Serverless

Scale

Limited. Many Atlas (dedicated cluster) features unavailable, or come with strict limits. i.e. Connection pool size, DB count, no Global Clusters or Sharding or multi-region, data < 1TB, no change streams, max 50 databases, max 500 connections, etc.

Fully available, at-scale. Natively serverless. Multi-region, strongly consistent and low latency with joins, change streams out of the box.

Features

Optimized for small & simple use cases. Atlas Serverless does not support multi-region deployments, multi-cloud deployments, sharded deployments, global clusters, triggers, Atlas Search, and a variety of aggregation pipeline functions.  

Production ready out of the box for sophisticated use cases. Multi-region replication, strong consistency, full relational capabilities, auto-routing and low latency – all delivered as an API.

Cost profile

Expensive to scale. Node-based cluster model passes along costs, with premium applied, for maintaining under-utilized resources.

Optimized for scale. Large multi-tenant cluster enables optimal resource allocation, eliminating over-provisioning.

Connectivity

Connection pool based. Mandates architectural considerations between front and back end. Incompatible with modern, API-first architectures.

API delivery based.  Unlimited scale, direct access (no middleware), simplified architecture, auto-routing, lowest geographical latency.  Great for edge or serverless use cases.

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CEO @ Cloaked

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Traditional node-based architecture. Single compute layer. Nuanced CPU to memory sizing. Relies on local storage or memory caching for IO performance.