Build Faster with MongoDB Atlas

MongoDB Atlas provides native support for flexibility and scalability, simplifying what PostgreSQL often complicates. See how MongoDB Atlas empowers faster, more adaptable development for modern applications.

	MongoDB Atlas	PostgreSQL
Database Model	MongoDB Atlas is a general purpose document database. Data is modeled in JSON-like syntax, which is stored in a binary format on disk.	PostgreSQL is a relational database, storing data in tables with rows and columns. It offers powerful query features, but binds users to strict schema control.
Data Governance	MongoDB’s flexible schema allows developers to dynamically evolve the database to their application’s demands. MongoDB offers built-in support for strict schema validation when required by the application.	A strict schema is always imposed, leading to slower development for rapidly changing applications.
Versatility	MongoDB Atlas supports a myriad of workloads all from within the same platform and a single connection string. Developers can power their application's operational, search, vector search, analytics, time series, or other features all via a unified API and single plane of control.	There are many third party libraries or products that provide features to enable PostgreSQL to serve non-operational use cases. The downside is that these require additional education, development, cost planning, and maintenance as they turn an application’s stack into a collection of disparate systems.
Analytical Capabilities	The MongoDB Aggregation Pipeline offers powerful and expressive query capabilities for handling analytical workloads with ease.	PostgreSQL’s SQL engine is also well known for its ability to handle analytical workloads across a myriad of use cases.
Distributed vs Monolithic	MongoDB is a distributed database by design with replication, self-healing recovery, native sharding, and backup & restore processes.	PostgreSQL is designed for scale-up, not out. Horizontal scaling requires third party tools and custom engineering.
Unstructured Data Support	MongoDB is built around the concept of JSON documents, extended into BSON (binary JSON) to support advanced data types.	PostgreSQL’s JSON and JSONB support is retrofitted by storing JSON data into a single column which incurs significant overhead. While this is acceptable for certain use cases, it does not replace a document database.

MongoDB Atlas
Database Model	MongoDB Atlas is a general purpose document database. Data is modeled in JSON-like syntax, which is stored in a binary format on disk.
Data Governance	MongoDB’s flexible schema allows developers to dynamically evolve the database to their application’s demands. MongoDB offers built-in support for strict schema validation when required by the application.
Versatility	MongoDB Atlas supports a myriad of workloads all from within the same platform and a single connection string. Developers can power their application's operational, search, vector search, analytics, time series, or other features all via a unified API and single plane of control.
Analytical Capabilities	The MongoDB Aggregation Pipeline offers powerful and expressive query capabilities for handling analytical workloads with ease.
Distributed vs Monolithic	MongoDB is a distributed database by design with replication, self-healing recovery, native sharding, and backup & restore processes.
Unstructured Data Support	MongoDB is built around the concept of JSON documents, extended into BSON (binary JSON) to support advanced data types.

PostgreSQL
Database Model	PostgreSQL is a relational database, storing data in tables with rows and columns. It offers powerful query features, but binds users to strict schema control.
Data Governance	A strict schema is always imposed, leading to slower development for rapidly changing applications.
Versatility	There are many third party libraries or products that provide features to enable PostgreSQL to serve non-operational use cases. The downside is that these require additional education, development, cost planning, and maintenance as they turn an application’s stack into a collection of disparate systems.
Analytical Capabilities	PostgreSQL’s SQL engine is also well known for its ability to handle analytical workloads across a myriad of use cases.
Distributed vs Monolithic	PostgreSQL is designed for scale-up, not out. Horizontal scaling requires third party tools and custom engineering.
Unstructured Data Support	PostgreSQL’s JSON and JSONB support is retrofitted by storing JSON data into a single column which incurs significant overhead. While this is acceptable for certain use cases, it does not replace a document database.

Intuitive Data Model: Faster and Easier for Developers

In MongoDB Atlas, the leading document database, documents map to the objects in code. There is no need to decompose data across tables, run expensive JOINs, or integrate a separate ORM layer as PostgreSQL requires. Data that is accessed together is stored together, leading to less code needed to interact with the database, and subsequently higher performance. Schemas can also be modified at any time, without expensive schema migrations or downtime.

MongoDB Atlas Scaling and Performance Tuning are Built in and Automatic

PostgreSQL does not offer native mechanisms to partition the database across a cluster of nodes — whether storing relational or JSON data types. To scale, custom engineering and/or third party software is required. MongoDB Atlas has scale out and scale up mechanisms built in, such as the ability to automatically expand clusters, shard data, and identify performance problems. This can all be done with minimal involvement from engineers, allowing them to focus on higher-value work.

Address a Broader Range of Workloads on MongoDB Atlas

MongoDB Atlas is a foundation for working with application data, a true developer data platform. Atlas Database, Search, and Data Federation serve any class of transactional, operational, or analytical workloads through a common API. To get the same power, PostgreSQL must be extended with third party products that require additional engineering and support.

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