MongoDB vs PostgreSQL

by Gralio Feb 27, 2025

MongoDB and PostgreSQL cater to different data management needs. MongoDB prioritizes flexibility and scalability for unstructured data, ideal for modern applications and agile development. PostgreSQL emphasizes data integrity and relational structure, suitable for transactional systems and applications requiring strong ACID properties.

At Gralio.ai we help to simplify your decision-making process by offering detailed, side-by-side software comparisons like this one, to help you confidently choose the tool that aligns with your business goals.

This comparison was created by analysing 1466 reviews and 60 websites, saving 8 hours, 38 minutes of reading.

MongoDB

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PostgreSQL

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About

MongoDB is a flexible database management software designed for modern businesses. Unlike traditional systems that rely on rigid tables, MongoDB stores data as documents, similar to how we organize information in files. This makes it easier for developers to build applications and adapt to changing needs. MongoDB excels at handling large volumes of diverse data, making it suitable for various tasks like real-time analytics and managing online catalogs.

PostgreSQL is a reliable, open-source database system businesses use to store and manage their data. Known for its reliability and performance, it's suitable for a wide range of needs, from single computers to large-scale data storage for multiple users. PostgreSQL is free to use and is backed by a large community of developers.

Summary

Main difference

MongoDB is better for businesses needing a flexible, schema-less database for handling large volumes of unstructured data, particularly in the software and IT sectors. PostgreSQL is better for applications requiring strong data integrity, relational data structures, and ACID properties, making it suitable for financial and e-commerce applications.

Relative strengths of MongoDB (compared to PostgreSQL)

Flexible Schema: Adapts easily to evolving data structures, simplifying development and allowing for faster iterations.
Scalability: Handles large volumes of data and high traffic loads efficiently, suitable for growing businesses.
Ease of Use for Developers: The document-oriented model aligns well with how developers structure data in applications, making it easier to work with.

Relative weaknesses of MongoDB (compared to PostgreSQL)

Limited JOIN Support: Complex queries involving relationships between data can be challenging.
Higher Memory Consumption: Can require more memory compared to relational databases, impacting infrastructure costs.
Steeper Learning Curve for SQL Users: Requires learning a new query language and data model, potentially increasing training time.

Who should use MongoDB VS. PostgreSQL

MongoDB is a versatile database solution designed for modern businesses seeking flexibility and scalability. Its document-based model simplifies development and adapts to evolving data structures. Users praise its flexible schema and powerful query language but note the learning curve and limited JOIN support. A strong choice for handling large datasets and real-time analytics.

PostgreSQL is a powerful, open-source database known for its reliability and performance. Users appreciate its efficient handling of large datasets and fast query times. It's an excellent choice for businesses of all sizes needing a robust and scalable database solution. However, some users find the initial setup complex and the CPU usage somewhat high.

Ideal for small to large businesses (1-1000+ employees).
Best fit for Software, IT & Telecommunications.

Excellent fit for small, medium, and large businesses.
Ideal for IT, suitable for e-commerce, retail, and public sector, less suitable for industries like healthcare, manufacturing, and logistics.

MongoDB and PostgreSQL features

Supported

Partially supported

Not supported

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Object-relational database

Not supported

MongoDB is a NoSQL document database, not an object-relational database.

Supported

PostgreSQL is an object-relational database management system (ORDBMS).
Data Storage

Supported

MongoDB stores data centrally using a document-oriented model.

Supported

PostgreSQL is a relational database, organizing data in tables for efficient centralized storage.
Data Retrieval

Supported

MongoDB supports efficient data retrieval with its query system, indexing, and aggregation framework.

Supported

PostgreSQL allows efficient data retrieval with SQL queries, indexing, and query optimization.
SQL support

Partially supported

MongoDB does not support SQL directly, but offers SQL-like querying via tools like the BI Connector and Atlas SQL Interface.

Supported

PostgreSQL, as offered via EDB on AWS Marketplace, fully supports SQL querying.
NoSQL support

Supported

MongoDB is a NoSQL database, it natively supports querying data stored within it.

Not supported

PostgreSQL does not have built-in support for querying data in NoSQL databases like MongoDB or Cassandra. However, it offers NoSQL-like capabilities through its support for JSON and JSONB data types, enabling it to handle and query semi-structured data in a manner similar to NoSQL databases.
PL/SQL support

Not supported

MongoDB does not support PL/SQL, which is specific to Oracle databases. While Oracle provides an API for MongoDB, it allows using SQL within MongoDB, not PL/SQL.

Not supported

PostgreSQL does not support PL/SQL for querying Oracle databases. It uses its own procedural language, PL/pgSQL.

Qualities

Value and Pricing Transparency

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Customer Service

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Ease of Use

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Reliability and Performance

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Ease of Implementation

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Scalability

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MongoDB and PostgreSQL Pricing

No data

See full Pricing page

No data

We couldn't find a pricing page for PostgreSQL.

MongoDB and PostgreSQL review insights

Users love

Flexible schema design, allowing for evolving data structures
Scalability and efficient handling of large datasets
Powerful query language and aggregation framework
Active community and extensive documentation

Open-source database
Easy to install and configure
Handles large datasets efficiently
Fast querying time
Strong community support

Users dislike

Limited JOIN support, making complex queries challenging
High memory consumption, especially with large datasets
Steep learning curve for beginners, especially those familiar with SQL databases
Backup and restore operations can be complex compared to traditional relational databases

Consumes somewhat higher CPU usage so that efficiency is a bit low
Initial setup is not straightforward.
Limited GUI tools.
PostgreSQL may have slower performance than other RDBMS like SQL Server and MySQL.

MongoDB and PostgreSQL Ratings

No data

Company health

Employee growth

5% increase in the last year

1% increase in the last year

Web traffic

3% decrease in the last quarter

2% decrease in the last quarter

Financing

No data

How important is strict schema enforcement for your data integrity?

Strict schema enforcement is crucial for data integrity if you prioritize consistency and reliability. PostgreSQL, with its relational structure and SQL foundation, enforces a strict schema, ensuring data conforms to predefined rules and minimizing inconsistencies. MongoDB, being schema-less, offers flexibility but may increase the risk of data integrity issues if not carefully managed. Therefore, if strict enforcement is a primary concern, PostgreSQL is the more suitable choice.

Which database better suits your team's existing SQL expertise?

PostgreSQL better suits a team with existing SQL expertise. PostgreSQL is a relational database that uses standard SQL syntax, while MongoDB is a NoSQL database with its own query language. For a team already familiar with SQL, PostgreSQL will require significantly less training and allow them to leverage their existing skills.

What are the advantages of MongoDB?

MongoDB offers advantages in terms of flexibility and scalability. Its document-oriented data model makes it easier for developers to adapt to evolving data structures and handle diverse data types, unlike PostgreSQL's rigid relational model. MongoDB is also known for its ability to scale horizontally and handle large datasets efficiently, making it suitable for modern applications with high data volume and velocity. While PostgreSQL excels in data integrity and ACID compliance, MongoDB prioritizes flexibility and scalability for rapid development and evolving data needs.

What are the disadvantages of MongoDB?

MongoDB has limited JOIN support, making complex queries challenging. It also has high memory consumption, particularly with large datasets. Users have reported a steep learning curve, especially for those familiar with SQL databases. Finally, backup and restore operations can be more complex compared to traditional relational databases.

Alternatives to MongoDB and PostgreSQL

Microsoft SQL Server

Microsoft SQL Server is a database management system for businesses of all sizes. It helps you analyze various types of data and can be used in multiple environments, including on your servers and in the cloud. SQL Server offers high performance and strong security features. It allows developers to build applications using different programming languages and provides mobile business intelligence tools.

MariaDB

MariaDB is a popular, free alternative to traditional database software. Developed by the creators of MySQL, it's known for its strong performance, reliability, and open-source nature. MariaDB is highly adaptable, working well for both transaction-heavy tasks (like processing orders) and analyzing large datasets. It's commonly used by companies of all sizes and is a key part of many cloud computing services. Notably, MariaDB offers similar features as more expensive database products but without the high cost.

Amazon DynamoDB

Amazon DynamoDB is a fully managed database service that's part of the Amazon Web Services (AWS) cloud platform. It's designed to handle massive amounts of data and traffic, making it suitable for businesses with demanding applications, like mobile apps or e-commerce platforms. Unlike traditional databases, DynamoDB doesn't rely on servers, meaning it can scale its resources up or down automatically based on your needs. This ensures fast performance, high availability, and cost-efficiency as you only pay for what you use.

MySQL

MySQL is the world's most popular open-source relational database. It's a reliable and robust system used by companies of all sizes to store and manage data. MySQL offers a range of solutions from free downloads to enterprise-level packages with advanced features and support. Known for its flexibility and performance, MySQL is a popular choice for web applications, data analysis, and more.

MongoDB

MongoDB is a popular database that stores information in flexible documents instead of rigid tables. This makes it easy for developers to work with and allows for changes as your needs evolve. It's designed for modern applications and scales easily to handle large amounts of data. MongoDB is used by companies of all sizes and industries, particularly for managing customer data, online content, and real-time analytics.

ScyllaDB

ScyllaDB is a database designed for applications that need to handle massive amounts of data with very fast response times. Think of it like a super-efficient filing system that’s always lightning fast, even with billions of files. It's ideal for companies experiencing rapid growth that need to ensure their systems remain fast and reliable as they add more users and data. ScyllaDB boasts lower operating costs compared to other databases and is used by companies like Disney+ and Expedia.

Page co-authored by

Michal Kaczor

CEO at Gralio

Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.

Tymon Terlikiewicz

CTO at Gralio

Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.

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