PostgreSQL vs SQL Server: What are the key differences?

Trying to find the right database for your applications? When it comes to choosing a database technology, the most common SQL options to consider are PostgreSQL vs. SQL Server. While both systems share many core features, there are some key differences—the major one being that PostgreSQL is open source and SQL Server is owned by Microsoft.

Today, it is more vital than ever for companies to be able to manage, store, and activate data for modern business operations. With the growing assortment of databases available to choose from, it can be overwhelming to pick the right one for your applications.

The most important thing to remember is that no single database will be a good match for every project requirement, so it’s critical to understand the option that will work best for your specific use case.

So, what is PostgreSQL vs. SQL Server? In this short guide, we’ll discuss the basic differences between PostgreSQL and SQL Server.

Get started for free Stay informed

What is SQL?

Structured Query Language or SQL, as it's more commonly known, is a programming language used to manage, query, and retrieve data in a relational database. It is the standard language used by relational database management systems (RDBMS), including PostgreSQL, SQL Server, MySQL, and Oracle Database.

SQL typically uses commands written in statement format for queries and other database operations, which allow users to manipulate data in relational database tables. While originally created for relational databases, SQL acts as a foundation for many of today’s technology capabilities, making SQL knowledge an essential skill for many technology roles today, including data analysts, database engineers, and even backend programming.

However, you will find that there are different variants of SQL depending on the database or database management system that you choose.

What is Microsoft SQL Server?

SQL Server is a leading RDBMS that is built on top of SQL and developed by Microsoft. It is used to manage and store data to support numerous enterprise use cases for business intelligence, transaction processing, data analytics, and machine learning services.

SQL Server has a row-based table structure that allows you to connect related data elements from different tables without having to store data multiple times in a database.

In general, Microsoft SQL Server is known for its high availability, fast performance when handling large workloads, and easy integration with other applications to gain business intelligence across your entire data estate.

For more information, we recommend reviewing the official SQL Server documentation.

What is PostgreSQL?

PostgreSQL is an open source object-relational database management system, released under the PostgreSQL License. It supports both relational (SQL) and non-relational (JSON) and offers advanced SQL functions, including foreign keys, subqueries, and triggers. PostgreSQL is also highly extensible, allowing you to define data types and generate custom functions.

It boasts several robust extensions, including point-in-time recovery, granular access controls, multi-version concurrency (MVCC), and tablespaces. PostgreSQL also supports ACID (atomicity, consistency, isolation, durability) properties and is highly fault tolerant thanks to write-ahead logging. Furthermore, since it’s open source, it can run on almost all major operating systems, including Linux, Microsoft, OS X, and Unix.

Typically, companies choose PostgreSQL as the main data warehouse or data store to support internet-scale web, mobile, and geospatial applications.

For more information, we recommend reviewing the official PostgreSQL documentation.

SQL Server and PostgreSQL similarities

In general, SQL Server and PostgreSQL are among the most popular relational database management systems out there based on their performance, security, scalability, and usability. Both include relational database features and offer compatibility with an extensive range of small and large enterprise applications.

Typically, SQL Server has been the choice for bigger organizations that depend on Microsoft products. PostgreSQL, however, has carved out space for itself as a free, easy-to-implement database management system that offers maximum flexibility and functionality.

Difference between SQL Server and PostgreSQL

At a high level, here is a basic overview of the differences between SQL Server and PostgreSQL:

SQL Server	PostgreSQL
Relational database management system	Object-relational database management system
Commercial product from Microsoft	Open source (completely free)
Runs only on Microsoft or Linux	Runs on most machines and operating systems
Uses Transact-SQL or T-SQL (standard SQL + extra functionality)	Uses Standard SQL

SQL Server

PostgreSQL

Relational database management system

Object-relational database management system

Commercial product from Microsoft

Open source (completely free)

Runs only on Microsoft or Linux

Runs on most machines and operating systems

Uses Transact-SQL or T-SQL (standard SQL + extra functionality)

Uses Standard SQL

Pricing

As a product owned by Microsoft, SQL Server is available for use through a commercial core-based license as a Standard or Enterprise edition with prices ranging from $3,586 to $13,748. There are also two free versions, a full-featured edition for developers available for non-production workloads and a free Express edition with limited features and database sizes.

PostgreSQL is open source, released under the PostgreSQL License. This means there is no fee for using this product for any purpose, including commercial use. According to the PostgreSQL Global Development Group, PostgreSQL will remain free and open source in perpetuity and there are no plans to change the license or release the product under a different license.

Platform support

As an open source platform, PostgreSQL supports most major operating systems. It can be hosted on a wide range of operating systems, including Linux, macOS, Windows, BSD, and Solaris. You can also deploy it on Docker containers or Kubernetes.

On the other hand, SQL Server does not support operating systems other than Microsoft Windows, Microsoft Server, or Linux.

Syntax and language

SQL Server and PostgreSQL both use standard SQL query language, but also implement their own version of the SQL language—a SQL dialect.

SQL Server uses Transact-SQL, or T-SQL, which provides all the same functionality of SQL and adds several proprietary programming extensions. In PostgreSQL, you can use SQL along with its own procedural language PL/pgSQL that allows you to create functions, trigger procedures, and add control structures to SQL.

The syntax is very similar with a few notable differences. Here are some examples of the most basic differences between SQL Server and PostgreSQL:

	SQL Server	PostgreSQL
SELECT ...	Select [col1], [col2]	SELECT col1, col2
Aliases for columns and tables	SELECT AVG(col1)=avg1	SELECT AVG(col1) AS avg1
Working with dates	GETDATE() DATEPART()	CURRENT_DATE() CURRENT_TIME() EXTRACT()

SQL Server

PostgreSQL

SELECT ...

Select [col1], [col2]

SELECT col1, col2

Aliases for columns and tables

SELECT AVG(col1)=avg1

SELECT AVG(col1) AS avg1

Working with dates

GETDATE() DATEPART()

CURRENT_DATE() CURRENT_TIME() EXTRACT()

In regards to programming language support, there is quite a large difference between SQL Server and PostgreSQL. PostgreSQL supports Python, PHP, Perl, Tcl, Net, C, C++, Delphi, Java, JavaScript (Node.js), and more.

SQL Server is more limited, offering support for Java, JavaScript (Node.js), C#, C++, PHP, Python, and Ruby.

RDBMS vs. ORDBMS

A relational database management system (RDBMS) is based on the relational model of data. An object-relational database management is based on the relational model with additional support for object-oriented concepts, such as classes, objects, and inheritance.

An RDBMS like SQL Server is well-suited for handling traditional application tasks for data processing and administration while an ORDBMS like PostgreSQL is typically used for applications that contain complex objects.

For example, an object-relational database management system can handle new data types like video, audio, and image files that RDBMSs are not equipped to handle.

PostgreSQL vs. SQL Server: pros and cons

Beyond these basic differences, there are several pros and cons to each database management system that you should consider when trying to identify what best suits your business needs and requirements.

Below are some of the most commonly mentioned advantages and disadvantages of PostgreSQL:

Advantages	Disadvantages
Highly extensible to add functions, data types, languages, and more	Slower performance compared to other RDBMS like SQL Server and MySQL
Support for unstructured data types (for example, audio, video, and images)	Stronger focus on compatibility, speed improvements require extra work
MVCC for concurrent processing and high rates of transactions without almost no deadlock	Installation can be difficult for beginners
High availability and server failure recovery
Advanced security features like data encryption, SSL certificates, and advanced authentication methods
Active open source community continually improves and updates solutions

Advantages

Disadvantages

Highly extensible to add functions, data types, languages, and more

Slower performance compared to other RDBMS like SQL Server and MySQL

Support for unstructured data types (for example, audio, video, and images)

Stronger focus on compatibility, speed improvements require extra work

MVCC for concurrent processing and high rates of transactions without almost no deadlock

Installation can be difficult for beginners

High availability and server failure recovery

Advanced security features like data encryption, SSL certificates, and advanced authentication methods

Active open source community continually improves and updates solutions

Below are some of the advantages and disadvantages of SQL Server:

Advantages	Disadvantages
High performance and in-memory database capabilities	No support for MVCC, depends on default locking to avoid errors
Built-in security features, such as alerts, monitoring, data protection, and data classification	Licensing, support, and advanced feature costs are expensive
Simple to install and configure with easy-to-use interface and automatic updates	Hardware restrictions may require you to upgrade your machines to support newer SQL Server versions
Convenient backup and data recovery features and high availability tools
Tasks can be scheduled using SQL Server Management Studio
Works well with other Microsoft data analytics, development, and monitoring tools

Advantages

Disadvantages

High performance and in-memory database capabilities

No support for MVCC, depends on default locking to avoid errors

Built-in security features, such as alerts, monitoring, data protection, and data classification

Licensing, support, and advanced feature costs are expensive

Simple to install and configure with easy-to-use interface and automatic updates

Hardware restrictions may require you to upgrade your machines to support newer SQL Server versions

Convenient backup and data recovery features and high availability tools

Tasks can be scheduled using SQL Server Management Studio

Works well with other Microsoft data analytics, development, and monitoring tools

No matter what database you choose, it’s worth considering cloud databases instead of using your own on-premises data center. Running your databases on a cloud or hybrid cloud infrastructure can deliver a range of business benefits, such as cost savings, scalability, better reliability, easier management and maintenance, and stronger data security.

Google Cloud offers a wide range of one-of-a-kind cloud database systems to deliver massive scalability and data durability on the same underlying architecture that powers Google’s most popular products.

Our databases support the most popular commercial and open source engines, such as SQL Server, PostgreSQL, MySQL, Oracle, and Redis. They also easily integrate with our larger ecosystem of market-leading services, such as BigQuery, Looker, and Google Kubernetes Engine.