PostgreSQL remains one of the most popular choices in the entire database landscape when it comes to its popularity.
However, in this blog, we are not just here to talk about PostgreSQL and how it is excellent when it comes to databases.
We are here to combine PostgreSQL with Artificial Intelligence (AI) and find out if PostgreSQL is indeed the best choice when it comes to AI-driven applications.
The reason why this is a very important question in this day and age is because PostgreSQL is definitely one of the most popular databases out to their as it is open-source and extremely flexible with an ability to handle both traditional and modern data formats.
It also has the capability for excellent extensibility and all these benefits make it sound like the ideal choice for any AI project.
We are here to find out if PostgreSQL really translates to being the best option when it comes to being used for your next AI project in 2025.
That is why we will look at some very important things when it comes to PostgreSQL including its capabilities and features and look at how PostgreSQL can support AI use cases.
We will also compare it to other popular databases when it comes to AI projects and AI applications and see whether it meets the mark.
The reason why talking about PostgreSQL is so important is simply because it is one of the most popular databases out there.
According to the survey carried out by Stack Overflow in their 2023 Developer Survey, it was found out that PostgreSQL was the most preferred option among professional developers.
Nearly 50% of all professional developers from the over 60,000 responses mentioned using PostgreSQL as their preferred option and this speaks a lot about the usability and applicability of PostgreSQL.
So, let us understand what this type is all about by looking at PostgreSQL in the era of AI.
PostgreSQL in the Era of AI
PostgreSQL is one of the best choices out there when it comes to AI implementation because it is able to meet all the demands of modern data science and AI projects with ease. This is why it is able to do so.
Native Support for Diverse Data Types
PostgreSQL has excellent native support for different data types which includes both structured as well as unstructured data and it is able to also handle complex data formats in the form of arrays and ranges.
Seamless Integration with AI Frameworks
If you plan on conducting AI projects then you need support with popular AI programming languages such as Python as well as JavaScript and others which PostgreSQL is able to do quite easily and it also has support for extensions such as “pgvector” which allows PostgreSQL to store and query vector data formation learning purposes.
Real-Time Data Processing
If you are planning on successfully executive and AI project then you need real-time data streaming and processing which is possible with PostgreSQL and it also allows for applications like predictive analytics, anomaly detection and much more.
Active Open-Source Community
When you are looking for solutions related to AI you are also going to need continuous updates regarding PostgreSQL so that it stays relevant in this fast AI landscape and that is definitely the case with PostgreSQL as it has one of the biggest open-source communities out there with innovations happening every day.
AI-Ready Architecture
Additionally, you also have AI-ready architecture when it comes to PostgreSQL along with optimisations for high performance and ideal for handling large-scale datasets which is a prerequisite when it comes to AI projects such as AI training and much more.
This was just a small sample size of a huge list of reasons why PostgreSQL is suited perfectly in the era of AI.
Key Features of PostgreSQL for AI Projects
Scalability & Performance
PostgreSQL ensures horizontal and vertical scalability with features such as table partitioning and parallel queries. Additionally, you also have the benefit of high throughput which is a very important requirement when it comes to real-time AI applications.
When it comes to AI applications, you need the database to perform query planning operations and that can be done quite easily with PostgreSQL with support for complex analytical queries.
PostgreSQL is the perfect balance of scalability and performance when it comes to AI applications and that is why it is excellent for AI workflows.
Advanced Querying Capabilities
One of the key features of PostgreSQL is definitely going to be advanced querying capabilities. This is because you have advanced querying features such as window functions which is ideal for data analytics as well as preprocessing.
You also have the support for JSON/JSONB which means it will be able to handle semi-structured data which is very common in AI datasets.
Apart from that you get full-text search which is a must when it comes to NLP-based AI projects that require very good text search capabilities.
And lastly, you have to the PL/pgSQL language which means the possibility of creation of custom stored procedures so that you can preprocess data directly within the database.
Extensibility & Customization
Advanced querying capabilities are just not enough if you have to choose a database for your next AI project because you also need good support for customisation as well as extensibility.
You do not have to compromise on that when it comes to PostgreSQL as with this database you get to create custom functions in all the languages such as Python, R, or SQL.
Apart from that you also get to use extensions like “pgvector” for vector similarity searches as well as “PostGIS” for geospatial AI.
The best thing about using PostgreSQL is that you can tailor it to your specific AI needs.
Integration with AI Frameworks
PostgreSQL has excellent support for integration with all the popular AI frameworks and tools such as Python (Pandas, NumPy, TensorFlow) and this is possible because of PostgreSQL’s Python connectors (like psycopg2) which allow for very easy data transfer between the database and the AI framework.
PostgreSQL has support for R which means statistical computing and AI model evaluation.
Apart from that you also have tools such as the “pgvector” extension which allows PostgreSQL to store and query vector data and this is something quite important when it comes to natural language processing or even recommendation systems.
Built-in Data Integrity & Security
And of course, when you are working on your AI project you do not want your data to get stolen and you want the handling of sensitive data to be of the utmost priority.
PostgreSQL science in this area as well with its features like encryption as PostgreSQL offers SSL encryption as well as data-at-rest encryption.
You also get ACID compliance with PostgreSQL and this ensures data reliability.
Apart from that you also get the opportunity for easy auditing in order to track database changes and also make sure that your project is always compliant according to industry regulations.
This is How PostgreSQL Supports AI Use Cases
Predictive Analytics
One of the most popular use cases for PostgreSQL when it comes to AI is definitely going to be predictive analytics as PostgreSQL enables the developer to efficiently store and retrieve historical data for model training and inference quite easily with PostgreSQL.
Natural Language Processing (NLP)
PostgreSQL does extremely well when it comes to Natural Language Processing (NLP) as it is able to handle text data with advanced search and JSON capabilities so the developer will not need to compromise on Natural Language Processing (NLP).
Recommendation Engine
If you are use cases include Recommendation Engines then again PostgreSQL is an excellent database for that as it utilizes pgvector for vector-based similarity searches.
Geospatial AI
Geospatial AI use cases are also handled very well with PostgreSQL as with PostGIS you get to create location-based AI models that can be utilized her all kinds of AI projects especially in the logistics industry. You just need to use your imagination on how you would like to use it.
PostgreSQL vs. Other Databases for AI Projects
If we compare PostgreSQL with other popular databases for AI projects such as MySQL, MongoDB and Oracle DB in different areas then we are going to look at results such as this.
- Open Source: When it comes to the aspect of being open source you have MySQL, MongoDB as well as PostgreSQL shine in this category.
- Extensibility: If you consider extensibility as a factor then apart from MySQL all the other three databases pass with flying colours.
- Structured and Unstructured: We can now come to the aspect of support for structured and unstructured data and then again, all the other three databases apart from MySQL stand tall in this requirement.
- ACID Compliance: Things change a little bit when it comes to ACID Compliance as now MongoDB does not have support for that.
- Cost Efficiency: When we are talking about AI projects, we need to talk about cost efficiency and all three databases are really good in this aspect apart from Oracle DB.
- AI Integration Support: And finally, we come to AI integration support and this is where PostgreSQL enjoys the best level of support apart from all the other databases.
PostgreSQL is the clear winner when you consider all the factors because it passes with flying colours in every aspect right from being open-source to being cost-efficient and much more.
Case Studies: Successful AI Projects Powered by PostgreSQL
PostgresML
The first project is probably the most popular project out there because we are talking about PostgresML and it is a project that brought machine learning and artificial intelligence directly into the PostgreSQL database along with support for text classification as well as translation and summarization and much more.
Pgvector
Pgvector is one of the most popular AI projects out there as it is an open-source extension for PostgreSQL. The purpose of this extension is to allow vector similarity between searches and can be utilised in AI applications where you need vector storage and similarity search.
Pgai
Up next, we have to talk about pgai and this kind of project enables the generation of embeddings directly within the PostgreSQL database and this project was developed by Timescale and can be utilised for natural language processing among other AI tasks.
Text to SQL and SQL to Text
Developed by EverSQL, Text to SQL and SQL to Text is a project that allows for the auto-generation of SQL queries from text and it can also be utilised to explain SQL queries in simple language. The name of this project is quite self-explanatory and we even encourage you to use this because it will come very handy for your AI projects.
DeepSQL/DL
And finally, we have DeepSQL/DL and it serves a very important function as for being an AI-charged database engine. DeepSQL/DL can be utilised to extend PostgreSQL with deep learning capabilities. This means that the database can now perform advanced operations such as predictive analytics as well as NLP and even image recognition within SQL. With this project, you get both AI and big data integration quite easily and it also ensures very good data processing and storage capabilities.
It is 2025 and when we think of AI, we have to think of PostgreSQL along with it as it is one of the most reliable and scalable database solutions out there when it comes to AI projects.
It is able to support different kinds of data types as well as advanced querying and also supports seamless integration with other AI frameworks.
This makes PostgreSQL a very powerful tool for developers when it comes to choosing a rich database for AI projects.
If you are someone like that and if you are a visionary willing to utilise PostgreSQL for your innovative AI project and you are looking for some of the most experienced developers in the industry, we are here for you.
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