5 PostgreSQL Plugins to Watch in 2025

PostgreSQL is becoming a key tool for AI, and as a Junior AI Model Trainer, you can boost your model’s accuracy by using the right extensions. We explore five PostgreSQL plugins expected to be essential in 2025, including pgvector for similarity searches and PostgresML for in-database machine learning. These plugins help you manage data and train models directly within PostgreSQL, streamlining your workflow and improving efficiency. By understanding these tools, you gain a competitive edge in developing and deploying AI solutions.

I. Introduction: PostgreSQL and the AI Revolution (Define Key Terms)

Let’s talk about how PostgreSQL, a super-powered database, is becoming best friends with Artificial Intelligence (AI). You might be thinking, “What does a database have to do with AI?” Well, a lot! This article will show you five cool PostgreSQL add-ons (called plugins) that will be important for AI in 2025.

Define PostgreSQL:
PostgreSQL (often called Postgres or psql) is a free and open-source database. Think of it as a super-organized digital filing cabinet. It’s really good at storing and managing lots of information. Because it’s open-source, anyone can use it and even change it to fit their needs. This makes it perfect for big projects that need to handle tons of data, like the ones used in AI.
Define Plugins:
Plugins are like LEGO bricks for PostgreSQL. They are special add-ons that give PostgreSQL extra abilities. The basic PostgreSQL system does a lot, but plugins let you customize it and make it even more powerful. You can add plugins to do things like work with special types of data or connect to other programs.
The Intersection of AI and PostgreSQL:
AI models need lots and lots of data to learn and work correctly. Imagine teaching a computer to recognize cats in pictures. You need to show it thousands of pictures of cats! PostgreSQL is great at storing all those pictures (or any other kind of data) in a safe and organized way. This makes it a perfect place to keep the information that AI models need.
Why Plugins are Crucial for AI:
Plugins help PostgreSQL and AI work together even better. Some plugins can help you get your data ready for AI. Others can even let you run parts of your AI model inside the database! This can make things much faster and easier. For example, a plugin can help you find pictures that are similar to each other, which is useful for things like recommending products or detecting fraud.
Target Audience Context (Junior AI Model Trainer):
As a Junior AI Model Trainer, you’re learning how to build and train AI models. Understanding these PostgreSQL plugins can help you in a few important ways:
- Better Data: Plugins can help you clean and prepare your data, which leads to better models.
- Faster Training: Some plugins can speed up the process of getting your data ready for training.
- Easier Deployment: You can even use plugins to run your models directly from the database, making them easier to use in real-world applications.
The 2025 Landscape:
The world of databases and AI is always changing. More and more people are using “serverless” databases, which are databases that you don’t have to worry about managing yourself. Companies like Neon, Supabase, Amazon (Aurora Serverless), Microsoft (Azure PostgreSQL), and Xata offer these types of databases. Also, there’s a growing need for “AI gateways,” which are tools that help connect AI models to other systems. These trends are making PostgreSQL plugins even more important.
Blog Post Overview:
In this article, we’re going to look at five PostgreSQL plugins that we think will be really important for AI in 2025. We’ll explain what each plugin does and how it can help you with your AI projects.
Disclaimer:
Keep in mind that things change quickly! While we think these plugins are promising, we can’t be 100% sure what will happen in 2025. But we hope this article gives you a good idea of how PostgreSQL and AI are working together.

II. Plugin 1: pgvector (Vector Similarity Search)

Let’s dive into our first awesome PostgreSQL plugin: pgvector.

What is pgvector?
pgvector is like a special tool you add to PostgreSQL. It helps the database store and search for things called “vector embeddings” really, really fast. Think of vector embeddings as secret codes for things like pictures, words, or sounds. These codes are made up of numbers that tell the computer what the thing means. For example, a picture of a cat will have a vector embedding that is similar to the vector embedding of another picture of a cat.
Why is it important for AI?
AI often needs to find things that are similar. Imagine you’re building a system that recommends movies. You want to find movies that are like the movies a person already enjoys. pgvector makes this possible! It lets AI quickly find the “closest” vector embeddings, meaning things that are most alike. This is called similarity search, and it’s super important for lots of AI jobs.
How it works:
pgvector uses clever tricks to find similar vectors quickly. One of these tricks is called HNSW (Hierarchical Navigable Small World). Don’t worry about the complicated name! Just think of it as a super-organized map that helps pgvector zoom in on the vectors that are most likely to be similar. It’s like having a super-fast search engine just for vector embeddings!
Use Cases:
Here are some examples of how pgvector is used in the real world:
- Image Retrieval: Finding pictures that look like another picture. Imagine searching for “red dress” and finding all dresses that are mostly red.
- Natural Language Processing (NLP): Understanding what words mean and finding documents that are about the same topic. Think of a program that can summarize a long article.
- Recommendation Systems: Suggesting products, movies, or music that you might like based on what you’ve liked before. Like when Netflix suggests shows for you.
- Fraud Detection: Spotting unusual transactions that might be fraudulent by comparing them to past fraudulent activities.
Integration with AI Frameworks:
Good news! pgvector works well with the tools that AI experts use, like TensorFlow, PyTorch, and scikit-learn. There are special libraries that help you easily move data between these AI tools and your PostgreSQL database with pgvector. This makes it easier to build AI models that use similarity search.
Performance Considerations:
pgvector is much faster than trying to find similar things without it. However, the speed depends on a few things:
- Index Size: The bigger the “map” (HNSW index), the longer it takes to build but the faster it is to search.
- Query Complexity: The more complicated the search, the longer it takes.
- Hardware Resources: A faster computer with more memory will make pgvector work even better.
Future Developments:
pgvector is getting even better! People are working on new ways to make it faster and more efficient. They are also exploring how to connect it with other PostgreSQL features. Imagine even faster searches and smarter ways to use vector embeddings!
Relevance to Junior AI Model Trainers:
As a Junior AI Model Trainer, understanding pgvector can help you build better AI models. If your model needs to find similar things, pgvector can make it faster and more accurate. This means you can train your models more efficiently and get better results. Knowing how to use pgvector is a valuable skill for any AI professional!

III. Plugin 2: PostgresML (In-Database Machine Learning)

Now, let’s explore another amazing PostgreSQL plugin: PostgresML.

What is PostgresML?
PostgresML is like adding a mini-AI lab right inside your PostgreSQL database. It’s a special tool that lets you train (teach) and use machine learning models directly within Postgres. Think of it as teaching your database to make predictions! It’s an extension, meaning it adds new features to your existing PostgreSQL setup.
Why is it important for AI?
Imagine you have all your data in PostgreSQL. Normally, to use AI, you’d have to copy that data to a different program, train your AI model, and then copy the results back. That’s slow and complicated! PostgresML fixes this. It lets you train and use AI models inside the database. This is super helpful because:
- It’s faster: No more copying data back and forth.
- It’s safer: Your data stays secure inside the database.
- It’s easier: Everything is in one place, making things simpler to manage.
Supported Algorithms:
PostgresML supports many different types of machine learning “recipes,” called algorithms. Some examples include:
- Linear Regression: Predicting a number based on other numbers (like predicting house prices).
- Logistic Regression: Predicting if something belongs to a category (like predicting if an email is spam).
- Decision Trees: Making decisions based on a series of questions (like deciding if you should go outside based on the weather).
- Random Forests: A bunch of decision trees working together to make even better predictions.

Training Process:

Training a model with PostgresML is like teaching a dog a new trick, but with SQL code! You use special SQL commands to tell PostgresML what data to use and what kind of model to create. It looks something like this:

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-- Example: Training a linear regression model
    SELECT pgml.train(
      'my_model',  -- Name of your model
      'SELECT feature1, feature2 FROM my_table', -- Data to use for training
      'target_column', -- What you want to predict
      'linear_regression' -- Type of model
    );
    

PostgresML then uses this information to build a model that can make predictions.

Model Deployment:

Once you’ve trained your model, you can use it to make predictions with more SQL code! It’s like asking your trained dog to perform the trick you taught it.

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-- Example: Making a prediction using the trained model
    SELECT pgml.predict(
      'my_model', -- Name of your model
      'SELECT new_feature1, new_feature2' -- New data to make a prediction on
    );
    

PostgresML will then use the model to predict the outcome based on the new data.

Use Cases:
PostgresML can be used for all sorts of cool AI applications, such as:
- Fraud Detection: Predicting if a transaction is likely to be fraudulent.
- Customer Churn Prediction: Predicting which customers are likely to stop using a service.
- Predictive Maintenance: Predicting when a machine is likely to break down so you can fix it before it does.
Integration with EDB Postgres AI:
EDB Postgres AI is like a super-powered version of PostgreSQL that’s designed for AI. PostgresML could work with EDB Postgres AI to make things even easier. For example, EDB Postgres AI might provide tools to help you choose the best machine learning algorithm for your data, or help you understand the results of your models. They could work together to give you a complete AI platform inside your database.
Relevance to Junior AI Model Trainers:
If you’re a Junior AI Model Trainer, PostgresML is a fantastic tool to learn and experiment with. It lets you try out different machine learning models without needing to be a database expert or set up complicated infrastructure. You can quickly build and deploy models using SQL, making it a great way to get hands-on experience with AI! You don’t need to be a super programmer to get started.

IV. Plugin 3: plpython/pljava/plperl (Procedural Languages for AI Logic)

Sometimes, you need to do things with your data that are too complicated for regular SQL. That’s where procedural languages come in! Think of them as special tools that let you write code in languages like Python, Java, or Perl inside your PostgreSQL database.

What are procedural languages in PostgreSQL?
PostgreSQL lets you use different languages to create your own functions. These are called procedural languages. Three popular ones are:
- plpython: This lets you write functions using the Python language inside your database. Python is great for many AI tasks.
- pljava: This lets you write functions using the Java language inside your database. Java is often used for big, complex programs.
- plperl: This lets you write functions using the Perl language inside your database. Perl is good at working with text.
So, plpython, pljava, and plperl are all ways to add extra programming power to your PostgreSQL database. They let you create custom functions using Python, Java, and Perl, respectively, to do things SQL can’t easily handle.
Why are they important for AI?
These languages are important because they give you more flexibility. You can do almost anything you want with your data. They let you use powerful AI tools and libraries directly inside your database. This means you don’t have to move data around as much, which can save time. If there is an AI library written in Python, for example, plpython lets you use it directly in your Postgres database!
Use Cases for plpython:
plpython is super useful for AI! Here are some examples:
- Data pre-processing: Cleaning up your data before you train an AI model. For example, you can use Python to remove errors or fill in missing values.
- Feature engineering: Creating new, helpful information from your existing data. For example, you could combine two columns to make a new, more useful column.
- Integration with AI libraries: Using powerful Python AI tools like scikit-learn (for machine learning) or TensorFlow (for deep learning) directly within your database. Imagine training a machine learning model directly from your Postgres data using scikit-learn within plpython!
Use Cases for pljava:
pljava lets you use Java code inside PostgreSQL. This is helpful if:
- You already have AI tools written in Java.
- You need to work with big, complex Java-based AI systems.
For example, if you have a Java library that can analyze images, you can use pljava to use that library directly on images stored in your PostgreSQL database.
Use Cases for plperl:
plperl is good at working with text. You can use it for:
- Text processing: Cleaning and preparing text data for AI models. For example, removing punctuation or converting all letters to lowercase.
- Data manipulation: Changing data from one format to another.
- Other AI-related tasks: Anything where you need to work with text data inside your database.
Security Considerations:
Using these languages can be risky if you’re not careful. Someone could write code that harms your database. Here are some tips:
- Only let trusted people write code in these languages.
- Carefully check the code before you use it.
- Limit what these languages can do inside the database.
Performance Implications:
Using these languages can sometimes be slower than using regular SQL. This is because PostgreSQL has to switch between SQL and the other language (Python, Java, or Perl).
Here are some ways to make things faster:
- Only use these languages when you really need them.
- Write efficient code.
- Move complex calculations outside the database if possible.
Relevance to Junior AI Model Trainers:
As a Junior AI Model Trainer, these procedural languages can be super helpful! They let you:
- Customize your AI pipelines: Create your own special steps for preparing data and training models.
- Integrate with different AI tools: Use the AI tools you like best, even if they’re not directly supported by PostgreSQL.
- Experiment with new AI techniques: Try out new ideas without having to move data around all the time.
By understanding and using plpython, pljava, and plperl, you can become a more powerful and flexible AI Model Trainer!

V. Plugin 4: pg_cron (Scheduled Tasks for AI Pipelines)

Imagine you have a robot that needs to do a task every day at the same time. pg_cron is like setting a timer for your PostgreSQL database to do things automatically!

What is pg_cron?
pg_cron is a PostgreSQL extension. Think of it as a special add-on that tells your database to run specific tasks at set times. It’s like a built-in scheduler. You can tell it to run a command every hour, every day, or even on specific days of the week.
Why is it important for AI?
AI pipelines are like assembly lines for building and using AI models. They often involve steps that need to happen regularly, such as:
- Getting new data: Like downloading the latest information to train your AI.
- Training models: Teaching your AI model with the new data.
- Deploying models: Making your trained AI model available for use.
pg_cron helps automate these steps. Instead of manually running these tasks, pg_cron does it for you on a schedule. This keeps your AI working smoothly!
Scheduling Syntax:
You tell pg_cron what to do and when using a special language. It looks like this:

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SELECT cron.schedule('my_daily_job', '0 0 * * *', 'SELECT my_function();');

Let's break it down:

*   `cron.schedule`:  This tells PostgreSQL you want to schedule a task.
*   `'my_daily_job'`: This is the name you give to your scheduled task.
*   `'0 0 * * *'`: This is the schedule itself. It's like a secret code! In this example, it means "run at 00:00 (midnight) every day".  The parts are (minute, hour, day of month, month, day of week).  `*` means "every".
*   `'SELECT my_function();'`: This is the command you want to run.  In this case, it runs a function called `my_function`.

Use Cases:
Here are some examples of how pg_cron can be used in AI:
- Retraining models: Automatically retrain your AI model every week with the latest data.
- Running batch inference: Process a large amount of data and make predictions using your AI model every night.
- Generating reports: Create a summary of your AI model’s performance every month.
Monitoring and Logging:
It’s important to make sure your scheduled tasks are running correctly. pg_cron keeps a log of everything it does. You can check this log to see if any tasks failed or had problems. This helps you fix any issues quickly.
Integration with Other Plugins:
pg_cron works really well with other plugins. For example:
- You can use pg_cron with PostgresML to automatically retrain your machine learning models.
- You can use pg_cron with plpython to run Python scripts that process data for your AI.
Alternatives:
While pg_cron is great, there are other ways to schedule tasks:
- External Task Schedulers (like cron on your computer): These run outside of PostgreSQL. They can be more powerful, but harder to set up.
- Cloud-based Services (like AWS Lambda or Google Cloud Functions): These are services in the cloud that can run code on a schedule. They are easy to use, but can cost money.
pg_cron is often a good choice because it’s simple to use and runs right inside your database.
Relevance to Junior AI Model Trainers:
As a Junior AI Model Trainer, pg_cron can be a lifesaver! It lets you automate the boring, repetitive tasks in your AI pipeline. This frees up your time to focus on the fun stuff, like improving your models and experimenting with new ideas! You can set up pg_cron to automatically prepare data, train models, and test their performance, letting you focus on making your AI smarter.

VI. Plugin 5: TimescaleDB (Time-Series Data for AI)

Imagine you’re tracking the temperature of a room every minute. That’s time-series data – information that changes over time. TimescaleDB helps PostgreSQL handle this kind of data super efficiently, which is great for AI!

What is TimescaleDB?
TimescaleDB is like a supercharger for PostgreSQL. It’s an extension, meaning it’s an add-on that makes PostgreSQL even better at handling time-series data. It’s designed to store and retrieve data that’s constantly changing over time, like sensor readings, stock prices, or website traffic. Think of it as a specialized tool for data that has a timestamp.
Why is it important for AI?
Many AI applications rely on time-series data. For example, AI can predict when a machine might break down (predictive maintenance) by looking at sensor data over time. It can also detect unusual activity in financial transactions (anomaly detection) by analyzing patterns over time. TimescaleDB makes it much faster and easier to work with this data, which means AI models can be built and trained more effectively. Without efficient time-series storage and querying, these AI applications would be much slower and less accurate.
Key Features:
TimescaleDB has some special features that make it perfect for time-series data:
- Automatic Data Partitioning: TimescaleDB automatically breaks your data into smaller chunks called “chunks”. This makes it easier and faster to find the specific data you need. It’s like organizing your toys into different boxes instead of having one giant pile.
- Continuous Aggregation: It lets you create summaries of your data automatically. For example, you can automatically calculate the average temperature for each hour from minute-by-minute data. This saves you time and makes your queries faster.
- Time-Series-Specific Functions: TimescaleDB includes special functions designed to work with time-series data. These functions make it easier to perform common tasks like calculating moving averages or finding the first and last data points in a series.
Use Cases:
Here are some examples of how TimescaleDB can be used with AI:
- Predicting Equipment Failures: An AI model can learn to predict when a machine will break down by analyzing sensor data (temperature, pressure, vibration) stored in TimescaleDB.
- Detecting Fraudulent Transactions: AI can identify unusual patterns in financial transactions stored in TimescaleDB, helping to detect fraud.
- Forecasting Energy Demand: AI can predict how much energy will be needed in the future by analyzing historical energy consumption data stored in TimescaleDB.
- Monitoring Website Performance: Track website traffic, response times, and other metrics over time to identify performance issues and optimize the user experience. AI models can then be trained to predict future traffic patterns and potential bottlenecks.
Integration with AI Frameworks:
TimescaleDB works well with popular AI frameworks like Prophet (for forecasting) and TensorFlow (for building machine learning models). You can easily pull data from TimescaleDB into these frameworks to train your AI models. For example, you can use a SQL query to select the relevant time-series data from TimescaleDB and then feed it into a Prophet model for forecasting.
Performance Considerations:
TimescaleDB is generally faster than regular PostgreSQL tables for time-series data because of its special features. However, performance can still be affected by things like:
- Data Volume: The more data you have, the longer queries can take.
- Query Complexity: Complex queries with lots of calculations will take longer.
- Hardware Resources: Having enough RAM and CPU power is important for good performance.
Alternatives for Time-Series:
While TimescaleDB is a great option, there are other time-series databases you could use:
- InfluxDB: A database specifically designed for time-series data. It is very fast for writes, but might be less flexible for complex queries than TimescaleDB.
- Prometheus: Commonly used for monitoring systems and applications. Great for real-time monitoring, but less suitable for long-term data storage and complex analysis.
- Regular PostgreSQL Tables: You can store time-series data in regular PostgreSQL tables, but it won’t be as efficient as using TimescaleDB. Queries will be slower, and you’ll have to manage data partitioning yourself.
Relevance to Junior AI Model Trainers:
As a Junior AI Model Trainer, TimescaleDB can help you build better AI models by making it easier to work with time-series data. You can use TimescaleDB to:
- Prepare data for training: Quickly extract and transform time-series data for your models.
- Build more accurate models: Use the efficient storage and querying capabilities of TimescaleDB to train models on larger datasets.
- Solve real-world problems: Apply your AI skills to a wider range of problems involving time-series data, such as predicting equipment failures or detecting fraud.

VII. Conclusion: The Future of PostgreSQL and AI

We’ve explored some cool tools that let PostgreSQL work even better with AI! Let’s quickly recap what we learned.

Recap of the Plugins:
- pgvector: Helps find similar things using vectors, like finding similar images or documents.
- PostgresML: Lets you build and use machine learning models right inside your database.
- plpython/pljava/plperl: Allow you to use different programming languages (Python, Java, Perl) to create special functions inside PostgreSQL for AI tasks.
- pg_cron: Schedules tasks to run automatically, which is useful for things like retraining AI models or updating data.
- TimescaleDB: Makes PostgreSQL super good at handling data that changes over time, which is important for many AI applications.
The Growing Importance of PostgreSQL in AI:
PostgreSQL is becoming a popular choice for AI because it’s free to use (open-source), you can add lots of features to it (extensible), and it can handle lots of data (scalable). More and more people are using it for their AI projects!
The Role of Plugins in Bridging the Gap:
These plugins are like bridges connecting PostgreSQL to the world of AI. They give you the tools you need to build smart applications that use your data effectively.
Call to Action:
Now it’s your turn! Try out these plugins. Read the documentation (the instructions), look for tutorials (lessons), and join online communities (groups of people) to learn more. Experiment and see what you can build! For example, search online for “pgvector tutorial” or “PostgresML documentation.”
Future Trends:
In the future, we might see even more plugins that do specific AI jobs. Maybe there will be a plugin just for image recognition, or one for understanding human language. AI might even become a built-in part of PostgreSQL itself! Cloud platforms will likely offer PostgreSQL with these AI extensions pre-installed, making it even easier to use.
Open Source and Community:
PostgreSQL is made by a big group of people working together (the open-source community). You can help too! If you find a bug (a mistake), tell someone. If you have an idea for a new feature, share it! By working together, we can make PostgreSQL even better.
Final Thoughts for Junior AI Model Trainers:
Knowing about these plugins can really help you as a Junior AI Model Trainer. You’ll be able to build better AI models, manage your data more effectively, and be a valuable member of your team. Understanding how these plugins work will allow you to build more robust and scalable solutions.
Encourage Further Exploration:
Keep learning! The world of PostgreSQL and AI is always changing. Read articles, watch videos, and try new things. The more you learn, the more successful you’ll be!

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