PostgresML is an open-source PostgreSQL extension that integrates machine learning directly into the database, allowing users to perform training and inference on text and tabular data using SQL queries.
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PostgresML
PostgresML is an open-source PostgreSQL extension that integrates machine learning directly into the database, allowing users to perform training and inference on text and tabular data using SQL queries.
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What is PostgresML?
PostgresML is an extension for the PostgreSQL database server that enables end-to-end machine learning inside your database. It allows users to build, train, and deploy ML models directly within PostgreSQL, eliminating the need for data movement between systems. By using SQL queries, users can perform training and inference on both text and tabular data, maximizing data privacy and security while reducing latency and improving performance.
Who will use PostgresML?
- Database Administrators
- Data Scientists
- ML Engineers
- Developers
- Data Analysts
How to use the PostgresML?
- Step1: Install PostgreSQL and PostgresML extension.
- Step2: Prepare your data within the PostgreSQL database.
- Step3: Use SQL queries to create and train machine learning models.
- Step4: Deploy the models for inference using SQL queries.
- Step5: Monitor and evaluate the model performance within the database.
Platform
- web
- mac
- windows
- linux
PostgresML's Core Features & Benefits
The Core Features
- In-database machine learning
- SQL-based model training
- Inference on text and tabular data
- Integrated data security
- No data movement required
The Benefits
- Improved data privacy
- Reduced latency
- Increased performance
- Simplified ML model management
- Seamless integration with PostgreSQL
PostgresML's Main Use Cases & Applications
- Real-time data analysis
- Predictive maintenance
- Customer segmentation
- Fraud detection
- Recommendation systems
PostgresML's Pros & Cons
The Pros
In-database ML and AI operations eliminate the need to move data
Supports GPU acceleration for faster computations
Integration with state-of-the-art large language models via Hugging Face
Built-in Pipeline for Retrieval-Augmented Generation (RAG)
High scalability and support for millions of transactions per second
Wide range of supported ML algorithms and NLP tasks
Open-source with an active community
The Cons
Does not currently support direct integration with some remote LLM providers like OpenAI
Self-hosting might require Docker and PostgreSQL knowledge
Primarily designed for users familiar with PostgreSQL and SQL
PostgresML's Pricing
| Has free plan | YES |
|---|---|
| Free trial details | |
| Pricing model | Free |
| Is credit card required | No |
| Paid from | |
| Has lifetime plan | No |
| Billing frequency |
FAQs of PostgresML
PostgresML Company Information
- Website: https://postgresml.org
- Company Name: PostgresML
- Support Email: [email protected]
- Facebook: NA
- X(Twitter): NA
- YouTube: NA
- Instagram: NA
- Tiktok: NA
- LinkedIn: https://www.linkedin.com/company/postgresml
Analytic of PostgresML
Visit Over Time
Monthly Visits
29
Avg Visit Duration
00:00:00
Page Per Visit
1.01
Bounce Rate
46.31%
Aug 2025 - Oct 2025 All Traffic
Geography
Top 1 Regions
India
100%
Aug 2025 - Oct 2025 Worldwide Desktop Only
Traffic Sources
Search
40.71%
Direct
38.61%
Referrals
14.01%
Social
3.78%
Paid Referrals
2.01%
Mail
0.18%
Aug 2025 - Oct 2025 Desktop Only
Top Keywords
| Keyword | Traffic | Cost Per Click |
|---|---|---|
| ycjobs | 200 | $ -- |
PostgresML Reviews
5/5
PostgresML's Main Competitors and alternatives?
- TensorFlow
- PyTorch
- H2O.ai
- MLflow
- Scikit-learn
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