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PostgresML

1
5.0
(0 Reviews)
United States19.17%

PostgresML brings machine learning and AI directly into your PostgreSQL database, enabling faster, more secure, and scalable applications.

Added on: 2025.06.13 · Updated on: 2025.06.13
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PostgresML

PostgresML Product Information

What is PostgresML?

PostgresML is an innovative extension for PostgreSQL that brings machine learning (ML) and artificial intelligence (AI) directly into your database. By combining data storage and ML inference in one place, PostgresML eliminates the need to move data to separate systems, making your applications more efficient, secure, and scalable.

What are the features of PostgresML?

  • In-Database ML/AI: Run machine learning and AI operations directly within PostgreSQL.
  • GPU Acceleration: Leverage GPU power for faster computations and model inference.
  • Large Language Models: Integrate state-of-the-art models from Hugging Face for advanced NLP tasks.
  • RAG Pipeline: Built-in functions for chunking, embedding, ranking, and transforming text.
  • Vector Search: Efficient similarity search using pgvector integration.
  • Diverse ML Algorithms: 47+ classification and regression algorithms available.
  • High Performance: 8-40X faster inference compared to HTTP-based model serving.
  • Scalability: Supports millions of transactions per second and horizontal scaling.
  • NLP Tasks: Wide range of natural language processing capabilities.
  • Security: Enhanced data privacy by keeping models and data together.

What are the use cases of PostgresML?

  • Customer Service Automation: Use AI to analyze and respond to customer inquiries.
  • Personalized Recommendations: Deliver tailored suggestions based on user behavior.
  • Fraud Detection: Identify suspicious transactions with ML models.
  • Content Generation: Automatically generate text, summaries, or translations.
  • Data Analysis: Run classification, regression, and clustering tasks directly on your data.

How to use PostgresML?

  1. Get Started with PostgresML Cloud: Sign up for a free account and get a database with GPU support in seconds.
  2. Self-Host with Docker: Run PostgresML locally using Docker:
    docker run -it -v postgresml_data:/var/lib/postgresql -p 5433:5432 -p 8000:8000 ghcr.io/postgresml/postgresml:2.10.0 sudo -u postgresml psql -d postgresml
    
  3. Use PostgresML Extensions: Install the pgml extension in your PostgreSQL database and start running ML queries.

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PostgresML Related Other Categories

PostgresML Traffic Analysis

💡 Insights

🚀
Super High Traffic
Over 10M monthly visits. A market leader with extremely high user trust.
➡️
Stable
Traffic remains stable, indicating a mature product.
💎
High Stickiness
Low bounce rate (36%) and deep engagement (6.1 pages/visit). Excellent user experience.
🎯
Strong Brand
58% direct traffic. High user loyalty.
  • Monthly Visits

    542.61M

  • Bounce Rate

    36.08%

  • Pages Per Visit

    6.10

  • Visit Duration

    00:06:22

  • Global Rank

    56

  • Country Rank

    82

Visits Over Time

Traffic Sources

Direct58.50%
Search29.08%
Referrals10.54%
Social1.47%
Paid0.36%
Mail0.05%

Top Keywords

1
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524.37KTraffic
4
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500.47KTraffic
5
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462.23KTraffic

Top Regions

RegionPercentage
United States
United States
19.17%
China
China
12.64%
India
India
9.57%
Russia
Russia
5.59%
Germany
Germany
4.20%
Low
High

PostgresML FAQ

What is PostgresML?

Why should I use PostgresML instead of traditional ML approaches?

Can I use my own machine learning models with PostgresML?

Is PostgresML suitable for large-scale applications?

How do I get started with PostgresML?

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