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Postgres + GPUs for ML/AI applications.
| Documentation | Blog | Discord |
Why do ML/AI in Postgres?
Data for ML & AI systems is inherently larger and more dynamic than the models. It's more efficient, manageable and reliable to move models to the database, rather than constantly moving data to the models.
- Getting started
- PostgresML Cloud
- Self-hosted
- Ecosystem
- Large Language Models
- Hugging Face
- OpenAI and Other Providers
- RAG
- Chunk
- Embed
- Rank
- Transform
- Machine Learning
Architecture
PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database. By integrating these functionalities, PostgresML eliminates the need for separate systems and data transfers, enabling you to perform ML operations directly on your data where it resides.
Features at a glance
- 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 and use state-of-the-art LLMs from Hugging Face
- 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: Support for 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
- Seamless Integration: Works with existing PostgreSQL tools and client libraries
Getting started
The only prerequisites for using PostgresML is a Postgres database with our open-source pgml extension installed.
PostgresML Cloud
Our serverless cloud is the easiest and recommend way to get started.
Sign up for a free PostgresML account. You'll get a free database in seconds, with access to GPUs and state of the art LLMs.
Self-hosted
If you don't want to use our cloud you can self host it.
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 postgresmlFor more details, take a look at our Quick Start with Docker documentation.
Ecosystem
Don't lose this
Three weeks from now, you'll want Postgresml again. Will you remember where to find it?
Save it to your library and the next time you need Postgresml, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.
⚡ Pro tip for geeks: add a-gnt 🤵🏻♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.
a-gnt's Take
Our honest review
This plugs directly into your AI and gives it new abilities it didn't have before. Postgres with GPUs for ML/AI apps. Once connected, just ask your AI to use it. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
Tips for getting started
Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.
Your data stays between you and your AI — nothing is shared with us or anyone else.
What's New
Imported from GitHub
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