We've been hard at work improving PostgresML, and thought it was time for an update now that our cloud offering is generally available.

In case you missed our first posts, I built the open-source ML platform at Instacart way back in 2017. I learned a ton, but primarily that it's better to bring your ML workload to the database rather than bringing the data to the codebase. That's why we made PostgresML.

Fundamentally, it enables Postgres to act as a GPU-powered AI application database — where you can both save models and index data. That eliminates the need for the myriad of separate services you have to tie together for your ML workflow. Pgml + pgvector create a complete ML platform (vector DB, model store, inference service, open-source LLMs) all within open-source extensions for PostgreSQL. That takes a lot of the complexity out of your infra, and it's ultimately faster for your users.

We wanted to give folks a way to tinker with it easily, so we've added a few interactive demos to our site.

At the top of the homepage, you can test out a few models right on our site. If you scroll down, you can actually try out our Python, JavaScript, Rust and SQL SDKs locally and for free. Just copy the example code and you can play around with open-source models in your favorite database.

Let us know what you think.