postgresml/postgresml
Postgres with GPUs for ML/AI apps.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
Postgres + GPUs for ML/AI applications. 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. 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. The only prerequisites for using PostgresML is a Postgres database with our open-source pgml extension installed. 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. If you don't want to use our…
Read the full README ↗ · Preview checked 2026-10-28T09:30:59.891Z
Inside the original README — Document outline
- Architecture
- Features at a glance
- Getting started
- PostgresML Cloud
- Self-hosted
- Ecosystem
- Large language models
- Hugging Face
- OpenAI and other providers
- RAG
- Chunk
- Embed
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What this repository does
Postgres with GPUs for ML/AI apps.
Repository facts
- Owner
- postgresml
- Primary language
- Rust
- Stars
- 6,826
- Forks
- 364
- Open issues + pull requests
- 148
- License
- MIT
- Archived
- No
- Default branch
- master
- Created
- 2022-04-11T05:33:26.000Z
- Last push
- 2025-07-01T12:26:02.000Z
Topics and intended use
Owner-supplied topics: ai, ann, approximate-nearest-neighbor-search, artificial-intelligence, classification, clustering, embeddings, forecasting, knn, llm, machine-learning, ml, postgres, rag, regression, sql, vector-database
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Source and freshness
Source: GitHub. Metadata observed 2026-10-31T13:20:02.570Z. Daily imports are snapshots, not real-time monitoring.
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