postgresml

postgresml

PostgresML is an open-source platform that integrates machine learning and vector search directly inside PostgreSQL, enabling embeddings, vector indexing (HNSW/IVFFlat), model inference, and model training on GPU-backed Postgres deployments with SDKs for Python, JavaScript and SQL.

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Data reviewed Sep 7, 2026

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Quick Overview

Best for: AI Agents

What it does

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Pricing snapshot

Pricing available from See pricing page

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PostgresML is an open-source platform and cloud offering that integrates machine learning capabilities directly into PostgreSQL. It colocates vectors, models, and application data in a GPU-backed database so teams can index, search, and run inference with fewer moving parts. The platform supports embeddings, ANN/KNN search with HNSW and IVFFlat indexes, LLM inference and fine-tuning, supervised learning (regression, classification, clustering), and SDKs for Python and JavaScript alongside SQL examples.

PostgresML targets engineering and production use cases where teams prefer to keep ML workflows inside the database to reduce infrastructure complexity, improve performance, and limit data exposure across multiple vendors. The project is offered as open-source software and as PostgresML Cloud with options like VPC deployments and free credits for getting started.

MLOps platform as a PostgreSQL extension for building ML models inside the database.

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Key Features

Vector indexing and search

Index, filter and re-rank vector embeddings with support for HNSW and IVFFlat, enabling fast KNN and ANN search inside Postgres.

Embedding generation

Generate embeddings using state-of-the-art open-source models and built-in data preprocessors for splitting and chunking text.

Colocated data and compute

Embed, serve and store data in the same GPU-backed Postgres process to reduce cross-vendor exposure and simplify architecture.

Model training and tuning

Train, tune and deploy models for regression, classification, clustering, and fine-tune LLMs on your own data; monitor model deployments over time.

LLM inference in SQL

Run LLM inference and grounded answers using SQL, and serve many NLP tasks with the same infrastructure.

SDKs and examples

Client libraries and examples for Python, JavaScript, and SQL to run hosted open-source models and migrate to self-hosted clusters.

Integrated ecosystem

Integration with ML libraries, frameworks and models (PyTorch, TensorFlow, Hugging Face, Llama, Mistral, etc.) and support for multiple programming languages and OSS tools.

Pricing

Free Tier Available

Get started with $100 in free credits (promotion mentioned on the site)

PostgresML Cloud

See pricing page
  • Pay for models and compute you use
  • Fewer separate bills for vector search, embeddings, and inference

Use Cases

Retrieval-Augmented Generation (RAG)

Build RAG pipelines by storing vectors and application data together, performing fast vector search and using LLMs for grounded answers in SQL.

Semantic Search and Ranking

Index and re-rank vector embeddings to power semantic search experiences and improve relevance with ANN/KNN algorithms.

Chatbots and Conversational AI

Serve chatbots with embedded data and LLM inference directly from the database to simplify stack and reduce latency.

Supervised Machine Learning

Train and deploy regression, classification and clustering models within the same Postgres-based infrastructure.

Embedding pipelines and vector databases

Generate and store embeddings in-database and perform large-scale vector operations on terabytes of data on a single machine.

Integrations

PyTorch / TensorFlow / Flax

Support for major ML frameworks for model training and inference.

Hugging Face

Run hosted open-source models and use Hugging Face models inside PostgresML.

Llama / Mistral / Mixtral / Falcon / OpenAI

Prebuilt model support and examples for a variety of LLMs and embedding models.

SciKit-Learn / XGBoost / LightGBM / CatBoost

Classic ML libraries supported for supervised learning workflows.

Languages & OSS tooling

Client and tooling integrations across many languages and OSS projects (Python, Node, Java, Rust, Airflow, DBT, Kafka, etc.)

Benefits

Simplified architecture by colocating vectors, models and application data in a single Postgres deployment
Performance and cost advantages vs multi-vendor stacks (claims include 4x faster for RAG vs HuggingFace+Pinecone, 10x faster for embeddings vs OpenAI, and 42% savings on vector DB cost vs Pinecone)
Built-in privacy and security from reduced data movement across vendors
Supports production workflows with SDKs, SQL examples, and cloud/VPC deployment options

Limitations

Loading non-cached models may take a few moments (site notes that non-cached models can be slow to load).
GPU-backed deployments are required for full performance of model inference and training; some features imply GPU infrastructure.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Read the documentation: follow setup guides, SDK references, and SQL examples in the Docs.
  2. 2 Try hosted open-source models using the provided Python, JavaScript, or SQL examples to evaluate performance before deploying.
  3. 3 Use PostgresML Cloud or deploy PostgresML on your own GPU-backed cluster; PostgresML advertises a $100 free credit promotion to get started.

Support

docs

Setup guides, SDK references, and SQL examples are available in the documentation.

community

Active community on Discord; GitHub repository and blog for releases and tutorials.

contact

Contact page linked from the site for sales or support inquiries.

API

Available: Yes
Documentation:

Docs include SDK references and SQL examples for Python, JavaScript and SQL.

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