PostgresML is a complete MLOps platform built inside PostgreSQL. Our operating principle is:

Move models to the database, rather than constantly moving data to the models.

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 continuously moving data to the models.

AI engine

PostgresML allows you to take advantage of the fundamental relationship between data and models, by extending the database with the following capabilities:

  • Model Serving - GPU accelerated inference engine for interactive applications, with no additional networking latency or reliability costs
  • Model Store - Access to open-source models including state of the art LLMs from Hugging Face, and track changes in performance between versions
  • Model Training - Train models with your application data using more than 50 algorithms for regression, classification or clustering tasks; fine tune pre-trained models like Llama and BERT to improve performance
  • Feature Store - Scalable access to model inputs, including vector, text, categorical, and numeric data: vector database, text search, knowledge graph and application data all in one low-latency system
Machine Learning Infrastructure (2.0) by a16z

PostgresML handles all of the functions described by a16z

These capabilities are primarily provided by two open-source software projects, that may be used independently, but are designed to be used together with the rest of the Postgres ecosystem:

  • pgml - an open source extension for PostgreSQL. It adds support for GPUs and the latest ML & AI algorithms inside the database with a SQL API and no additional infrastructure, networking latency, or reliability costs.
  • PgCat - an open source connection pooler for PostgreSQL. It abstracts the scalability and reliability concerns of managing a distributed cluster of Postgres databases. Client applications connect only to the pooler, which handles load balancing, sharding, and failover, outside of any single database server.
PostgresML architectural diagram

To learn more about how we designed PostgresML, take a look at our architecture overview.

Client SDK

The PostgresML team also provides native language SDKs which implement best practices for common ML & AI applications. The JavaScript and Python SDKs are generated from the a core Rust library, which provides a uniform API, correctness and efficiency across all environments.

While using the SDK is completely optional, SDK clients can perform advanced machine learning tasks in a single SQL request, without having to transfer additional data, models, hardware or dependencies to the client application.

Some of the use cases include:

  • Chat with streaming responses from state-of-the-art open source LLMs
  • Semantic search with keywords and embeddings
  • RAG in a single request without using any third-party services
  • Text translation between hundreds of languages
  • Text summarization to distill complex documents
  • Forecasting time series data for key metrics with and metadata
  • Anomaly detection using application data

Our mission

PostgresML strives to provide access to open source AI for everyone. We are continuously developping PostgresML to keep up with the rapidly evolving use cases for ML & AI, but we remain committed to never breaking user facing APIs. We welcome contributions to our open source code and documentation from the community.

Managed cloud

While our extension and pooler are open source, we also offer a managed cloud database service for production deployments of PostgresML. You can sign up for an account and get a free Serverless database in seconds.