The platform for production
RAG apps

    Index, filter & rank vectors
    Create embeddings
    Generate real-time, fact-based outputs
Instacart SudoWrite OneSignal OneSignal OneSignal
"Bleeding edge stuff in
a
matter of minutes."
Hasura

Stuck with an AI stack so complicated your app barely runs in prod?🤔

construction
Microservice mayhem

You’re managing a multitude of microservices – a vector database, embedding model, LLMs, and frameworks to glue them all together. 

cognition
Increasing inefficiency

Production outages that won't stop, high-latency UX, ever-increasing dev time, and data-hungry compute with costly vendors. 

mystery
Excessive exposure

Your data is sent through multiple systems. You can’t be sure if it’s secure, stable, compliant or private. 

Architecture makes or breaks your app.
PostgresML radically simplifies it

Old Way and New Way Old Way and New Way

4x Faster

than HuggingFace + Pinecone
for a RAG chatbot

10x faster

than OpenAI for embedding
generation

Save 42%

On vector database cost
compared to Pinecone

Don't take our word for it.

Explore the SDK and test open source models in our hosted database.

What makes PostgresML so powerful

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Index, filter and re-rank vector embeddings
    10x faster vector operations
    Perform fast KNN and ANN search
    Index embeddings with HNSW or IVFFlat 
Learn More arrow_forward
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Generate embeddings
    Choose from state-of-the-art models
    Built-in data preprocessors for splitting and chunking
    Convert text to vector embeddings
Learn More arrow_forward
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Colocate data and compute
    Embed, serve and store all in one process
    Terabytes of data on a single machine
    Built-in data privacy & security 
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Train, tune and deploy
    Regression, classification and clustering
    Fine-tune LLMs on your own data 
    Monitor model deployments over time 
Learn More arrow_forward
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Get the most of LLMs
    Use open-source models (Mistral, LLama, etc.)
    Perform a range of NLP tasks
    Serve with the same infrastructure 
Learn More arrow_forward
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Comprehensive platform
    Multiple deployment options
    Perform several AI & machine learning tasks
    Use SQL or SDKs in JS and Python
feature image
Index, filter and re-rank vector embeddings
    10x faster vector operations
    Perform fast KNN and ANN search
    Index embeddings with HNSW or IVFFlat 
Learn More arrow_forward
feature image
Generate embeddings
    Choose from state-of-the-art models
    Built-in data preprocessors for splitting and chunking
    Convert text to vector embeddings
Learn More arrow_forward
feature image
Colocate data and compute
    Embed, serve and store all in one process
    Terabytes of data on a single machine
    Built-in data privacy & security 
feature image
Train, tune and deploy
    Regression, classification and clustering
    Fine-tune LLMs on your own data 
    Monitor model deployments over time 
Learn More arrow_forward
feature image
Get the most of LLMs
    Use open-source models (Mistral, LLama, etc.)
    Perform a range of NLP tasks
    Serve with the same infrastructure 
Learn More arrow_forward
feature image
Comprehensive platform
    Multiple deployment options
    Perform several AI & machine learning tasks
    Use SQL or SDKs in JS and Python

Better price for performance

Our pricing is based on the models you use. It’s designed to minimize costs and operations. You’ll also save because you can replace many existing tools.

Integrated Libraries

add remove
PyTourch
PyTourch
TensorFlow
TensorFlow
Flax
Flax
SciKit-Learn
SciKit-Learn
Hugging Face
Hugging Face
Llama
Llama
Mistral
Mistral
XGBoost
XGBoost
LightGBM
LightGBM
CatBoost
CatBoost

Models

add remove
Llama
Falcon
OpenAI
Mixtral
Mistral
dbrx-instruct

Languages

add remove
C++
C#
Elixir
Go
Haskell
Java & Scala
Julia
Lua
Node
Perl
PHP
Python
R
Ruby
Rust
Swift

OSS Ecosystem

add remove
Apache Airflow
Apache Airflow
DBT
DBT
DBeaver
DBeaver
Dagster
Dagster
Kafka
Kafka
AWS
AWS
Azure
Azure
Google Cloud
Google Cloud

Work with
what you want

Hear from our community

This is why I’m bullish on @postgresml - devs will always prefer to do things in data stores they already use in production

James yu

James yu

@jamesyu

Great article by PostgresML, running @huggingface models INSIDE @PostgreSQL nice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"

Paul Copplestone

Paul Copplestone

@kiwicopple

Love the fact that @postgresml can run various algorithms to find the optimum one for model creation

RebataurAI

RebataurAI

@rebataur

You can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functions

Dushyant (e/acc)

Dushyant (e/acc)

@DevDminGod

If you want to seamlessly integrate machine learning models into your #PostgreSQL database, use PostgresML.

Khuyen Tran

Khuyen Tran

@KhuyenTran16

💯 there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvector

Martin McFly

Martin McFly

@martinmark

Tons of capability in that Postgres extension. It's an important part of the ML Stack at cloud.tembo.io as well.

Adam Hendel

Adam Hendel

@adamhendel

A game-changer indeed! By integrating ML and AI directly at the database level with @postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.

Pranay Suyash

Pranay Suyash

@pranaysuyash

This is why I’m bullish on @postgresml - devs will always prefer to do things in data stores they already use in production

James yu

James yu

@jamesyu

Great article by PostgresML, running @huggingface models INSIDE @PostgreSQL nice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"

Paul Copplestone

Paul Copplestone

@kiwicopple

Love the fact that @postgresml can run various algorithms to find the optimum one for model creation

RebataurAI

RebataurAI

@rebataur

You can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functions

Dushyant (e/acc)

Dushyant (e/acc)

@DevDminGod

If you want to seamlessly integrate machine learning models into your #PostgreSQL database, use PostgresML.

Khuyen Tran

Khuyen Tran

@KhuyenTran16

💯 there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvector

Martin McFly

Martin McFly

@martinmark

Tons of capability in that Postgres extension. It's an important part of the ML Stack at cloud.tembo.io as well.

Adam Hendel

Adam Hendel

@adamhendel

A game-changer indeed! By integrating ML and AI directly at the database level with @postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.

Pranay Suyash

Pranay Suyash

@pranaysuyash

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project today

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