The platform for production RAG apps
[{“translation_text”:”Bienvenue à l'avenir!”}]
"Bleeding edge stuff in
a
matter of minutes."
Stuck with an AI stack so complicated your app barely runs in prod?🤔
Microservice mayhem
You’re managing a multitude of microservices – a vector database, embedding model, LLMs, and frameworks to glue them all together.
Increasing inefficiency
Production outages that won't stop, high-latency UX, ever-increasing dev time, and data-hungry compute with costly vendors.
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
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
Index, filter and re-rank vector embeddings
Generate embeddings
Colocate data and compute
Train, tune and deploy
Get the most of LLMs
Comprehensive platform
Index, filter and re-rank vector embeddings
Generate embeddings
Colocate data and compute
Train, tune and deploy
Get the most of LLMs
Comprehensive platform
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 removeModels
add removeLanguages
add removeOSS Ecosystem
add removeWork 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
@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
@kiwicopple
Love the fact that @postgresml can run various algorithms to find the optimum one for model creation
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)
@DevDminGod
If you want to seamlessly integrate machine learning models into your #PostgreSQL database, use PostgresML.
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
@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
@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
@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
@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
@kiwicopple
Love the fact that @postgresml can run various algorithms to find the optimum one for model creation
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)
@DevDminGod
If you want to seamlessly integrate machine learning models into your #PostgreSQL database, use PostgresML.
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
@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
@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
@pranaysuyash
Start your free project today
Sign up and start building for free today.Our API and SDKs make it easy.
Start your free project today
Sign up and start building for free today.
Our API and SDKs make it easy.
PostgresML
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