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Apache 2.0

Easily deploy, train and manage any AI models and APIs on your datastore: from LLMs, public APIs to highly custom machine learning models, use-cases and workflows.

from superduperdb import superduper 
from superduperdb.mongodb import Collection 
import pymongo 
my_db = superduper(pymongo.MongoClient().my_db) 
r = my_db.execute(
        .like({'txt': 'similar to this'})

Build AI applications on top of your datastore

A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately.

Work with any ML/AI frameworks and APIs

Integrate and combine models from Sklearn, PyTorch, HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows.

from superduperdb import superduper 
from transformers import pipeline
model = superduper(pipeline('sentiment-analysis')) 
    select=Collection('docs').find({'rating': {'$exists': 1}})
)    # make predictions on unseen data

What you can do with SuperDuperDB:

Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.

Train models on your data in your datastore simply by querying without additional ingestion and pre-processing.

Integrate AI APIs (such as OpenAI) to work together with other models on your data effortlessly.

Search your data with vector-search, including model management and serving.

Deploy & Compute
Train Models
Integrate APIs
Vector Search
from superduperdb import superduper 
from sentence_transformers import SentenceTransformer

model = superduper(


Example Use-Cases

Check out AI use cases and applications that we have already implemented using open-source models and public APIs!

SuperDuperDB transforms your datastore into:


which includes a model repository & registry as well as computation of outputs


allowing you to easily train and fine-tune your models simply by querying your data(store)


in which the model outputs are stored in desired formats and types, instantly available in the datastore


enabling straightforward generation of vector embeddings on your data with your choice of models

Data LayerMongoDB (Atlas), S3, PostgreSQL, MySQL, DuckDB, SQLite, BigQuery, Snowflake
Self-hosted ModelsLLaMA, Dolly, Clip, Stable Diffusion, and more + custom.
AI APIsOpenAI, Cohere AI, and more.
AI framework & LibrariesPytorch, Tensorflow, Sklearn, HuggingFace, Keras, and more.
ML ToolingWeights & Biases, MLFlow, Tensorboard, and more.

Who is SuperDuperDB for?

Full Stack Developers

who want to implement next gen AI into their applications without MLOps knowledge required.

Data Scientists

who want to develop and train AI models using their favourite tools, with minimum overhead.

ML Engineers

who want want a single scalable setup that supports both local, on-prem and cloud deployment.

Why choose SuperDuperDB?


with a single scalable deployment of all AI models and APIs


keeping the deployment always up-to-date


that can handle even the most complex AI use-cases without requiring MLOps knowledge


on your data is massively simplified

A developer experience tailored to AI

Work natively in Python

Apply AI with just a few simple commands, no MLOps experience required

Integration with ML/ AI frameworks and APIs

Native support of PyTorch, Sklearn and HuggingFace models as well as models externally hosted behind public APIs.

Complementary to the existing ecosystem

Based around flexible notions of data types, data stores, data retrieval and AI models, SuperDuperDB is super-easy to extend and customize.

#third code card
from superduperdb.ext.pillow import pil_image
from superduperdb.ext.torch import Tensor
    'y': pil_image(y),
    'x': Tensor(x, shape=x.shape)
#second code card
from superduperdb.ext.torch import TorchModel
from superduperdb.ext.transformers import TransformersModel
from superduperdb.ext.sklearn import SklearnModel
from superduperdb.ext.openai import OpenAIEmbedding
m1 = TorchModel('m1', object=MyTorchModule, preprocess=p1)
m2 = TransformersModel('m2', object=MyTorchModule, preprocess=p2)
m3 = SklearnModel('m3', object=MyTorchModule, preprocess=p3)
m4 = OpneAIEmbedding('m4', preprocess=p4)
#first code card
In [1]: from superduperdb import superduper 
   ...: from superduperdb.mongodb import Collection 
   ...: import pymongo 

In [2]: my_db = superduper(pymongo.MongoClient().my_db) 

In [3]: r = my_db.execute(Collection('docs').find_one())


Get started with SuperDuperDB

SuperDuperDB comes pre-loaded with all you need to supercharge your data with AI. It’s really that simple!