What it is
Activeloop Deep Lake is an open-source database for AI data, written in C++ under Apache-2.0. It presents itself as a tensor database and AI data runtime for agents, combining serverless Postgres with a multimodal datalake for scalable retrieval and training. The project lives in the machine-learning infrastructure ecosystem.
It solves the fragmentation between object storage, vector search, and model training pipelines. Deep Lake stores embeddings, text, audio, images, video, DICOM files, PDFs, and annotations in one place, while keeping data in the user's own cloud. It supports retrieval for large language model applications and dataset management during deep-learning training, with versioning, lineage, and streaming.
Key capabilities
- It stores and searches multimodal data plus vectors, including embeddings, audio, text, videos, images, DICOM files, PDFs, and annotations.
- It provides vector search, with LangChain and LlamaIndex integrations, allowing use as a vector store for retrieval workflows.
- It supports PyTorch and TensorFlow through built-in dataloaders, including dataset shuffling for training.
- It keeps images, audio, and video in native compression while exposing NumPy-like slicing, indexing, iteration, and lazy loading.
- It works with S3, Azure, GCP, Activeloop cloud, local storage, in-memory storage, and S3-compatible systems such as MinIO.
- It includes data versioning and lineage, plus Weights & Biases integration for tracking data during training.
- It offers instant visualization in the Deep Lake app for bounding boxes, masks, and annotations.
Who uses it and how
- The README lists Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, and Oxford as users.
- LLM developers use it as a vector store for retrieval workflows, image similarity search, and agent memory.
- Deep-learning teams use it to manage datasets while training models, including MMDetection and MMSegmentation workflows.
- MLOps practitioners use it to centralize multimodal datasets across clouds, stream training data, and preserve lineage.
- Researchers can start from community datasets such as MNIST, COCO, ImageNet, CIFAR, and GTZAN.
Getting started
Install the package with pip install deeplake, then register in the Deep Lake app to access all features. You can run it against local storage, in-memory storage, S3, Azure, GCP, Activeloop cloud, or S3-compatible storage such as MinIO.
When to use it — and when not to
Deep Lake is useful when a team wants one Apache-2.0 database for multimodal AI data, vector retrieval, and training dataloaders across clouds. It is less suitable when you need a fully self-hosted stack with no external app dependency, because the README says all features require registering in the Deep Lake app. The metadata lists zero contributors, sixty-six open issues, and a zero-year repository age, so maturity and maintenance posture deserve careful review before production adoption.
project readme (upstream, from github) — read inline
Deep Lake: Database for AI
What is Deep Lake?
Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Deep Lake can be used for:
- Storing and searching data plus vectors while building LLM applications
- Managing datasets while training deep learning models
Deep Lake simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, dicom, pdfs, annotations, and more), querying and vector search, data streaming while training models at scale, data versioning and lineage, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in your own cloud and in one place. Deep Lake is used by Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, & Oxford.
Deep Lake includes the following features:
Multi-Cloud Support (S3, GCP, Azure)
Use one API to upload, download, and stream datasets to/from S3, Azure, GCP, Activeloop cloud, local storage, or in-memory storage. Compatible with any S3-compatible storage such as MinIO.
Native Compression with Lazy NumPy-like Indexing
Store images, audio, and videos in their native compression. Slice, index, iterate, and interact with your data like a collection of NumPy arrays in your system's memory. Deep Lake lazily loads data only when needed, e.g., when training a model or running queries.
Dataloaders for Popular Deep Learning Frameworks
Deep Lake comes with built-in dataloaders for Pytorch and TensorFlow. Train your model with a few lines of code - we even take care of dataset shuffling. :)
Integrations with Powerful Tools
Deep Lake has integrations with Langchain and LLamaIndex as a vector store for LLM apps, Weights & Biases for data lineage during model training, MMDetection for training object detection models, and MMSegmentation for training semantic segmentation models.
100+ most-popular image, video, and audio datasets available in seconds
Deep Lake community has uploaded 100+ image, video and audio datasets like MNIST, COCO, ImageNet, CIFAR, GTZAN and others.
Deep Lake offers integrations with other tools in order to streamline your deep learning workflows. Current integrations include:
Getting started guides, examples, tutorials, API reference, and other useful information can be found on our documentation page.
Deep Lake users can access and visualize a variety of popular datasets through a free integration with Deep Lake's App. Universities can get up to 1TB of data storage and 100,000 monthly queries on the Tensor Database for free per month. Chat in on our website: to claim the access!
