---
id: deeplake
version: "4.7.1"
license: unclear
license_treatment: unclear
maintenance: active
---

# deeplake — Data Lake for Multi-Modal AI Search

License: unclear · Maintenance: active · Downloads: 194.6K/mo

## What it is and what it does

Deep Lake is a serverless, multi-cloud database optimized for storing and querying AI datasets. It handles raw data types—images, videos, audio, text, embeddings, PDFs, and more—in a single unified storage layer, with built-in vector search for retrieval-augmented generation and LLM applications. Unlike traditional vector databases that store only embeddings plus light metadata, Deep Lake stores the raw data itself in native compression, enabling lazy loading and efficient streaming to training pipelines.

The package integrates with popular ML frameworks and tools to simplify deployment of LLM-based products and deep learning workflows. It supports multi-cloud backends (S3, GCP, Azure, Activeloop cloud, local, in-memory) and includes dataset versioning, visualization, and dataloaders for common frameworks. Core dependencies are numpy, deepframe, and requests.

Use it for:

- Build RAG pipelines and LLM applications with vector search over embeddings and raw documents stored in one place.
- Manage and version large datasets for training deep learning models with efficient streaming and lazy loading.
- Store and search multi-modal datasets (images, videos, text) with instant visualization in the Deep Lake App.
- Deploy serverless vector stores locally or in your own cloud without managing infrastructure.
- Fine-tune large language models using Deep Lake's performant dataloaders for PyTorch or TensorFlow.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Deep Lake is a serverless database for storing, searching, and streaming multi-modal data (images, videos, text, embeddings) alongside vector search capabilities, designed for AI applications and deep learning workflows.

Yes, with conditions. Deep Lake is actively maintained and well-suited for AI/ML workflows requiring multi-modal storage and vector search. However, install it only after verifying the license terms directly in the repository (metadata is unclear) and confirming Python version compatibility for your environment. Medium install friction is acceptable for projects that need its specific combination of raw data storage, vector search, and cloud flexibility.

## Install

pip install deeplake
uv add deeplake
poetry add deeplake

## Installing deeplake

Before you install: Medium install friction with wheels available for multiple Python versions on macOS ARM64 and Linux x86_64/aarch64. Active maintenance with a recent release and 9224 repository stars indicate ongoing development, though Python version requirements are not explicitly specified in package metadata.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata, so users cannot verify licensing terms before installation. This should be resolved by consulting the repository directly.

Quickstart:

pip install deeplake

import deeplake
ds = deeplake.dataset('path/to/dataset')
ds.create_tensor('images')
ds.images.append(image_array)

License terms are unclear in package metadata; verify in the repository before production use. Python version support is unspecified.

Verify before relying:

- Exact Python version support range (requires_python is unspecified in metadata)
- Exact license under which the package is distributed
- Whether the three runtime dependencies (numpy, deepframe, requests) are sufficient or if optional extras are needed for full functionality

## Package facts

- License: not declared (unclear)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 194.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

vector database for llm applications, multi-modal data storage ai, serverless vector store, dataset management deep learning, embedding storage and search, vector-search, multi-modal-ai, rag-pipeline

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