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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Deeplake is an AI data runtime and GPU database built for teams developing agents, RAG systems, multimodal applications, robotics workflows, and generative media products. It is designed to solve the gap between GPU-powered AI models and CPU-bound data systems by keeping data closer to where AI workloads execute. The platform supports serverless Postgres, vector search, multimodal data storage, analytical workloads, and AI-optimized data lake functionality. Deeplake helps agents remember, retrieve, and act in fast cycles, making it useful for systems that need repeated context retrieval across long-running tasks. It can manage complex data such as video, images, point clouds, sensors, PDFs, audio, embeddings, model weights, and structured records. Developers can use familiar database concepts while gaining support for GPU-speed retrieval and scalable AI data operations. The platform is positioned for production-grade AI use cases where agents may generate databases, query thousands of times, and require faster memory access. Deeplake also supports private deployment patterns, including VPC environments, so organizations can keep sensitive data within their own infrastructure. With open-source adoption, enterprise security credentials, and a focus on agentic workloads, Deeplake helps AI teams build faster and more efficient data systems.

Description

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
ChatGPT
Google Cloud Platform
OpenAI
PyTorch
Activeloop
Amazon SageMaker
Azure Marketplace
ChatGPT Plus
ChatGPT Pro
Cohere
Coral
HoneyHive
Hugging Face
IBM watsonx.data
Java
Jupyter Notebook
LangChain
Milvus
TensorFlow

Integrations

Amazon Web Services (AWS)
ChatGPT
Google Cloud Platform
OpenAI
PyTorch
Activeloop
Amazon SageMaker
Azure Marketplace
ChatGPT Plus
ChatGPT Pro
Cohere
Coral
HoneyHive
Hugging Face
IBM watsonx.data
Java
Jupyter Notebook
LangChain
Milvus
TensorFlow

Pricing Details

$0
Free Trial
Free Version

Pricing Details

$0
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Activeloop

Founded

2018

Country

United States

Website

deeplake.ai/

Vendor Details

Company Name

Zilliz

Founded

2017

Country

United States

Website

zilliz.com

Product Features

Product Features

Database

Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization

Alternatives

Alternatives

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