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What is pgvector?

Postgres has introduced open-source capabilities for vector similarity searches. This advancement enables users to perform both precise and approximate nearest neighbor searches by using various metrics, including L2 distance, inner product, and cosine distance. Furthermore, this new feature significantly improves the database's efficiency in handling and analyzing intricate data sets, making it a valuable tool for data-driven applications. As a result, developers can leverage these capabilities to enhance their data processing workflows.

What is Deeplake?

Deeplake is a GPU-native database and multimodal AI data runtime from Activeloop that helps developers build faster, more capable production AI agents. It is designed for the agentic era, where AI systems do not just query data occasionally but continuously create, retrieve, reason over, and update data during autonomous workflows. Deeplake brings together serverless Postgres, vector search, multimodal data lake functionality, analytical query performance, and GPU acceleration in one platform. The database is built to reduce the bottlenecks caused when AI models run on GPUs but data retrieval still depends on CPU-based systems and repeated data transfers. For agentic loops, Deeplake acts as a high-speed memory layer that helps agents retrieve context and act across rapid cycles. For physical AI, it supports data from robots, sensors, videos, 3D scans, and model artifacts in one searchable system. For generative media, it indexes content by meaning so teams can find images, video, audio, and other assets without depending only on manual folders or tags. Deeplake also supports vector database and RAG workflows, helping teams build applications that need scalable retrieval and context management. Its architecture is positioned around familiar database concepts, including Postgres-style access, while adding AI-optimized storage and GPU-speed execution. Organizations can deploy Deeplake in VPC environments and use it as part of secure enterprise AI infrastructure. With open-source momentum, SOC 2 Type II certification, multimodal support, and GPU-native performance, Deeplake gives AI teams a modern data foundation for agents, robotics, retrieval, training, and media intelligence.

Media

Media

Integrations Supported

Activeloop
Amazon SageMaker
Amazon Web Services (AWS)
Apify
Cake AI
ChatGPT
Google Cloud Platform
Jupyter Notebook
LangChain
Langtrace
MyDBA.dev
Nebius Token Factory
OpenAI
PostgreSQL
PyTorch
Supabase
Superexpert.AI
TensorFlow

Integrations Supported

Activeloop
Amazon SageMaker
Amazon Web Services (AWS)
Apify
Cake AI
ChatGPT
Google Cloud Platform
Jupyter Notebook
LangChain
Langtrace
MyDBA.dev
Nebius Token Factory
OpenAI
PostgreSQL
PyTorch
Supabase
Superexpert.AI
TensorFlow

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

$0
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

pgvector

Company Website

github.com/pgvector/pgvector

Company Facts

Organization Name

Activeloop

Date Founded

2018

Company Location

United States

Company Website

deeplake.ai/

Categories and Features

Categories and Features

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