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What is Weaviate?

Weaviate is an open-source, AI-native database that helps organizations build and ship AI applications on a single, scalable foundation. It stores data objects alongside the vector embeddings produced by your chosen machine learning models and scales smoothly to billions of records. Teams can supply their own vectors or use Weaviate's built-in vectorization, then query their data through vector, keyword, and hybrid search to surface the most relevant results, even with complex filters. By integrating with leading large language models, Weaviate makes it straightforward to build retrieval-augmented generation, grounded question answering, and intelligent search over proprietary data. Beyond core retrieval, Weaviate offers a growing platform: the Query Agent converts natural-language questions into precise, cited queries; Engram provides managed memory that lets AI agents retain context over time; and Weaviate Embeddings handles vectorization as a managed service. Organizations can self-host under an open-source license or adopt fully managed Weaviate Cloud on AWS, GCP, or Azure, with SOC 2 Type II compliance, multi-tenancy, replication, and role-based access control. From semantic search and recommendations to agentic automation, Weaviate turns business data into AI-powered products.

What is Qdrant?

Qdrant operates as an advanced vector similarity engine and database, providing an API service that allows users to locate the nearest high-dimensional vectors efficiently. By leveraging Qdrant, individuals can convert embeddings or neural network encoders into robust applications aimed at matching, searching, recommending, and much more. It also includes an OpenAPI v3 specification, which streamlines the creation of client libraries across nearly all programming languages, and it features pre-built clients for Python and other languages, equipped with additional functionalities. A key highlight of Qdrant is its unique custom version of the HNSW algorithm for Approximate Nearest Neighbor Search, which ensures rapid search capabilities while permitting the use of search filters without compromising result quality. Additionally, Qdrant enables the attachment of extra payload data to vectors, allowing not just storage but also filtration of search results based on the contained payload values. This functionality significantly boosts the flexibility of search operations, proving essential for developers and data scientists. Its capacity to handle complex data queries further cements Qdrant's status as a powerful resource in the realm of data management.

Media

Media

Integrations Supported

Azure Marketplace
Agno
Boomi
Contextual AI
DSPy
Firecrawl
GitHub
Grok
IBM API Connect
Mem0
Ollama

Integrations Supported

Azure Marketplace
Airtool
Cognee
Database Mart
Dropstone
Elestio
IBM watsonx.data
LLMWare.ai
NLWeb
Workers by Delos

API Availability

Has API

API Availability

Pricing Information

Free
Open source (free); free Weaviate Cloud tier; paid Cloud plans from $45/mo.
Free Version

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub
Webinars
On-Site Training

Company Facts

Organization Name

Weaviate

Date Founded

2019

Company Location

The Netherlands

Company Website

weaviate.io

Company Facts

Organization Name

Qdrant

Date Founded

2021

Company Location

Germany

Company Website

qdrant.tech/

Categories and Features

AI Memory Layers

Not specified

Context Engineering

Not specified

Vector Databases

Not specified

Categories and Features

AI Memory Layers

Not specified

Context Engineering

Not specified

eCommerce Search

Not specified

Neural Search

Not specified

Site Search

Not specified

Vector Databases

Not specified

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