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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Qdrant serves as a sophisticated vector similarity engine and database, functioning as an API service that enables the search for the closest high-dimensional vectors. By utilizing Qdrant, users can transform embeddings or neural network encoders into comprehensive applications designed for matching, searching, recommending, and far more. It also offers an OpenAPI v3 specification, which facilitates the generation of client libraries in virtually any programming language, along with pre-built clients for Python and other languages that come with enhanced features. One of its standout features is a distinct custom adaptation of the HNSW algorithm used for Approximate Nearest Neighbor Search, which allows for lightning-fast searches while enabling the application of search filters without diminishing the quality of the results. Furthermore, Qdrant supports additional payload data tied to vectors, enabling not only the storage of this payload but also the ability to filter search outcomes based on the values contained within that payload. This capability enhances the overall versatility of search operations, making it an invaluable tool for developers and data scientists alike.

Description

Vald is a powerful and scalable distributed search engine designed for fast approximate nearest neighbor searches of dense vectors. Built on a Cloud-Native architecture, it leverages the rapid ANN Algorithm NGT to efficiently locate neighbors. With features like automatic vector indexing and index backup, Vald can handle searches across billions of feature vectors seamlessly. The platform is user-friendly, packed with features, and offers extensive customization options to meet various needs. Unlike traditional graph systems that require locking during indexing, which can halt operations, Vald employs a distributed index graph, allowing it to maintain functionality even while indexing. Additionally, Vald provides a highly customizable Ingress/Egress filter that integrates smoothly with the gRPC interface. It is designed for horizontal scalability in both memory and CPU, accommodating different workload demands. Notably, Vald also supports automatic backup capabilities using Object Storage or Persistent Volume, ensuring reliable disaster recovery solutions for users. This combination of advanced features and flexibility makes Vald a standout choice for developers and organizations alike.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

IBM watsonx.data Yes 
Activepieces Yes 
Airtool Yes 
Broxi AI Yes 
ChatGPT Plus Yes 
CogniSync Yes 
Database Mart Yes 
Docker No 
Dropstone Yes 
Elestio Yes 
Go No 
Knolli Yes 
Kong AI Gateway Yes 
LLMWare.ai Yes 
Langtrace Yes 
Mimasa AI Yes 
NLWeb Yes 
Node.js No 
Prem AI Yes 
Unremot Yes 

Integrations

IBM watsonx.data Yes 
Activepieces No 
Airtool No 
Broxi AI No 
ChatGPT Plus No 
CogniSync No 
Database Mart No 
Docker Yes 
Dropstone No 
Elestio No 
Go Yes 
Knolli No 
Kong AI Gateway No 
LLMWare.ai No 
Langtrace No 
Mimasa AI No 
NLWeb No 
Node.js Yes 
Prem AI No 
Unremot No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Qdrant

Founded

2021

Country

Germany

Website

qdrant.tech/

Vendor Details

Company Name

Vald

Website

vald.vdaas.org

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