Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.
LiveAgent is a fully-featured omnichannel help desk software that offers an all-in-one help desk solution for businesses of all sizes and types. LiveAgent's core strength is the ability to integrate multiple communication channels such as email, live chat, phone support, social media but also rarely integrated channels like WhatsApp, Instagram and Viber.
LiveAgent boasts the fastest live chat widget on the market and over 180+ additional features, including ticketing, automation, tags, a customer portal and more. LiveAgent's pricing plans are smart - they enable you to pay for only what you use. Save money and time with LiveAgent.
LiveAgent is used by over 15,000 businesses worldwide including Forbes, Yamaha, eSky and Huawei. It has served over 150M end-users worldwide. Join them in providing world-class customer service. Start your free 30-day trial now, no contracts or credit card required.
No Qdrant videos yet. You could help us improve this page by suggesting one.
Qdrant's answer
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
LiveAgent's answer:
LiveAgent stands out with its ultra-fast performance, robust ticketing system, and user-friendly interface. It is a scalable solution equipped with over 180+ features and 200+ integrations, capable of growing as your customer service needs expand.
Qdrant's answer
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
LiveAgent's answer:
LiveAgent offers 24/7 availability, an exceptional 20-second average response time, and extraordinary usability. Suitable for any type of business, its unbeatable value for money makes it a top choice for reliable and efficient customer service.
LiveAgent's answer:
Our primary audience consists of businesses of all sizes seeking to enhance their customer service experience. This includes startups, SMEs, and large corporations across various industries.
LiveAgent's answer:
Born out of the need for better customer interactions, LiveAgent was founded in 2004. Driven by the philosophy 'to treat customers as people, not tickets,' we've grown into a leading customer service solution.
Qdrant's answer
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
LiveAgent's answer:
As a cloud-based solution, LiveAgent employs cutting-edge technologies to ensure a fast, secure, and reliable customer service platform. Our intricate infrastructure guarantees optimal functionality and high performance at all times.
LiveAgent's answer:
Renowned brands like Huawei, Yamaha, BMW, and Oxford University are proud users of LiveAgent, trusting us for world-class customer service.
Based on our record, Qdrant seems to be more popular. It has been mentiond 40 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 5 days ago
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / about 1 month ago
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / about 1 month ago
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / about 2 months ago
I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / 2 months ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Freshdesk - Freshdesk is a cloud-based customer support software that lets you support customers through traditional channels like phone and email, social channels like Facebook and Twitter, and your own branded community
Weaviate - Welcome to Weaviate
Zendesk - Zendesk is a beautiful, lightweight help-desk solution.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
Intercom - Intercom is a customer relationship management and messaging tool for web businesses. Build relationships with users to create loyal customers.