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.
No features have been listed yet.
No Qdrant videos yet. You could help us improve this page by suggesting one.
Qdrant's answer:
Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.
Qdrant's answer:
Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.
Qdrant's answer:
Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.
Vultr might be a bit more popular than Qdrant. We know about 58 links to it since March 2021 and only 40 links to Qdrant. 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.
Vultr.com pick the $5 monthly plan and enable backup. Source: 6 months ago
Most reputable places out there will allow everything above (and match your budget), such as Linode, or Vultr (there are others). Source: 8 months ago
I recommend Hetzner or Vultr as a VPS provider as they're cheap and I/my friends have had good experiences with them. Source: 10 months ago
Am I allowed to use VPS servers from vultr to use honeygain. Source: about 1 year ago
Linode (Mumbai) and DigitalOcean (Bangalore) each have a single DC in India, and Vultr has 3 (Mumbai, Bangalore, Delhi). Source: about 1 year ago
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 3 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
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.Sign up to Linode through SaaSHub and get a $100 in credit!
Weaviate - Welcome to Weaviate
Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs