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.
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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.
Nhost might be a bit more popular than Qdrant. We know about 51 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.
Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 4 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
Nhost.io - Serverless backend for web and mobile apps. The free plan includes PostgreSQL, GraphQL (Hasura), Authentication, Storage, and Serverless Functions. - Source: dev.to / 4 months ago
Only caveat I say is make sure there's something in it for you; if it's 2 AM it better be mostly for self-benefit. I'm busy constructing a monorepo with the latest technology with NX and pnpm and a half dozen other technologies (I recommend checking out http://nhost.io/); at the end I will build whatever I want and maybe make money. It's not done for the good of someone else exclusively that's for sure. Source: about 1 year ago
I'm really digging nhost and apollo-client. Source: about 1 year ago
Backend Frontend Database pick two or even one. Maybe something like this. Source: over 1 year ago
I am trying to use basic authentication of nhost.io . Source: over 1 year ago
Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.
Supabase - An open source Firebase alternative
Weaviate - Welcome to Weaviate
Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.
pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs
Hasura - Hasura is an open platform to build scalable app backends, offering a built-in database, search, user-management and more.