Qdrant Reviews and details

Questions & Answers

1. Why should a person choose Qdrant over its competitors?

   Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

2. What makes Qdrant unique?

   Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

3. Which are the primary technologies used for building your product?

   Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

Social recommendations and mentions

• Build Code-RAGent, an agent for your codebase

  The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by... - Source: dev.to / about 22 hours ago

• Ingest (almost) any non-PDF document in a vector database, effortlessly

  Qdrant is an easy-to-set-up, highly performing and scalable vector database, that offers numerous functionalities (among which hybrid search and metadata filtering). - Source: dev.to / 5 days ago

• Why You Shouldn’t Invest In Vector Databases?

  In cases where a company possesses a strong technological foundation and faces a substantial workload demanding advanced vector search capabilities, its ideal solution lies in adopting a specialized vector database. Prominent options in this domain include Chroma (having raised $20 million), Zilliz (having raised $113 million), Pinecone (having raised $138 million), Qdrant (having raised $9.8 million), Weaviate... - Source: dev.to / 6 days ago

• Preview 2 of .NET AI Chat Web App Template Now Available

  /filters:no_upscale()/news/2025/04/microsoft-dotnet-ai-template-p2/en/resources/1use-aspire-orchestration-1745167526397.png) A notable addition in Preview 2 is the support for .NET Aspire, enhancing the development toolkit with advanced AI capabilities. The Qdrant vector database can be utilized alongside .NET Aspire to create scalable applications. The template continues to utilize the Retrieval Augmented... - Source: dev.to / 8 days ago

• Building Your Own RAG System: Enhancing Claude with Your Documentation

  Qdrant is a vector database optimized for storing and searching these embeddings. - Source: dev.to / about 1 month ago

• 10 Ways AI Can Speed Up your Mobile App Development

  Qdrant is a vector similarity search engine. It enables storing and searching through high-dimensional vectors using embeddings. The database offers filtering capabilities and real-time updates. - Source: dev.to / 2 months ago

• Serverless LLM Chatbot Using Your Custom Data - built with Langtail and Qdrant

  The chatbot and the tool function will be hosted on Langtail but what about the data and its embeddings? I wanted a vector database that is free, easy to setup and use and allows me to have the actual text data stored there too. That led me to choose Qdrant vector database. It has a generous free tier for the managed cloud option and I can store the text data directly in the payload of the embeddings. - Source: dev.to / 6 months ago

• Step-by-Step: Building an AI Agent with Flowise, Qdrant and Qubinets

  Within the building process, in this case, our platform serves as the bridge between Flowise and Qdrant. It provides a unified platform seamlessly integrating both tools by handling all the underlying infrastructure and configuration. Qubinets automates the setup process, from instantiating a cloud environment to syncing Flowise and Qdrant to work together without any manual intervention. - Source: dev.to / 7 months ago

• A Complete Guide to Filtering in Vector Search

  This is called filtering and it is one of the key features of vector databases. Here is how a filtered vector search looks behind the scenes. We'll cover its mechanics in the following section. - Source: dev.to / 8 months ago

• vec2pg: Migrate to pgvector from Pinecone and Qdrant

  At launch we support migrating to Postgres from Pinecone and Qdrant. You can vote for additional providers in the issue tracker and we'll reference that when deciding which vendor to support next. - Source: dev.to / 8 months ago

• Nylas Assistant

  Nylas Assistant is an AI-powered email assistant built with Laravel, Nylas, OpenAI, and Qdrant. Sync your inbox, parse emails, and store them as OpenAI embeddings in a Qdrant vector database. Interact with an OpenAI agent through a chat-like interface that provides context-aware responses based on your emails. ✉️💡. - Source: dev.to / 8 months ago

• Our journey integrating Qdrant in Zerops

  Vector databases are revolutionizing how data is managed and stored for AI applications. At Zerops, we recognized the growing importance of vector databases, leading us to integrate Qdrant, one of the most popular options available. While it might seem straightforward to spin up a Qdrant instance using a Docker container, the reality of managing a production-ready vector database is far more complex. In this... - Source: dev.to / 8 months ago

• Top 5 Vector Databases in 2024

  Overview: Qdrant is an advanced vector search engine designed for high-dimensional data processing. It provides a scalable solution for similarity search and machine learning model integration. - Source: dev.to / 9 months ago

• txtai: Open-source vector search and RAG for minimalists

  Has anyone had experience with qdrant (https://qdrant.tech/) as a vector store data and can speak to how txtai compares? - Source: Hacker News / 9 months ago

• Building a Traceable RAG System with Qdrant and Langtrace: A Step-by-Step Guide

  Vector databases are the backbone of AI applications, providing the crucial infrastructure for efficient similarity search and retrieval of high-dimensional data. Among these, Qdrant stands out as one of the most versatile projects. Written in Rust, Qdrant is a vector search database designed for turning embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and... - Source: dev.to / 10 months ago

• Optimizing RAG Through an Evaluation-Based Methodology

  Qdrant is a vector database and vector similarity search engine designed for efficient storage and retrieval of high-dimensional vectors. Because Qdrant offers efficient indexing and searching capabilities, it is ideal for implementing RAG solutions, where quickly and accurately retrieving relevant information from extremely large datasets is crucial. Qdrant also offers a wealth of additional features, such as... - Source: dev.to / 10 months ago

• WizSearch: 🏆 Winning My First AI Hackathon 🚀

  Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 11 months ago

• How to Build a Chat App with Your Postgres Data using Agent Cloud

  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 / 12 months ago

• Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain

  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 / 12 months ago

• Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant

  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 1 year ago

• Ask HN: Has Anyone Trained a personal LLM using their personal notes?

  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 / about 1 year ago