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 Qdrant?

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

Is Qdrant good?

Social recommendations and mentions

• How to give Claude Code persistent memory with a self-hosted mem0 MCP server

  The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools for your Claude Code instance to manage long-term memory operations, and your memories data never leaves your machine. - Source: dev.to / 5 months ago

• The Database Zoo: Vector Databases and High-Dimensional Search

  Qdrant: Open-source vector database optimized for hybrid search and easy integration with ML workflows. - Source: dev.to / 8 months ago

• Java's Agentic Framework Boom is a Code Smell

  Yes, Java SDKs are critical. But you don't need to rebuild entire orchestration engines just to write agents in Java. The ecosystem already has platforms solving the hard problems: memory (Zep, Mem0, LangMem), tools (specialized platforms), vectors (Pinecone, Weaviate, Qdrant), observability (LangSmith, Helicone, Langfuse). Integrate, don't rebuild. - Source: dev.to / 9 months ago

• What is the Most Effective AI Tool for App Development Today?

  James Allsopp adds, "LangChain or LlamaIndex for managing LLM workflows, especially if you're adding vector search or documents." These tools handle multi-step processes, essential for complex apps. - Source: dev.to / 11 months ago

• 🔥 Build a RAG Chatbot That Talks to Your Documents Using Python (Gemma + Qdrant + Docling)

  📦 Qdrant for fast vector search and retrieval. - Source: dev.to / 12 months ago

• How to build image search with semantic understanding

  Qdrant is a high performance vector database. We use it to store and query the embeddings. - Source: dev.to / about 1 year ago

• 10 open-source MCPs that make your AI agents smarter than your team lead

  Qdrant — open-source and super developer-friendly. - Source: dev.to / about 1 year ago

• 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 1 year 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 / about 1 year 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 / about 1 year 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 / about 1 year 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 / over 1 year 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 / over 1 year 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 / over 1 year 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 / almost 2 years 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 / almost 2 years 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 / almost 2 years 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 / almost 2 years 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 / almost 2 years 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 / almost 2 years 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 / almost 2 years ago