[{"data":1,"prerenderedAt":75},["ShallowReactive",2],{"seo-verification":3,"marketplace-app-fr-qdrant":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"slug":7,"name":8,"description":9,"phase":10,"docsUrl":11,"logo":12,"github":13,"tagline":14,"longDescription":15,"features":16,"useCases":23,"steps":33,"faq":49,"specs":12,"compatibleOs":65,"relatedApps":67,"relatedPosts":68,"category":69},"qdrant","Qdrant","Self-hosted vector database for semantic search and RAG. Store, index and query high-dimensional embeddings at sub-millisecond latency — the missing infrastructure layer for your LLM stack.",1,"https:\u002F\u002Fservorbit.com\u002Fblog\u002Fself-host-qdrant-vps",null,"https:\u002F\u002Fgithub.com\u002Fqdrant\u002Fqdrant","Self-hosted vector database for semantic search and RAG — the missing infrastructure layer for your LLM stack.","Qdrant is an open-source, high-performance vector database written in Rust. It stores vector embeddings — the numerical representations produced by LLMs and embedding models — and retrieves the most similar ones in milliseconds, even across hundreds of millions of vectors.\n\nDeployed on a ServOrbit VPS, Qdrant becomes the memory layer of your AI stack: pair it with Ollama for local inference, LiteLLM for a unified API gateway, and your own application code to build production-grade RAG pipelines, semantic search engines, recommendation systems or AI chatbots that actually remember context — all on infrastructure you control, at a fixed monthly cost.",[17,18,19,20,21,22],"Sub-millisecond approximate nearest-neighbour search over millions of vectors","Payload filters — combine vector similarity with structured filters (category, date, score) in a single query","HNSW indexing + scalar and product quantisation for fast search on small RAM budgets","Collections and named vectors — store multiple embedding types per document in one collection","REST and gRPC APIs — every popular LLM framework (LangChain, LlamaIndex, Haystack) has a native Qdrant connector","Apache 2.0 — fully auditable, no telemetry, air-gap capable",[24,27,30],{"title":25,"body":26},"RAG pipeline memory","Index your documentation, knowledge base or product catalogue as embeddings and retrieve the most relevant chunks before each LLM call. Pair with Ollama on the same VPS for a fully air-gapped, zero-per-token RAG stack.",{"title":28,"body":29},"Semantic search","Replace keyword search with meaning-based retrieval. A query for 'fast payment processing' surfaces results about 'instant transactions' even when those exact words never appear — because Qdrant compares intent, not tokens.",{"title":31,"body":32},"Recommendation engine","Represent products, articles or users as embedding vectors and find the nearest neighbours. 'Users who liked this' and 'similar products' become a single nearest-neighbour query with optional payload filters for price or category.",[34,37,40,43,46],{"title":35,"body":36},"Order a VPS","Pick a ServOrbit VPS with at least 2 GB RAM (512 MB covers Qdrant itself; the rest is headroom for your embedding model or co-located services). Ubuntu 22.04, Docker pre-installed by the provisioner.",{"title":38,"body":39},"Deploy Qdrant","The provisioner runs: `docker run -d -p 6333:6333 -p 6334:6334 -v qdrant_storage:\u002Fqdrant\u002Fstorage --name qdrant --restart always qdrant\u002Fqdrant:latest`. The REST API is immediately available on port 6333; gRPC on 6334.",{"title":41,"body":42},"Create a collection and index vectors","Create a collection with the right vector size for your model (e.g. 1536 for OpenAI `text-embedding-3-small`, 4096 for `mxbai-embed-large` via Ollama): `curl -X PUT http:\u002F\u002F\u003Cvps-ip>:6333\u002Fcollections\u002Fmy_docs -H 'Content-Type: application\u002Fjson' -d '{\"vectors\":{\"size\":1536,\"distance\":\"Cosine\"}}'`. Then upsert your embeddings via the REST API or any Qdrant SDK.",{"title":44,"body":45},"Query from your application","Install the Qdrant client (`pip install qdrant-client` or `npm install @qdrant\u002Fjs-client-rest`) and query: pass the embedding of the user's question and get back the top-k most similar document chunks. Feed those chunks as context to your LLM for a grounded, hallucination-resistant answer.",{"title":47,"body":48},"Secure the instance","Set `QDRANT__SERVICE__API_KEY` as an environment variable (restart the container with `-e QDRANT__SERVICE__API_KEY=\u003Cstrong-key>`) and put Qdrant behind a reverse proxy (Caddy or Nginx) for HTTPS. Never expose port 6333 directly to the internet without authentication.",[50,53,56,59,62],{"question":51,"answer":52},"What is a vector database and why do I need one for RAG?","A vector database stores embeddings — the numerical representations produced by LLMs and embedding models. RAG (Retrieval-Augmented Generation) works by retrieving the document chunks most similar to a user's question before calling the LLM. A vector database like Qdrant does that retrieval in milliseconds across millions of chunks, which a relational database or full-text index cannot match.",{"question":54,"answer":55},"How much RAM does Qdrant need?","Qdrant itself idles around 100–200 MB. The practical limit is your index size: 1 million 1536-dimensional float32 vectors take about 6 GB unquantised. With scalar quantisation (int8) that drops to ~1.5 GB. A 4 GB VPS is comfortable for most small-to-medium projects.",{"question":57,"answer":58},"Which embedding models and frameworks work with Qdrant?","Any model that produces fixed-size float vectors works. Popular choices self-hosted via Ollama: `mxbai-embed-large` (1024 dims), `nomic-embed-text` (768 dims). Via API: OpenAI `text-embedding-3-small` (1536 dims), Cohere Embed. Frameworks with native connectors: LangChain, LlamaIndex, Haystack, semantic-kernel, and the official Python\u002FTypeScript\u002FGo\u002FRust SDKs.",{"question":60,"answer":61},"Is Qdrant a good alternative to Pinecone or Weaviate?","Qdrant competes directly with Pinecone (managed) and Weaviate (OSS). Qdrant's advantages: written in Rust for lower memory overhead, the best payload-filter + vector-search combination in benchmarks, and Apache 2.0 licence with no phoning home. Trade-off: you manage the infrastructure yourself, which is exactly what a ServOrbit VPS gives you.",{"question":63,"answer":64},"Can I run Qdrant alongside Ollama on the same VPS?","Yes. Both are single-container deployments with no port conflict (Ollama on 11434, Qdrant on 6333\u002F6334). A 4 GB RAM VPS comfortably hosts both with a 7B Q4 model loaded. This is the recommended starting point for a fully self-contained, air-gapped RAG stack.",[66],"ubuntu-24.04",[],[],{"key":70,"slug":70,"name":71,"objective":72,"icon":73,"color":74},"ia","Intelligence Artificielle","Créer, héberger et exploiter des solutions IA.","ai","text-purple-400 bg-purple-500\u002F10",1784126256786]