Qdrant

Qdrant

Open-source vector database for fast, scalable similarity search on embeddings with rich filtering

from 15€ per month
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Qdrant is an open‑source, high‑performance vector database and similarity search engine designed for AI workloads like semantic search, recommendations, and RAG(Retrieval‑augmented generation). It focuses on fast, accurate vector search at scale, with rich filtering and production‑grade infrastructure features.​

What Qdrant Is

Qdrant stores high‑dimensional embeddings (text, images, audio, etc.) and lets applications find the most similar items in milliseconds, even across millions or billions of vectors. It is written in Rust, exposes a convenient API, and is available both as open‑source and as a fully managed cloud service.​ Each stored item (called a point) consists of an ID, one or more vectors, and an optional JSON payload with metadata that can be used for filtering and ranking. This design makes it well suited for modern AI features where you need both semantic similarity and structured filters (for example, user, language, tags, or permissions).

Key Features

  • High‑performance vector search using optimized ANN indexes for low‑latency, high‑recall similarity search at scale.​
  • Rich JSON payloads with advanced filtering (text, numeric, geo, boolean) so you can combine semantic relevance with precise business rules.​
  • Scales to billions of vectors with sharding, replication, and on‑disk storage options that keep performance predictable as data grows.​
  • Advanced compression and quantization to dramatically reduce memory footprint while preserving accuracy.​
  • Production‑ready APIs, SDKs, and managed cloud offerings for fast integration into RAG, recommendation, and semantic search pipelines.

Use Cases

  • Semantic search over documents, tickets, logs, and knowledge bases to return contextually relevant results instead of simple keyword matches.​
  • Personalized recommendations for products, content, or jobs by matching user behavior and item embeddings in real time.​
  • Retrieval‑augmented generation (RAG) for LLM apps, retrieving the most relevant chunks from private data to ground model responses.​
  • Anomaly and fraud detection by spotting unusual behavior patterns as outlier vectors in high‑dimensional space.​
  • AI agents and copilots that need fast similarity search across multi‑modal embeddings (text, image, audio) to reason over large context.

Missing a specific application? No problem! Contact us – we check functionality and security and provide your desired app individually.

Power your AI apps with Qdrant, a blazing‑fast, open‑source vector database built to deliver accurate, scalable semantic search and recommendations in production.

FAQ

  • It's a high-performance open-source vector database that stores data as vectors (mathematical embeddings) for similarity search. It's essential for RAG (LLM memory) and semantic search.

  • Yes. It supports Dense (semantic) and Sparse (keyword-based) vectors simultaneously, allowing you to combine meaning and specific keyword matching for better results.

  • It uses Payload filtering, allowing fast, pre-filtered searches on metadata (like category or date) attached to the vectors, ensuring high speed and accuracy even on small subsets.

  • Qdrant differs from traditional databases because it is purpose-built for vector similarity search rather than exact-match or relational queries. While traditional SQL or NoSQL databases are optimized for structured data, joins, and transactional workloads, Qdrant is designed to store and index high-dimensional vector embeddings generated by machine learning models. This allows Qdrant to perform fast approximate nearest-neighbor searches and semantic retrieval. Additionally, Qdrant supports hybrid search by combining vector similarity with metadata filtering, making it more suitable for AI-driven applications such as semantic search, recommendation systems, and LLM-powered retrieval workflows.