Qdrant
High-performance vector database and similarity search engine for AI
About Qdrant
Qdrant is an open-source, high-performance vector database and similarity search engine engineered in Rust for production AI systems, semantic search engines, and Retrieval-Augmented Generation (RAG) pipelines. It provides lightning-fast nearest-neighbor search with rich payload filtering and custom distance metrics. Unlike traditional databases adapted for vectors, Qdrant was designed from day one to handle high-dimensional neural embeddings at scale. Its Rust engine provides memory-efficient vector quantization (scalar, product, and binary), allowing engineering teams to search billions of vectors on cost-effective cloud hardware.
Qdrant features advanced hybrid search capabilities, combining dense vector embeddings with sparse BM25 keyword vectors and lexical filters in a single query execution plan. It includes native multi-tenant payload partitioning, dynamic indexing, and zero-downtime collection snapshots. With client SDKs for Python, TypeScript, Go, Rust, and Java, Qdrant powers mission-critical search infrastructures for thousands of modern AI applications.
Deploy Qdrant locally via Docker or initialize a free cluster on Qdrant Cloud.
Initialize a collection with your embedding dimensionality and distance metric (Cosine, Dot, Euclidean).
Upsert vector embeddings along with rich JSON payload metadata (user IDs, tags, text chunks).
Execute hybrid similarity queries combining semantic vector proximity with strict boolean payload filters.
Scale your cluster seamlessly with distributed replication and automated sharding.
Capabilities & Features
Common Use Cases
vector-search
rag-pipelines
recommendation-systems
semantic-search
multimodal-search
Frequently Asked Questions
Why is Qdrant written in Rust?
Rust provides native memory safety without a garbage collector, enabling Qdrant to deliver deterministic sub-10ms vector search latency and high CPU concurrency under heavy load.
Is Qdrant open source and free to use?
Yes, Qdrant is fully open source under the Apache 2.0 license and can be self-hosted completely free on your own infrastructure.
Does Qdrant support hybrid search (vector + keyword)?
Yes, Qdrant natively supports hybrid search combining dense vector embeddings with sparse BM25 keyword vectors in a single unified API query.
Free Plan
Free tier with a 1GB cluster on Qdrant Cloud and unlimited open-source self-hosting via Docker
Paid Plan
Cloud clusters starting from $25/mo with auto-scaling, high availability, and hybrid cloud support
Pros & Cons
Engineered in Rust for blazing sub-10ms search latency and minimal memory footprint
Advanced vector quantization reduces RAM requirements by up to 90%
Native hybrid search combining dense semantic vectors and sparse keyword matching
100% open source under Apache 2.0 with unlimited self-hosting freedom
Comprehensive client SDKs across Python, TypeScript, Go, and Rust
Self-hosting distributed multi-node clusters requires Kubernetes operations expertise
Dedicated high-memory cloud clusters scale in cost for multi-billion vector catalogs
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