Overview
Qdrant is a vector database built in Rust that stores embeddings and runs nearest-neighbor search for AI applications — retrieval-augmented generation, semantic search, recommendations, and deduplication. It exposes REST and gRPC APIs, runs as a single binary or a distributed cluster, and is one of the few engines that combine dense vectors, sparse (BM25/SPLADE) vectors, and rich metadata filtering in one query. Because it is open source under the Apache-2.0 license, you can run it on a modest VPS or inside your own Kubernetes cluster with no software cost; Qdrant Cloud adds a managed control plane, autoscaling, and a permanent free tier. In our testing it handles millions of vectors on modest hardware and stays competitive with closed-source alternatives on latency, which is why it has become a default choice for RAG engineers who want to avoid vendor lock-in.
Key Features
- Rust-native engine with HNSW indexing for sub-millisecond search at millions of vectors
- Hybrid search mixing dense and sparse vectors in a single ranked query
- Named vectors let one collection hold multiple embedding models (text, image, multilingual)
- Strong metadata pre-filtering by conditions, ranges, and geo before the vector search
- Self-hosted, hybrid, or fully managed Cloud with SOC 2, ISO 27001, and GDPR
Pricing
| Plan | Price | For |
|---|---|---|
| Self-hosted | $0 | Open source Apache-2.0, run on your own infrastructure |
| Cloud Free | $0/mo | 1 GB storage, 0.5M vectors, free forever |
| Cloud Standard | from $25/mo | Usage-based, $0.06/GB-mo storage, production scale |
| Enterprise | Custom | HIPAA, PrivateLink, dedicated SLA, BYOC |
Comparison
vs. Pinecone: Pinecone is fully managed and the easiest to start, but it is closed source and locks one index to a single embedding model. Qdrant is open source, supports multiple named vectors per collection, and can be self-hosted for free — better if you want to avoid lock-in or run hybrid search. Choose Pinecone for zero-ops simplicity; choose Qdrant when cost control and flexibility matter.
vs. Weaviate: Weaviate also offers hybrid search and a GraphQL API, while Qdrant keeps a simpler REST/gRPC surface and a Rust performance edge. Qdrant’s named-vectors API is less configuration-heavy than Weaviate’s multi-vector setup. Pick Weaviate if you like its module ecosystem; pick Qdrant for raw throughput and low memory.
vs. RAGFlow: RAGFlow is a full RAG pipeline with document parsing, whereas Qdrant is the retrieval store underneath such a pipeline. Use RAGFlow when you want an end-to-end box; use Qdrant when you are building the retrieval layer yourself.