# Qdrant

Qdrant는 AI 검색, 시맨틱 검색, 유사도 매칭을 위해 Rust로 작성된 오픈 소스 벡터 검색 엔진입니다. 벡터 검색과 JSON 페이로드 필터링을 결합하며 클라우드, 하이브리드, 프라이빗, 엣지 배포를 지원합니다.

- Findings24: <https://findings24.com/ko/tools/qdrant-qdrant-tech>
- 웹사이트: <https://qdrant.tech>
- 가격: Freemium
- 카테고리: AI 지식 베이스, AI 검색 엔진, AI 연구 어시스턴트

## Qdrant란 무엇인가요?

Qdrant is an open-source vector search engine written in Rust. It is designed for vector similarity search at scale and is positioned for AI retrieval workflows that need fast search, metadata filtering, and flexible deployment.

The product combines vector storage with JSON payloads, hybrid dense-plus-sparse search, and multivector retrieval. It also supports cloud, hybrid, private, and edge deployment models, so teams can use it as a managed service or run it in their own infrastructure.

## Qdrant(으)로 무엇을 할 수 있나요?

### Payload-aware filtering

Store JSON payloads next to vectors and filter on structured metadata such as keywords, numbers, geo values, datetimes, and UUIDs.

### Hybrid search

Combine dense and sparse retrieval in a single query for keyword-plus-semantic search workflows.

### Multivector retrieval

Use multiple vectors per object to model richer representations and late interaction retrieval patterns.

### One-stage filtering

Apply filters during HNSW traversal to support efficient one-stage filtering with high recall and low latency.

### Reranking and result control

Re-rank and diversify results with business logic, late interaction models, and techniques such as MMR.

### Developer APIs and tools

Work through REST, gRPC, and official clients, with a built-in web UI for inspecting collections and queries.

## 사용 사례

### RAG and GenAI

Use Qdrant as the retrieval layer for RAG systems that need fresh context, metadata filters, and a mix of dense and sparse signals.

### AI agents

Build memory-backed agents that search for relevant context quickly across evolving conversation or task data.

### Semantic search

Power semantic search experiences that go beyond keyword matching by combining vector search with metadata constraints.

### Recommendations

Support recommendation flows where similarity search is used to surface related items and personalized results.

### Anomaly detection

Analyze records for unusual patterns by searching for points that differ from the expected vector distribution.

## 자주 묻는 질문

### What pricing and deployment options does Qdrant offer?

Qdrant Cloud offers a free tier for testing and prototypes, a standard tier for production workloads, and premium and enterprise options for additional security and compliance needs. The pricing page also describes Hybrid Cloud and Private Cloud deployment options.

### Can existing OSS deployments move to Qdrant Cloud?

Yes. The pricing page says you can migrate from OSS to Qdrant Cloud, and it mentions a migration tool and documentation to help with the transition.

### What kinds of metadata can Qdrant store and search on?

Qdrant stores payloads as JSON alongside vectors. Those fields can be used for filtering and ranking, and the documentation shows support for types such as integers, floats, booleans, keywords, geo values, datetimes, and UUIDs.

### Does Qdrant support hybrid search?

Qdrant supports hybrid queries that combine dense and sparse vectors in one query, including approaches such as BM25, SPLADE++, and miniCOIL.

### Where can Qdrant be deployed?

Qdrant can be deployed as managed cloud, hybrid cloud, private cloud, or edge, depending on the level of control and infrastructure model you need.

## 주요 정보

- **Category**: Vector Search Engine
- **Platform**: Cloud, hybrid, private cloud, and edge
- **Primary users**: Developers and teams building AI retrieval systems
- **Primary language**: Rust
- **Source domain**: qdrant.tech
- **Pricing model**: Free tier plus usage-based and enterprise options