Qdrant API
Qdrant API
Qdrant is an open-source vector database written in Rust — excellent performance (Rust-based), clean API, growing ecosystem; main open-source alternative to Pinecone.
Rust-based excellent performance (faster than Weaviate on some benchmarks)
Younger ecosystem than Weaviate (born 2021)
docker run qdrant/qdrant for local instance. Python: client.create_collection("docs", vectors_config=VectorParams(size=1536, distance=Distance.COSINE))
Uptime · 30-day window
About this API
Qdrant is a Berlin-based open-source vector database company founded 2021, one of the main open-source alternatives to Pinecone. Differentiators: (1) Written in Rust, considered one of the highest performance in the vector DB space — faster than Weaviate, Milvus on some benchmarks; (2) Complete quantization support — product quantization (PQ), scalar quantization (SQ) can reduce memory usage to 1/4 to 1/8 while preserving search quality; (3) Clean modern API design with good docs. Ecosystem is younger than Weaviate (2-year gap) but growing fast — HuggingFace, X (Twitter), Disney are users. Qdrant Cloud is the commercial SaaS version; self-host Docker is one-command start.
What you can build
- 1High-performance RAG apps
- 2Self-host without cloud vendor dependency
- 3Edge deployment (small Rust binary)
- 4Quantization for memory-efficient scenarios
Strengths & limitations
Strengths
- Rust-based excellent performance (faster than Weaviate on some benchmarks)
- Quantization (PQ, SQ) saves memory
- Clean API design
- Active community + commercial version
Limitations
- Younger ecosystem than Weaviate (born 2021)
- Hybrid search weaker than Weaviate
- SDK coverage improving but still less than Pinecone
Example request
curl https://qdrant.tech/<endpoint> \
-H "Authorization: Bearer $API_KEY"
# Some providers use X-Api-Key instead — verify in the docs.Getting started
docker run qdrant/qdrant for local instance. Python: client.create_collection("docs", vectors_config=VectorParams(size=1536, distance=Distance.COSINE))
FAQ
Qdrant vs. Weaviate performance?+
Raw vector search: Qdrant slightly faster. Hybrid (vector + keyword): Weaviate more native. Depends on use case.
Is quantization worth it?+
When memory-constrained, PQ saves 4-8x memory with <5% search quality loss. Worth enabling on large datasets (millions of vectors).
Technical details
- Auth type
- api_key
- Pricing
- freemium
- Rate limit
- 自托管无限；Cloud 按节点付费
- Protocols
- REST, gRPC
- SDKs
- python, typescript, rust, go, java, csharp
- Response time
- 322 ms
- Last health check
- 5/12/2026, 7:38:08 AM