Qdrant

Qdrant is a vector database written in Rust for storing embeddings with associated payloads and running fast similarity search with extended filtering (faceted search, metadata conditions). It exposes both REST (port 6333) and gRPC (6334) APIs plus a built-in web dashboard, and supports dense, sparse, and multi-vector search with quantization, sharding, and replication for scale.

It is Apache-2.0 licensed, ships as a single Docker image, and has official clients for Python, TypeScript, Go, Rust, Java, and .NET. Qdrant Cloud is the managed offering; the OSS core is the same engine. Independent press coverage includes TechCrunch's reporting on its $28M Series A led by Spark Capital (2024).

1. Pull and run the server:

docker run -p 6333:6333 -p 6334:6334 -v "$(pwd)/qdrant_storage:/qdrant/storage:z" qdrant/qdrant

1. Open the dashboard at http://localhost:6333/dashboard to verify it is up.
2. Install a client: pip install qdrant-client (or the TS/Go/Rust/Java/.NET client).
3. Create a collection:

```python

from qdrant_client import QdrantClient

from qdrant_client.models import Distance, VectorParams

client = QdrantClient(url="http://localhost:6333")

client.create_collection(collection_name="test_collection",

vectors_config=VectorParams(size=4, distance=Distance.DOT))

```

1. Upsert points and run client.query_points(collection_name=..., query=[...], limit=3) — both verified verbatim from the official quickstart.
2. Before any networked use, set an API key / JWT access control and TLS per the security docs — the quickstart runs unauthenticated.