Weaviate vs Qdrant

Schema-First Design

Weaviate uses a schema-based approach with classes and properties, similar to traditional databases but optimized for vectors.

Core Components

• • GraphQL query engine
• • HNSW vector index
• • Module system for extensibility
• • Built-in vectorizers

Performance-First Design

Qdrant focuses on high-performance vector search with advanced filtering, optimizing every component for speed.

Core Components

• • Optimized HNSW implementation
• • Advanced filtering engine
• • Payload storage system
• • REST and gRPC APIs

Getting Started

import weaviate

client = weaviate.Client("http://localhost:8080")

# Define schema
schema = {
    "class": "Product",
    "properties": [{
        "name": "title",
        "dataType": ["text"]
    }, {
        "name": "price",
        "dataType": ["number"]
    }]
}
client.schema.create_class(schema)

# GraphQL query with filters
result = client.query.get("Product", ["title", "price"])     .with_near_vector({"vector": query_vector})     .with_where({
        "path": ["price"],
        "operator": "GreaterThan",
        "valueNumber": 50
    })     .with_limit(10)     .do()

Pros

• ✓ Powerful GraphQL API
• ✓ Built-in modules
• ✓ Multi-modal support
• ✓ Rich documentation

Cons

• ✗ Complex schema setup
• ✗ Verbose queries
• ✗ Steeper learning

Getting Started

from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance

client = QdrantClient("localhost", port=6333)

# Create collection
client.create_collection(
    collection_name="products",
    vectors_config=VectorParams(size=768, distance=Distance.COSINE)
)

# Search with filters
results = client.search(
    collection_name="products",
    query_vector=query_vector,
    query_filter={
        "must": [{
            "key": "price",
            "range": {"gte": 50}
        }]
    },
    limit=10
)

Pros

• ✓ Simple API design
• ✓ Great performance
• ✓ Advanced filtering
• ✓ Lower resources

Cons

• ✗ No GraphQL
• ✗ Manual multi-modal
• ✗ Smaller community