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

, ]
}
client.schema.create_class(schema)

# GraphQL query with filters
result = client.query.get("Product", ["title", "price"]) \
    .with_near_vector() \
    .with_where() \
    .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

        }]
    },
    limit=10
)`}

Pros

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

Cons

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