FAISS vs Qdrant

Library Design

FAISS provides low-level building blocks for vector search. You get complete control but must implement all database functionality yourself.

Core Components

• • Multiple index types (Flat, IVF, HNSW)
• • Quantization methods (PQ, SQ, LSQ)
• • GPU implementations
• • Direct memory control

Database Design

Qdrant is a complete database with advanced filtering, payload storage, and production features built-in from day one.

Core Components

• • HNSW index with filtering
• • Payload storage engine
• • Distributed architecture
• • REST and gRPC APIs

Getting Started

import faiss
import numpy as np

# Build index
index = faiss.IndexFlatL2(768)
# Or use IVF for scale
quantizer = faiss.IndexFlatL2(768)
index = faiss.IndexIVFFlat(quantizer, 768, 100)
index.train(training_data)
index.add(vectors)

# Search (no filtering)
D, I = index.search(query, k=10)

Pros

• ✓ Unmatched performance
• ✓ Algorithm flexibility
• ✓ GPU acceleration
• ✓ Memory efficiency

Cons

• ✗ No built-in filtering
• ✗ Manual everything
• ✗ Complex deployment

Getting Started

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

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

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

# Add with payloads
client.upsert(
    collection_name="my_collection",
    points=[{
        "id": i,
        "vector": vector,
        "payload": {"category": "...", "price": 100}
    }]
)

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

Pros

• ✓ Advanced filtering
• ✓ Production ready
• ✓ REST/gRPC APIs
• ✓ Payload storage

Cons

• ✗ Higher latency
• ✗ More resources
• ✗ Complex setup