Qdrant vs FAISS: Which Vector Database Should You Use?

Both store vectors and search them fast. The difference is what happens when your product catalog has 200,000 chunks, your team is three people, and you need to filter by tenant ID, delete a user's data on GDPR request, and not lose the index on container restart.

The Valid Failure

ShopBot's first vector store was an in-memory FAISS index. It was fast to set up, required no infrastructure, and worked correctly in development. On the first Railway deployment, the container restarted after six hours. The FAISS index was in RAM. It was gone.

Re-ingesting from scratch took eleven minutes. During those eleven minutes, ShopBot answered every question with "I don't have enough information" — because the retriever returned zero results for every query. The system was working as designed. The design was wrong.

Persistence is not a performance feature. It is a correctness requirement.

What FAISS Is

FAISS (Facebook AI Similarity Search) is a C++ library for approximate nearest-neighbour search. It is extremely fast, runs in-process, and has no dependencies beyond the library itself.

What it does not provide:

• Persistence to disk (you manage this manually)
• Payload storage (you manage a separate lookup table)
• Filtering by metadata (requires post-retrieval filtering)
• HTTP API (your application embeds it directly)
• Delete by ID (rebuilding the index is the standard approach)
• Multi-tenancy (one index, all data, you enforce any separation)

FAISS is the right choice when you need maximum throughput in a single-process application, have a static dataset that rarely changes, and are willing to manage persistence, filtering, and deletion yourself.

What Qdrant Is

Qdrant is a vector database — a server with a REST and gRPC API, persistent storage, payload filtering, and a Python client that wraps the API.

What it provides that FAISS does not:

• Persistent storage: chunks survive container restarts, deployments, crashes
• Payload filtering: must=[FieldCondition(key="tenant_id", match=MatchValue(value="tenant_a"))]
• Delete by ID or filter: client.delete(collection_name, points_selector=Filter(...))
• Named collections: one collection per tenant (the architecture used in Book 4)
• Scroll API: iterate over all points for migration, backup, or re-ingestion
• HTTP API: retrieval service decoupled from application process

Head-to-Head Comparison

Which to Use

Use FAISS when:

• You are building a prototype or research tool
• Your dataset fits in RAM and rarely changes
• You need maximum raw throughput and will manage infrastructure yourself
• You are embedding FAISS directly into a Python service with no multi-user requirements

Use Qdrant when:

• You need persistence across restarts and deployments
• You need to filter results by metadata (tenant, category, date)
• You need to delete specific documents (GDPR, catalog updates)
• You are building a multi-tenant system
• You want your retrieval service decoupled from your application

The RAG Mastery Series starts with ChromaDB in Book 1 (fast setup, development-friendly), migrates to Qdrant in Book 2 using a shadow deployment and comparison log, and uses per-tenant Qdrant collections in Book 4 for cryptographic isolation between six tenants.

A Minimal Qdrant Setup

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

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

client.recreate_collection(
    collection_name="products",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)

# Upsert with payload
client.upsert(
    collection_name="products",
    points=[
        PointStruct(
            id=str(uuid.uuid4()),
            vector=[0.1, 0.2, ...],  # your 384-dim vector
            payload={
                "text": "Cotton voile kurta, lightweight and airy.",
                "tenant_id": "shopbot",
                "product_id": "K-1299",
            },
        )
    ],
)

# Search with filter
from qdrant_client.models import Filter, FieldCondition, MatchValue

results = client.search(
    collection_name="products",
    query_vector=query_vector,
    query_filter=Filter(must=[FieldCondition(key="tenant_id", match=MatchValue(value="shopbot"))]),
    limit=3,
    score_threshold=0.72,
)

The Valid Knowledge

• FAISS is a library; Qdrant is a service: the operational model is different, not just the features.
• Persistence is a correctness requirement, not an optimisation: an index that disappears on restart means zero retrieval results, which means refusals on every query.
• Pre-retrieval filtering is faster than post-retrieval filtering: Qdrant applies payload filters during the ANN search; FAISS returns all nearest neighbours and you filter after — which means retrieving more vectors than you need at full cost.
• The migration path exists: Book 2 of the RAG Mastery Series shows how to move from ChromaDB to Qdrant under a live product using shadow reads and a comparison log, with zero downtime and no customer impact.