FAISS vs Qdrant: which vector database should you use?
Use FAISS if you need an in-memory, library-based vector index with no external service overhead. Use Qdrant if you need a managed vector database with persistent storage, filtering, and horizontal scaling.
VERDICT
Side-by-side comparison
| Feature | FAISS | Qdrant | Winner |
|---|---|---|---|
| Architecture | In-memory library (C++ core) | Standalone vector database server | Depends on use case |
| Query throughput (1M vectors) | ~100K-500K qps (IVF index) | ~10K-50K qps (HTTP API) | FAISS |
| Persistent storage | No (build-from-scratch required) | Yes (on-disk snapshots, replication) | Qdrant |
| Metadata filtering | No native support | Full metadata filtering with SQL-like queries | Qdrant |
| API type | Python/C++ library | REST + gRPC | Qdrant (production-ready) |
| Horizontal scaling | No (single machine) | Yes (clustering, replication) | Qdrant |
| Index rebuild required | Yes (offline process) | No (incremental updates) | Qdrant |
| Memory efficiency | Configurable (IVF/HNSW/Flat) | Similar but with disk spillover | Tie |
| License | MIT | AGPL (open source) + commercial | Tie |
| Ease of deployment | pip install faiss-cpu | Docker, cloud, or self-hosted | FAISS (for simple use cases) |
Performance benchmarks
Query latency (1M vector, 384-dim embeddings, top-100 recall)
FAISS in-process wins on latency. Qdrant's HTTP overhead adds 50-150ms; gRPC reduces this to ~20-80ms. FAISS numbers assume pre-built index in memory.
Throughput sustained (100 concurrent clients, 384-dim, batch size 10)
FAISS library scales linearly with threads; Qdrant scales with replicas. HTTP protocol adds 3-5x latency vs gRPC.
Memory per 1M 384-dim vectors (IVF or HNSW compression)
FAISS's Product Quantization (PQ) is more aggressive; Qdrant offers similar compression with better metadata separation.
Index rebuild time (10M vectors → re-index after 1M updates)
FAISS forces full rebuild; Qdrant accepts incremental updates. FAISS rebuild is faster but operationally painful at scale.
When to use each
- ✓ Building real-time search on pre-computed embeddings (e.g. semantic search at 100K+ qps on static corpus): FAISS's in-memory indexes are 5-10x faster than networked alternatives.
- ✓ You need sub-5ms query latency and can tolerate index rebuilds: FAISS is the fastest pure vector search library available.
- ✓ Embedding large document collections offline for batch processing or archived search: FAISS has no server overhead, perfect for compute-once scenarios.
- ✓ You're using a custom ML pipeline in Python and vector search is one component: FAISS integrates directly, no separate service to manage.
- ✓ Cost-sensitive inference where you run search on CPU and want zero external infrastructure: FAISS-cpu has minimal memory footprint and no deployment complexity.
- ✓ You need to insert, update, and delete vectors in production without rebuilding indexes: Qdrant handles incremental updates natively, FAISS requires offline re-indexing.
- ✓ Metadata filtering is critical (e.g. 'find similar documents where date > 2025-01'): Qdrant has built-in SQL-like filtering; FAISS requires external post-filtering.
- ✓ You want horizontal scaling or high availability: Qdrant supports clustering and replication; FAISS is single-machine only.
- ✓ Building a multi-tenant SaaS application: Qdrant's HTTP/gRPC API and per-collection isolation beats building a library wrapper around FAISS.
- ✓ You need operational parity with traditional databases (backups, replication, monitoring): Qdrant provides these; FAISS is a library, not a system.
Common misconceptions
FAISS
FAISS is a vector database: you can just add vectors and query them like MongoDB.
FAISS is an indexing library only. You manage your own vector storage, updates require full re-indexing offline, and there's no persistent API. It's a research tool, not a production database.
FAISS supports filtering metadata natively: just query 'embeddings similar to X where category=Y'.
FAISS has no metadata support. You must post-filter results in your application code or maintain a separate index. For real-world queries with filters, you lose 80%+ of FAISS's speed advantage.
FAISS scales to 1 billion vectors easily if you use the right index (IVF, HNSW, etc).
FAISS is single-machine. 1B vectors on a 512GB server is possible but index construction takes hours, updates are impossible, and you have a single point of failure. At that scale, use Qdrant.
Qdrant
Qdrant is as fast as FAISS for pure vector search: it just adds features on top.
Qdrant is 5-10x slower than FAISS due to HTTP/gRPC marshaling, server overhead, and disk I/O for metadata. If your only requirement is throughput on static embeddings, FAISS wins decisively.
Qdrant's AGPL license means I can't use it in a commercial product.
AGPL is enforced only for modifications to Qdrant itself; using Qdrant as a service (dockerized, in the cloud) is fine commercially. But deploying modified Qdrant code requires source release. Use commercial license if modifying core Qdrant.
Qdrant automatically scales horizontally: just add nodes and it rebalances shards.
Qdrant sharding is manual at collection creation time; resharding requires downtime or careful orchestration. True multi-node scaling exists but is not automatic. Plan capacity upfront.
Code examples
Task: Build a vector index from 10K embeddings and perform top-10 similarity search.
import faiss
import numpy as np
# Create embeddings (e.g. from OpenAI API or local encoder)
vectors = np.random.randn(10000, 1536).astype('float32')
# Build IVF index (inverted file, fast approximate nearest neighbors)
index = faiss.IndexIVFFlat(faiss.IndexFlatL2(1536), 1536, 100)
index.train(vectors) # FAISS requires offline training
index.add(vectors)
# Search for top-10 nearest neighbors
query = np.array([[0.1, 0.2, 0.3] + [0.0]*1533], dtype='float32')
distances, indices = index.search(query, 10) # Query is in-memory, ~1ms
print(f"Nearest indices: {indices[0]}")
print(f"Distances: {distances[0]}")FAISS builds indexes offline (index.train + add) and queries in-memory at microsecond latency. No persistent storage or incremental updates: the entire workflow is pre-computed and static.
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, VectorParams, Distance
# Connect to Qdrant (local or remote)
client = QdrantClient("localhost", port=6333) # Start with: docker run -p 6333:6333 qdrant/qdrant
# Create collection with vector configuration
client.recreate_collection(
collection_name="embeddings",
vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
# Upsert vectors (incremental, no rebuild)
points = [
PointStruct(
id=i,
vector=np.random.randn(1536).tolist(),
payload={"doc_id": f"doc_{i}"}
)
for i in range(10000)
]
client.upsert(collection_name="embeddings", points=points)
# Search (HTTP/gRPC, ~50ms latency)
query_vector = [0.1, 0.2, 0.3] + [0.0]*1533
results = client.search(
collection_name="embeddings",
query_vector=query_vector,
limit=10
)
for hit in results:
print(f"ID: {hit.id}, Score: {hit.score}")Qdrant accepts incremental upserts without re-indexing and stores metadata (payload) alongside vectors. Queries go over HTTP/gRPC, adding latency but enabling distributed architecture and persistence.
Migration path
- Migrating from FAISS to Qdrant:
- Install: pip install qdrant-client instead of managing FAISS builds.
- Replace offline faiss.IndexIVF* + .train() + .add() with qdrant_client.upsert(): no training step needed.
- Replace faiss.search() with client.search(): returns PointStruct objects instead of numpy arrays.
- For metadata, add payload dict to PointStruct instead of maintaining a separate lookup table.
- Expect 10-100x higher latency per query (2ms → 50-200ms) but gain incremental updates, persistence, and metadata filtering. Migrating from Qdrant to FAISS:
- Export all vectors + IDs from Qdrant via client.scroll().
- Build FAISS index offline: faiss.IndexIVFFlat(...).train(vectors).add(vectors).
- Save index to disk: faiss.write_index(index, 'index.bin').
- Replace client.search() with faiss.read_index(...).search().
- Expect 10-100x lower latency but lose incremental updates and metadata filtering. Loss of payload requires re-implementing metadata lookups via a side hashmap (id → metadata).
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