What's the difference between Faiss and Pinecone for vector search?

FAISS vs Pinecone for Vector Search

Choosing between FAISS and Pinecone comes down to where you want to manage infrastructure.

FAISS (Facebook AI Similarity Search)

An open-source library for efficient similarity search. Runs in-process — no server needed.

pythonCopy
import faiss
import numpy as np

dim = 1536  # text-embedding-3-small dimension

# Flat index — exact search, best for < 100K vectors
index_flat = faiss.IndexFlatL2(dim)

# IVF index — approximate search, fast for millions of vectors
quantizer = faiss.IndexFlatL2(dim)
index_ivf = faiss.IndexIVFFlat(quantizer, dim, nlist=100)  # 100 Voronoi cells
index_ivf.train(training_embeddings)   # Required for IVF

# GPU acceleration
res = faiss.StandardGpuResources()
index_gpu = faiss.index_cpu_to_gpu(res, 0, index_flat)

# Add vectors
embeddings = np.random.rand(10000, dim).astype(np.float32)
index_flat.add(embeddings)

# Search
query = np.random.rand(1, dim).astype(np.float32)
distances, indices = index_flat.search(query, k=5)

# Save and load (for persistence)
faiss.write_index(index_flat, "my_index.faiss")
loaded_index = faiss.read_index("my_index.faiss")

Pinecone

A managed vector database — cloud-hosted, serverless, with real-time upserts, metadata filtering, and namespaces.

pythonCopy
from pinecone import Pinecone, ServerlessSpec

pc = Pinecone(api_key="your-api-key")

# Create index (one-time setup)
pc.create_index(
    name="my-rag-index",
    dimension=1536,
    metric="cosine",
    spec=ServerlessSpec(cloud="aws", region="us-east-1")
)

index = pc.Index("my-rag-index")

# Upsert vectors with metadata
vectors = [
    {
        "id": "doc_001",
        "values": embedding_1,
        "metadata": {"source": "handbook.pdf", "page": 5, "category": "policy"}
    },
    {
        "id": "doc_002",
        "values": embedding_2,
        "metadata": {"source": "faq.pdf", "page": 1, "category": "faq"}
    }
]
index.upsert(vectors=vectors, namespace="production")

# Query with metadata filter
results = index.query(
    vector=query_embedding,
    top_k=5,
    filter={"category": {"$eq": "policy"}},  # Only retrieve policy docs
    include_metadata=True,
    namespace="production"
)

for match in results.matches:
    print(f"Score: {match.score:.4f} | ID: {match.id} | Source: {match.metadata['source']}")

Detailed Comparison

pip install faiss-cpu

When to Use Each

Use FAISS when:

• Prototyping or small-scale production (< 1M vectors)
• Full control required (on-prem, air-gapped)
• Minimising costs is critical
• Embedding rarely changes (batch indexing is fine)

Use Pinecone when:

• Real-time upserts needed (new documents indexed immediately)
• Metadata filtering is required at query time
• Team doesn't want to manage infrastructure
• Scale > 10M vectors or multi-region needed

Other Alternatives

Recommendation: Use Chroma or FAISS for development and small production deployments. Migrate to Pinecone or Qdrant when you need managed infrastructure, real-time updates, or metadata filtering at scale.