FAISS vs Chroma

Library Philosophy

FAISS provides algorithmic building blocks for vector search. You're responsible for the application layer, persistence, and infrastructure.

Core Design

• • Direct memory management
• • Multiple index types (Flat, IVF, HNSW)
• • Quantization techniques (PQ, SQ)
• • SIMD and GPU optimizations

Database Philosophy

Chroma focuses on developer experience, providing a complete embedded database that "just works" for most use cases.

Core Design

• • SQLite/DuckDB backend
• • HNSW index for vectors
• • Automatic persistence
• • Simple collection API

Getting Started

import faiss
import numpy as np

# Create and train index
dim = 768
index = faiss.IndexFlatL2(dim)
# Or use IVF for scale
# index = faiss.IndexIVFFlat(
#     faiss.IndexFlatL2(dim), dim, 100
# )
# index.train(training_vectors)
index.add(vectors)

# Search
D, I = index.search(query_vectors, k=10)

Pros

• ✓ Incredible performance
• ✓ Rich algorithm choices
• ✓ GPU acceleration
• ✓ Fine-grained control

Cons

• ✗ Complex API
• ✗ No built-in features
• ✗ Steep learning curve

Getting Started

import chromadb

# Create client and collection
client = chromadb.Client()
collection = client.create_collection("my_collection")

# Add documents with embeddings
collection.add(
    embeddings=vectors,
    documents=texts,
    metadatas=metadata,
    ids=ids
)

# Query
results = collection.query(
    query_embeddings=query_vectors,
    n_results=10
)

Pros

• ✓ Dead simple API
• ✓ Built-in persistence
• ✓ Metadata filtering
• ✓ Zero configuration

Cons

• ✗ Limited performance
• ✗ Scale limitations
• ✗ Basic features only