How does ChromaDB compare to FAISS, and when should you choose one over the other?

FAISS (Facebook AI Similarity Search) and ChromaDB both store and search embedding vectors, but they are designed for very different use cases. FAISS is a low-level library optimised for raw performance; ChromaDB is a higher-level database designed for developer ergonomics and full-stack AI applications.

# FAISS — lower level, manage everything manually
import faiss
import numpy as np

# Build index manually
dim = 384
index = faiss.IndexFlatIP(dim)           # inner product
vectors = np.random.rand(1000, dim).astype("float32")
faiss.normalize_L2(vectors)
index.add(vectors)                        # add vectors
D, I = index.search(query_vec, k=5)      # search
faiss.write_index(index, "index.faiss")  # save manually

# ChromaDB — higher level, text in, results out
import chromadb
client = chromadb.Client()
col = client.create_collection("demo")
col.add(documents=["text one", "text two"], ids=["1","2"])
results = col.query(query_texts=["similar text"], n_results=2)
# Embeddings, persistence, metadata all handled automatically