ANNex vs FAISS

FAISS (Facebook AI Similarity Search) is a C++/Python library for efficient similarity search, widely used in research. ANNex is a Rust-native engine that adds payload filtering, hybrid dense-sparse retrieval, snapshots, and WAL on top of HNSW.

Feature Comparison

	ANNex	FAISS
Dense vector search	✓	✓
Sparse / BM25	✓	✗
Hybrid RRF	✓	✗
Multivector / ColBERT	✓	✗
Payload filters	✓	✗
Embedded (no server)	✓	✓
HTTP server	✓	✗
Rust-native library	✓	✗
Python bindings	✓	✓
Snapshots + WAL	✓	✗

Recall–Latency: ANNex vs FAISS HNSW

NYT256 · 290K docs · 256-D angular · Apple M2 · interleaved · offset=4000 · ANNex v0.1.0 · FAISS 1.15.1

System	ef	Recall	p50 ms
annex_screen	32	0.864	0.252
annex_screen	128	0.918	0.580
annex_screen	512	0.961	2.205
faiss_hnsw16	128	0.868	0.356
faiss_hnsw16	512	0.925	1.457
faiss_hnsw32	128	0.904	0.627
faiss_hnsw32	512	0.957	2.489

FAISS HNSW32 is competitive; ANNex leads at matched recall below ~95%. Full data: benchmarks →

When ANNex makes sense

→ You need payload filtering, hybrid retrieval, or multivector search — FAISS has none of these.
→ You want a Rust library with persistence (snapshots, WAL) built in.
→ You want Python bindings with NumPy-native batch search and thread control.

When FAISS makes sense

→ You need GPU acceleration for very large-scale search.
→ You need IVF or product quantization indexes that FAISS has spent years optimizing.
→ Your team already has deep FAISS expertise or an existing FAISS integration.