The reality of building vector search
Building approximate nearest neighbor (ANN) search that actually performs at scale isn't a sprint project. One engineer's detailed account of moving from Python prototype to C++ implementation highlights why Meta's FAISS library remains the reference implementation - and why most enterprises are better off using it than building their own.
The progression is predictable: Python prototypes work fine for proof-of-concept. NumPy delivers decent accuracy and acceptable latency for small datasets. Then you hit scale.
Where the complexity lives
The shift to C++ for performance reveals the real challenges aren't algorithmic - they're architectural. Memory access patterns matter more than CPU speed. Cache misses cost hundreds of queries per second. SIMD and AVX optimizations help, until thread contention and memory bandwidth become the bottleneck.
FAISS handles billions of vectors efficiently because it's built on years of this exact optimization work. The library supports multiple index types (IVF-Flat, HNSW) with different speed-accuracy trade-offs, GPU acceleration for large datasets, and production-tested memory management. Getting those details right takes time measured in engineer-years, not quarters.
The build vs. buy question
For enterprise teams, the decision tree is straightforward:
- Under 1 million vectors: Brute force often suffices. Don't overcomplicate.
- 1-10 million vectors: FAISS or similar libraries make sense. Integration complexity is manageable.
- Above 10 million: Managed services (Pinecone, Milvus) or FAISS with serious infrastructure investment.
The vector database market is projected above $4 billion by 2026, driven by RAG and AI search implementations. That growth creates pressure to build custom solutions. History suggests most teams overestimate their ability to match FAISS performance and underestimate the maintenance burden.
What this means in practice
Vector search is table stakes for AI applications. The implementation approach matters less than time-to-production and operational overhead. Unless your competitive advantage specifically requires novel ANN algorithms, use existing tools. FAISS isn't perfect - it demands expertise in dimensionality reduction, quantization, and hardware tuning - but it's production-proven.
The gap between working code and production-ready vector search is real. Most teams should respect that gap rather than try to cross it.