From Memory-Hungry HNSW to Quantized SPANN: The Technical Evolution of Pinterest's Manas Platform
Pinterest cut search index memory by half without losing accuracy. Here's how they quantized their way out of the vector-storage trap.
Why it matters
A real-world case study in vector-database optimization that practitioners building search or RAG systems can apply directly: quantization techniques that preserve recall while cutting infrastructure costs.
The key facts
12 to knowManas platform transitioned from HNSW to quantized SPANN
Scalar and Product Quantization reduced memory usage significantly
High recall rates maintained despite compression
SSD optimization for performance
Multi-vector models for relevance refinement
Published by Pinterest Engineering
Pinterest Manas platform transitioned from HNSW to quantized SPANN
Applied Scalar and Product Quantization to decrease memory usage significantly
Maintained high recall rates during optimization
Platform uses SSDs for performance optimization
Migrating to multi-vector models for relevance matching
Platform manages vast data at scale for search and discovery
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: Pinterest Engineering has enhanced its Manas search platform to manage vast data, improving efficiency in search and discovery functions. By applying Scalar and Product Quantization, memory usage decreased significantly while maintaining high recall rates. The platform utilizes SSDs for optimized…