ToolsSeptember 16, 2026via InfoQ AI/ML
From Memory-Hungry HNSW to Quantized SPANN: The Technical Evolution of Pinterest's Manas Platform
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.
Key signals
- Manas 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
The hook
Pinterest cut search index memory by half without losing accuracy. Here's how they quantized their way out of the vector-storage trap.
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 pe…