# Evolving Pinterest's Embedding Retrieval Platform

DevFeed: [Evolving Pinterest's Embedding Retrieval Platform](<https://devfeed.tech/articles/evolving-pinterest-s-embedding-retrieval-platform-1230.md>)

Original publisher: [Read original article](<https://medium.com/pinterest-engineering/evolving-pinterests-embedding-retrieval-platform-aede4e831e01?source=rss----4c5a5f6279b6---4>)

Author: Pinterest Engineering

Published: 2026-09-11T15:01:03Z

Content type: article

Language: en

Sources: [Pinterest Engineering Blog - Medium](<https://devfeed.tech/sources/pinterest-engineering-blog-medium.md>)

Topics: [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [IO](<https://devfeed.tech/topics/io.md>)

Tags: [ann](<https://devfeed.tech/tags/ann.md>), [cost](<https://devfeed.tech/tags/cost.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [latency](<https://devfeed.tech/tags/latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [models](<https://devfeed.tech/tags/models.md>), [pinterest](<https://devfeed.tech/tags/pinterest.md>), [platform](<https://devfeed.tech/tags/platform.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [scalability](<https://devfeed.tech/tags/scalability.md>), [search](<https://devfeed.tech/tags/search.md>)

## AI overview

Pinterest describes evolving its Manas embedding-retrieval platform to address the cost, scale, and flexibility challenges of serving billions of embeddings. The excerpt covers ANN search, vector quantization, and SSD-based serving.

## Source excerpt

Authors: Bowen Zhou | Staff Software Engineer; Shan Gao | Senior Software Engineer; Jingwen Hu | Software Engineer II; Wenjiang Chu | Staff Software Engineer The Billion-Embedding Challenge At Pinterest, the "signal" is our lifeblood. Whether it's a home decor enthusiast finding the perfect rug or a fashion seeker discovering a new aesthetic, our discovery engine relies on understanding deep semantic relationships to help our users find inspirations. Over the last few years, the explosive growth of embedding-based retrieval has fundamentally transformed how we surface these signals -- and at the heart of that transformation is Manas, Pinterest's in-house distributed search platform. Embedding Retrieval is one of the core capabilities of Manas, supporting multiple approximate nearest neighbor search algorithms, hybrid queries with both token and embedding clauses, as well as real-time updates to ensure fresh contents become searchable within seconds. Deployed on over 80 clusters and serving billions of embeddings, Manas embedding retrieval powers all major product surfaces at Pinterest including Home Feed, Search, Related Pins, Ads, and Notifications. However, as our corpus scales toward tens of billions of embeddings and our models capture increasingly complex interactions, we face mounting challenges around cost efficiency, scalability, and flexibility. On the infrastructure side, traditional ANN algorithms like HNSW are notoriously memory-hungry -- they require the entire index to reside in RAM to maintain low query latency, making cost grow linearly with corpus size. On the modeling side, the classic two-tower retrieval paradigm is too restrictive: it reduces each candidate to a single embedding and scores relevance through a simple dot product, leaving little room to express richer, context-dependent notions of similarity. To tackle these challenges, our team has been evolving Manas's embedding retrieval stack across three fronts: Quantization. We reduce the memor