# Building the AI Retrieval Infrastructure Behind 20 Billion+ Vectors at HubSpot

DevFeed: [Building the AI Retrieval Infrastructure Behind 20 Billion+ Vectors at HubSpot](<https://devfeed.tech/articles/building-the-ai-retrieval-infrastructure-behind-20-billion-vectors-at-hubspot-29102.md>)

Original publisher: [Read original article](<https://product.hubspot.com/blog/building-the-ai-retrieval-infrastructure-behind-20-billion-vectors-at-hubspot>)

Author: Oleg Tereshin & Xin Liu

Published: 2026-06-25T18:17:44Z

Content type: article

Language: en

Sources: [HubSpot](<https://devfeed.tech/sources/hubspot.md>)

Topics: [Qdrant](<https://devfeed.tech/topics/qdrant.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [systems](<https://devfeed.tech/topics/systems.md>), [quantization](<https://devfeed.tech/topics/quantization.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [search](<https://devfeed.tech/tags/search.md>), [vector-database](<https://devfeed.tech/tags/vector-database.md>), [vectors](<https://devfeed.tech/tags/vectors.md>)

## AI overview

HubSpot describes how it built VaaS, a centralized vector storage and search platform using Qdrant, to support semantic search across tens of billions of vectors and many use cases.

## Source excerpt

Discover how HubSpot built a scalable AI retrieval infrastructure, managing over 20 billion vectors with Qdrant, to enhance semantic search and support diverse applications.