# How Cosmos delivered editorial-grade visual search with Qdrant

DevFeed: [How Cosmos delivered editorial-grade visual search with Qdrant](<https://devfeed.tech/articles/how-cosmos-delivered-editorial-grade-visual-search-with-qdrant-46597.md>)

Original publisher: [Read original article](<https://qdrant.tech/blog/case-study-cosmos/>)

Author: info@qdrant.tech (Andrey Vasnetsov)

Published: 2025-11-20T00:00:00Z

Content type: article

Language: en

Sources: [Qdrant Blog on Qdrant - Vector Search Engine](<https://devfeed.tech/sources/qdrant-blog-on-qdrant-vector-search-engine.md>)

Topics: [Cosmos](<https://devfeed.tech/topics/cosmos.md>), [Qdrant](<https://devfeed.tech/topics/qdrant.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [multimodal-ai](<https://devfeed.tech/topics/multimodal-ai.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [app](<https://devfeed.tech/tags/app.md>), [approximate-nearest-neighbor-search](<https://devfeed.tech/tags/approximate-nearest-neighbor-search.md>), [bert](<https://devfeed.tech/tags/bert.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cosmos](<https://devfeed.tech/tags/cosmos.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fasttext](<https://devfeed.tech/tags/fasttext.md>), [hnsw](<https://devfeed.tech/tags/hnsw.md>), [image-search](<https://devfeed.tech/tags/image-search.md>), [knn-algorithm](<https://devfeed.tech/tags/knn-algorithm.md>), [latency](<https://devfeed.tech/tags/latency.md>), [matching](<https://devfeed.tech/tags/matching.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [performance](<https://devfeed.tech/tags/performance.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [qdrant](<https://devfeed.tech/tags/qdrant.md>), [recommender-system](<https://devfeed.tech/tags/recommender-system.md>), [saas](<https://devfeed.tech/tags/saas.md>), [search](<https://devfeed.tech/tags/search.md>), [simaes-networks](<https://devfeed.tech/tags/simaes-networks.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [transformer](<https://devfeed.tech/tags/transformer.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>), [vector-search-engine](<https://devfeed.tech/tags/vector-search-engine.md>), [vectors](<https://devfeed.tech/tags/vectors.md>), [word2vec](<https://devfeed.tech/tags/word2vec.md>)

## AI overview

This case study explains how Cosmos, a visual search app, uses Qdrant Cloud to provide text, color, hybrid, and similarity search across millions of creative assets. It describes the move from Postgres with pgvector to a managed vector search layer to support multimodal results, metadata filtering, recommendations, and real-time performance.

## Source excerpt

Cosmos is redefining how people find inspiration online. It's a visual search app built for creative professionals and everyday users who want a clean, meditative, ad-free place to collect and curate ideas. In contrast to feeds dominated by doomscrolling, ads, and generative "AI slop," Cosmos focuses on high-quality, human-made content. AI-powered search and captions connect each image to its creator, making visual discovery richer, more accurate, and easier to navigate.