Author: Alexander Onbysh

In this post, we share Ring’s billion-scale semantic video search on Amazon RDS for PostgreSQL with pgvector architectural decisions vs alternatives, cost-performance-scale challenges, key lessons, and future directions. The Ring team designed for global scale their vector search architecture to support millions of customers with vector embeddings, the key technology for numerical representations of visual content generated by an AI model. By converting video frames into vectors-arrays of numbers that capture what’s happening (visual content) in each frame – Ring can store these representations in a database and search them using similarity search. When you type “package delivery,” the system converts that text into a vector and finds the video frames whose vectors are most similar-delivering relevant results in under 2 seconds.