How to cut vector search infrastructure costs at scale

Managing vector search infrastructure at scale creates complexity. AI retrieval pipelines often use separate systems for raw data, metadata, and embeddings, causing redundant state, costly replication, and synchronization overhead. RAM-dependent index architectures increase costs as vector counts grow.

This overview explores an object storage-native approach to vector search that decouples compute from storage, reducing infrastructure burden without sacrificing performance. Topics include:

• IVF-based indexing for partition-level loading
• Unified table for embeddings, metadata, and raw data
• Stateless compute nodes with durable object storage

Explore the tradeoffs in depth.