Building the Database for Trillion-Scale AI Search

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Издатель Jun 17, 2026

[2026 - Day 1 - Data Eng & Databases] This talk goes beyond architecture diagrams to share what actually happens when you operate an agentic search engine on trillions of documents. We'll dig into how an object storage-native design allows a small team of engineers to manage an AI search engine that scales to:

• Peak load of 1M+ writes per second and 30k+ searches per second
• 1+ trillion documents
• 5+ PB of logical data
• 400+ tenants
• p90 query latency less than 100 ms

Topics include:
• How using a modern storage architecture decreases COGS by 10x or more
• Optimizing traditional vector and FTS indexes for the high latency of object storage
• Building search algorithms that are fine-tuned for LLM-initiated searches
• A simple rate-limiting technique that provides strong performance isolation in multi-tenant environments
• Observability, reliability, and performance lessons learned from production incidents.

Viewers will leave with a concrete understanding of how separating storage from compute-and treating object storage as the primary database changes not only the cost structure, but the entire operational model of large-scale AI search.

SPEAKER:
Nikhil Benesch - CTO, turbopuffer