Why Latency Kills AI Features

AI features live and die by latency. When injecting large context windows into LLM prompts, standard database calls across continents become the bottleneck — adding hundreds of milliseconds before the model even starts generating.

The Edge Database Landscape

  • Turso — libSQL at the edge, SQLite-compatible, sub-10ms reads globally
  • Neon — serverless Postgres with instant branching for preview environments
  • Upstash — Redis-compatible, per-request billing, ideal for caching embeddings
  • PlanetScale — MySQL-compatible with non-blocking schema changes

Vector Embeddings at the Edge

Edge-native databases and globally distributed caching allow developers to fetch vector embeddings and user state closer to the user, keeping application responses crisp and sub-second.

// Fetching nearest embedding vectors from edge
const results = await db
  .select()
  .from(embeddings)
  .orderBy(cosineDistance(embeddings.vector, queryVector))
  .limit(5);

"Every 100ms of latency costs 1% in conversions — and AI apps are even more sensitive."