Multi-agent AI systems consume up to 15× more tokens than standard chat, creating unsustainable enterprise costs. A 25-agent deployment can cost nearly $200K/year in token costs alone due to fragmented data retrieval, low KV cache hit rates (~40%), and frequent context compaction events. Arango's Contextual Data Platform (AutoGraph, AutoRAG, Deep Search, ArangoDB multi-model) combined with NVIDIA's KV-cache infrastructure (CMX, Dynamo/AFD) addresses this from two directions: Arango reduces context window size by 43% through precision retrieval and stable prefixes, while NVIDIA's infrastructure raises cache hit rates from 40% to 92%. Together they cut token costs by 66%, saving over $500K annually for 100-agent deployments, while also improving AI decision accuracy by 20–35% and reducing integration complexity.
