Inference depends on model assets, customer data, retrieval systems, event streams, observability, billing, and analytics. When each workload runs on separate infrastructure, providers carry the added cost of integration, capacity management, protection, and operations.
VAST AI Operating System for Hyperscale AI Inference
Improve the Economics of Hyperscale AI Inference
Run hyperscale AI inference on a unified data platform that simplifies the infrastructure behind models, events, analytics, and operations, helping providers scale more efficiently as workloads, regions, and GPU fleets continue to grow.
The Hyperscale AI Inference Challenge
AI Inference Economics Carry the Cost of Data Complexity
Independent Capacity Planning
Each data platform needs its own headroom, replication, and growth plan. Capacity cannot be shared, making expansion more expensive and harder to predict.
Vectors Create a Second Data Plane
When embeddings live apart from source data, teams must synchronize metadata, permissions, lineage, retention, and deletion across two systems.
Event History Spans Two Stores
Request events flow through Kafka for live operations, then into a warehouse for SQL analysis. The same history consumes write bandwidth and capacity in two systems, with separate retention and replay paths.