SiliconMesh — Workload Placement & TCO Orchestration for Multi-Architecture AI Fleets

The Problem

AI infrastructure strategy used to sound simpler:

• pick a cloud,
• choose a few instance types,
• optimize price/performance,
• and scale mostly in one direction.

That assumption is breaking.

Now teams increasingly need to decide:

• which workloads belong on cloud CPUs versus custom accelerators;
• which models should run on training-oriented versus inference-oriented silicon;
• how to route latency-sensitive agent steps differently from batch-heavy pipelines;
• when networking or packaging constraints erase the headline gains of a new chip;
• and how to explain total cost of ownership across a fleet that keeps becoming more heterogeneous.

Most teams do not have software that sits above this complexity. They have dashboards, benchmark notebooks, procurement spreadsheets, and strong opinions from infra leads.

The opportunity is not to build another chip. It is to build the decision and orchestration layer for multi-architecture AI fleets.