A developer on a metered AI coding assistant can burn more than $600 a month just to hold their old pace, with a single API call metering 55,000 tokens before any work begins.
Connect more than 10 or 20 MCP servers and the model's context window fills before the conversation even starts. Context rot then adds cost and erodes reasoning quality inside every long session, and none of it shows up on a monthly invoice.
N-iX's Yaroslav Mota, Director and Head of Corporate AI & Efficiency, lays out the four-layer sequence for enterprise AI spend: measure a token baseline, govern MCP access through a centralized gateway, build context and output discipline (cut noise by up to 65%), then route tasks to the right model.
Discover how to measure, govern, and route enterprise AI spend before it compounds: get the full framework in this guide!
Enterprise tools load 55,000 tokens before a message is read. Spend compounds fast—get the four-layer fix in this guide!
Major enterprises burn through their 2026 AI budget in four months. Get the four-layer framework for token governance and model routing in this guide!
