AI gateway governance: are your controls keeping up with agent spend?

TL;DR: AI gateway controls are now the main lever for managing model spend, routing, caching and governance as usage climbs, according to Tetrate, because caps alone miss shared costs, bypass paths and attribution gaps. The practical shift is from per-team limits to gateway-level financial accountability and policy enforcement, which changes how enterprises govern AI at scale.

Editorial analysis by NHI Mgmt Group, based on content published by Tetrate: “Your AI Bill Is an AI Gateway Problem: How Enterprises Cut Spend Without Cutting Usage”.

Key questions

Q: How should security teams govern AI models that can call tools and access data?

A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.

Q: Why do per-user AI caps often fail to control enterprise spend?

A: Per-user caps measure request volume, not true cost, so a few long-context or high-value calls can consume far more budget than many ordinary ones.

Q: What do teams get wrong about showback and chargeback for AI?

A: They treat showback as a reporting exercise instead of the foundation for financial accountability.

Practitioner guidance

• Standardise gateway-mediated model access Route all production AI traffic through one shared control plane so routing, caching, logging and budget rules are applied consistently.
• Create owner-based showback first Map AI spend to a named business owner before moving to chargeback, and require the attribution to cover the majority of usage before shifting budgets.
• Govern agent tools at the same layer Expose only approved tools through the gateway so model calls and MCP tool calls share the same policy boundary and audit trail.

Bottom line: AI gateway design is now a governance issue as much as a cost issue, because the same layer that routes requests also determines what can be observed and controlled.

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