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As organizations deploy more AI agents, controlling what those agents can access is only part of the challenge. They also need to control what agents consume.
Xage Security has added token metering to Xage Zero Trust for AI, giving organizations real-time visibility and policy-based control over Large Language Model (LLM) usage and cost. Teams can track consumption, attribute it to the identities generating it, set budgets and quotas, route requests, or stop requests that exceed defined limits.
What You’ll Learn
- How token metering provides visibility into AI usage and cost
- How Xage helps prevent runaway AI consumption
- Why AI access and consumption should be governed together
Bring AI Consumption Into the Control Plane
As AI adoption scales, token consumption can become difficult to predict. A misconfigured agent, unexpected workload, or agent caught in a loop can quickly drive up usage and cost. Provider bills can show what was spent, but often provide limited context into the users, agents, teams, or projects responsible for that consumption.
Token metering gives organizations a more granular way to track, measure, and control that activity. Xage measures input and output tokens and translates usage into estimated cost based on the underlying model and provider pricing.
Organizations can attribute consumption to individual users, AI agents, teams, departments, projects, and applications, helping security, finance, and product teams understand exactly where AI resources are being consumed.
Stop Runaway Usage Before It Becomes Runaway Spend
Visibility alone does not prevent an AI agent from exceeding its intended limits. Xage adds enforcement directly into the path between identities and LLMs.
Administrators can:
- Set token quotas and cost budgets per period
- Track input and output token consumption
- Attribute usage and cost to specific identities and teams
- Apply limits to specific models
- Monitor consumption or enforce hard limits
- Block requests that would exceed defined budgets or optionally route them to cheaper models
- Audit AI consumption and enforcement activity
Before a request reaches an LLM, Xage can estimate its expected consumption and evaluate it against the configured policy. If the request would exceed an enforced limit, Xage can block it before it reaches the model. Actual consumption is then recorded when the response returns. The associated cost is then deducted from the allocated budget to determine the remaining budget for the period.
One Policy Layer for AI Access and Consumption
Token metering is part of the Xage Resource Gateway, which means organizations can govern AI consumption alongside access to enterprise resources, tools, and APIs.
Instead of treating cost management and AI security as separate problems, Xage enables organizations to control both what an AI identity can access and how much it can consume through the same Zero Trust architecture.
This becomes increasingly important as AI agents move beyond simple chatbot interactions and begin taking actions across enterprise systems. Least privilege should apply not only to an agent’s permissions, but also to the resources it is allowed to consume.
Key Takeaways
- Token metering provides real-time visibility into AI usage and cost.
- Usage can be attributed to users, agents, teams, projects, and applications.
- Budgets, quotas, and hard limits help prevent runaway consumption.
- Provider-agnostic metering creates a consistent view across AI models and services.
- Xage brings AI consumption and resource access into the same Zero Trust control plane.
Take Control of AI Usage
As AI agents become more autonomous, organizations need enforceable boundaries around both access and consumption. Token metering extends Xage Zero Trust for AI with the visibility, attribution, and controls needed to keep AI usage within those boundaries.
Frequently Asked Questions
What is AI token metering?
AI token metering tracks and measures the tokens consumed when users or AI agents interact with Large Language Models (LLMs). Xage also translates token usage into estimated cost and attributes consumption to specific users, agents, teams, departments, projects, or applications.
How does token metering help control AI costs?
Xage lets administrators establish token quotas and cost budgets and monitor consumption against those limits. Organizations can use monitoring mode for visibility,enforce hard limits that block requests when they would exceed a defined budget, or route requests to cheaper models when limit is reached.
Can Xage track token usage across different AI models?
Yes. Xage provides provider-agnostic token metering designed to normalize usage across providers such as OpenAI, Anthropic, and Google. This gives organizations a consistent view of consumption instead of relying on separate provider-specific metrics.
How does token metering fit into Zero Trust for AI?
Token metering extends Zero Trust controls from what an AI identity can access to how much it can consume. Because the capability is integrated with the Xage Resource Gateway, organizations can use a single control point to govern both LLM access and consumption while enforcing identity-based access policies for enterprise resources, APIs, and MCP-connected tools.
