Yes, if the product uses variable-cost AI workloads or agentic workflows. Starting with a governed usage model is easier than retrofitting one after adoption creates pricing debt. Early metering also makes it possible to align cost, entitlement, and auditability before the user base scales.
Why This Matters for Security Teams
AI product teams that add credits late usually discover they are not just billing a feature, they are governing access to variable-cost infrastructure, model calls, and often sensitive tool execution. When credits are absent at launch, spend, abuse, and entitlement all get tracked in separate systems, which makes auditability and customer limits hard to enforce. That is especially risky for agentic workflows, where one user action can trigger many downstream calls and token consumption. The Ultimate Guide to NHIs — The NHI Market frames this as an identity and governance problem, not just a finance problem, and NIST Cybersecurity Framework 2.0 reinforces that resilience depends on clear governance, control, and monitoring from the start.
For AI products, credits also help separate legitimate usage from abuse. A governed usage model can limit blast radius if an API key is leaked, if a workflow loops unexpectedly, or if a tenant begins consuming far more model capacity than intended. In practice, many security teams encounter pricing debt only after adoption has already made entitlement fixes painful, customer-facing, and operationally disruptive.
How It Works in Practice
The practical approach is to treat credits as a policy layer, not just a billing counter. Credits can define what a user, tenant, or workload is allowed to do, how much, and under what conditions. That means tying metering to product actions such as prompt submissions, tool calls, workflow steps, and agent task completions. In mature implementations, credits also connect to audit logs so each expensive or sensitive action has a traceable entitlement decision.
Security teams should align the credit model with identity and runtime controls. For example, user-facing plans may map to entitlements, while machine actions use workload identity and short-lived tokens. That makes it easier to apply least privilege, per-tenant quotas, and just-in-time access for agentic workflows. The goal is to prevent unconstrained spending and uncontrolled capability drift at the same time.
- Meter at the right level: request, task, workflow, or tenant, depending on product risk.
- Use short-lived enforcement logic so revoked credits stop new usage quickly.
- Log credit grants, consumption, and reversals for billing and security review.
- Separate trial, paid, and admin credits to avoid privilege confusion.
This is also where secrets and identity hygiene matter. NHIMG research on the LLMjacking: How Attackers Hijack AI Using Compromised NHIs report shows how quickly attackers exploit exposed credentials, while the broader secrets landscape in The State of Secrets in AppSec shows how fragmented secrets handling complicates control. These controls tend to break down when credits are tracked separately from runtime identity because abuse can continue even after the billing layer looks correct.
Common Variations and Edge Cases
Tighter credit controls often increase product friction, so organisations need to balance user experience against cost containment and abuse prevention. That tradeoff is especially visible in developer tools, sandbox environments, and autonomous agents, where rigid quotas can interrupt legitimate experimentation.
Best practice is evolving for AI products that blend subscriptions, usage-based pricing, and agent execution. Some teams use hybrid models with included credits, overage fees, and hard stops for high-risk actions. Others reserve credits only for model inference while leaving orchestration free, but that can hide real cost drivers when tool chains or retrieval loops expand.
There is no universal standard for this yet, but the pattern should be consistent: credits should reflect meaningful risk and cost boundaries, not arbitrary marketing tiers. If an agent can call tools, browse data, or trigger external actions, credits should help limit both spend and capability. The hardest edge case is internal enterprise deployment, where teams may want near-unlimited usage for adoption but still need strict audit trails and per-tenant containment.
For teams designing this from day one, the safest path is to make credits a first-class control that can be enforced, revoked, and audited independently of the UI.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.1 | Credits need governance, ownership, and policy decisions from launch. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Usage credits should pair with short-lived credentials and rotation control. |
| NIST AI RMF | AI RMF fits runtime governance for cost, access, and accountability. |
Bind credit enforcement to ephemeral credentials and rotate access on task completion.
