Enterprise Token Budgets: AI Governance

In an enterprise environment, "Cost" is not just about the total API bill. It's about Accountability. Which department spent the budget? Marketing? Engineering? Customer Support?

Without Token Budgets, internal AI adoption often stalls because the Finance department cannot predict the costs. To scale AI within a large company, you must treat tokens as a Billable Utility.

In this final lesson of Module 16, we learn how to implement Departmental Quotas, Chargeback Models, and Policy-Based Throttling.

---

1. The Chargeback Model

A Chargeback system attributes costs back to the internal business unit.

The Implementation:

1. Every API call must include a Department-ID in the metadata.
2. The monitoring layer (Module 16.3) aggregates usage by Department.
3. At the end of the month, the "Token Bill" is split according to usage.

---

2. Hard vs. Soft Budgets

• Soft Budget (Alert only): "Marketing has reached 80% of their $1,000 monthly limit. Send an email to the CMO."
• Hard Budget (Kill switch): "Engineering has reached $5,000. Disable all Tier 3 models until next month. Allow Tier 1 only."

ROI: Hard budgets prevent "Experimental Debt," where a developer accidentally leaves a recursive agent loop (Module 9.1) running over the weekend on the corporate credit card.

---

3. Implementation: The Budget Policy Engine (Python)

Python Code: Enforcement Logic

DEPT_LIMITS = {
    "MARKETING": 1000.0, # Dollars
    "ENGINEERING": 5000.0,
    "HR": 200.0
}

current_spend = {"MARKETING": 950.0, "ENGINEERING": 2000.0}

def authorize_agent_request(dept_id, estimated_cost):
    limit = DEPT_LIMITS.get(dept_id, 0)
    current = current_spend.get(dept_id, 0)
    
    if current + estimated_cost > limit:
        # BUDGET EXCEEDED POLICY
        return "REJECT" # Or "DOWNGRADE_TO_CHEAP_MODEL"
        
    return "APROVE"

---

4. Selling Efficiency as a "Feature"

In B2B SaaS, you can sell "Token Efficiency" as a premium feature.

• Silver Plan: Standard agents. (High delay, standard token usage).
• Gold Plan: Optimized agents with Prompt Caching and Thin-Context enabled. (Faster, cheaper, more sustainable).

By exposing token efficiency to the customer, you turn a Technical Cost into a Business Benefit.

---

5. Visualizing the Enterprise Fleet (React)

An Enterprise Admin Dashboard should show the "Financial Health" of the organization's AI.

pie title "Spend by Department"
    "Marketing" : 45
    "Engineering" : 30
    "Support" : 20
    "HR" : 5

---

6. Summary and Key Takeaways

1. Attribution is Governance: Every token should have an owner.
2. Hard Caps Save Careers: Prevent accidental multi-thousand dollar weekend billing spikes.
3. Chargebacks: Align AI costs with the departments that benefit from them.
4. Efficiency as a Product: Market your optimized context windows as a "Sustainability" or "Speed" benefit to enterprise clients.

---

Exercise: The Departmental Audit

1. Imagine a company with 2 departments: Sales and Support.
2. Sales uses AI to write personalized emails (Low volume / High value).
3. Support uses AI to answer common questions (High volume / Low value).
4. Design a Budget Policy:
   • Which department gets the higher "TPM" (Token Per Minute) limit?
   • Which department gets access to "Expert Models"?
   • Conclusion: Usually, Sales needs Experts, and Support needs high-volume Flash models. Setting these quotas correctly maximizes the company's total AI ROI.

---

Congratulations on completing Module 16! You are now an enterprise AI leader.