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Section 1. Budgeting, spend patterns, and cost controls

Scenario 1 — Context: AI costs doubled; What changed: check cost signals and usage; Your decision: adjust routing and budgets; What good looks like: costs under control; Common mistakes: ignoring cost signals

Scenario 2 — Context: Vendor LLM spend spikes; What changed: adjust quotas or reroute; Your decision: cap or route to cheaper path; What good looks like: cost stays within plan; Common mistakes: cutting capability too much

Scenario 3 — Context: Data heavy inference; What changed: batching and caching options; Your decision: enable smarter batching; What good looks like: lower cost with preserved quality; Common mistakes: over-optimizing at the expense of latency

Budgets, quotes, and approval workflows

Cost-aware routing and autoscaling guards

Spend dashboards and cost accountability across teams

What good looks like: sustainable token economy and spend discipline

🎮 Crossword: AI Financial Terms

Section 2. Token economics, quotas, and governance

Token ceilings, quotas, and throttling concepts

Understanding Autoscaling’s Impact on Your Budget

Cost tracking and reporting to leadership

Supply chain costs and vendor risk awareness

Reviewing ROI and value realization

Policies: how to enforce cost discipline

Glossary: key terms and definitions

Knowledge Assessment

Quiz: Operational Scenarios

Quiz: Budgeting and Workflows

Quiz: Routing and Autoscaling

Quiz: Token Economics and Governance

Quiz: ROI and Reporting

Course Feedback

Course Survey

Running AI: Scaling from Pilot to Sustainable Production Services is a hands-on course for leaders and practitioners who need to turn promising AI pilots into reliable, cost-disciplined, and value-creating production systems. You will learn how to forecast and control spend, design token economies and quotas, and build governance that keeps performance high while budgets stay predictable. Through realistic scenarios—like sudden vendor LLM spikes, data-heavy inference, and autoscaling surprises—you will practice making the trade-offs between capability, latency, and cost that define successful AI operations. The program blends financial rigor with technical depth: cost-aware routing, batching and caching, autoscaling guardrails, dashboards, approval workflows, and leadership-ready reporting. By the end, you will have a clear blueprint for sustainable token usage, vendor risk management, ROI tracking, and policy enforcement across teams. If you are responsible for AI reliability, unit economics, or executive communication, this course equips you with the frameworks, tactics, and tools to scale with confidence and avoid the hidden traps that derail budgets and stakeholder trust.

Running AI: Scaling from Pilot to Sustainable Production Services

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            <p>Transitioning an Artificial Intelligence initiative from a controlled pilot environment to a full-scale production service presents significant operational and financial challenges. Organizations often encounter unexpected spend patterns, such as sudden cost spikes or vendor-specific surges, which can jeopardize the long-term sustainability of a project. This course provides a comprehensive framework for managing these complexities, focusing on the identification of cost signals, the implementation of cost-aware routing, and the establishment of rigorous budget controls. By analyzing real-world scenarios involving data-heavy inference and batching strategies, participants will learn to maintain service quality while ensuring that expenditures remain aligned with organizational objectives.</p>

<p>Beyond immediate cost management, the curriculum delves into the structural elements of AI governance, including token economics, quotas, and the financial implications of autoscaling. Professionals will gain the skills necessary to design approval workflows, interpret spend dashboards, and communicate Return on Investment (ROI) effectively to leadership. The material is specifically designed for students, solopreneurs, and corporate staff transitioning into AI-driven operational roles, offering the tools to navigate vendor risks and supply chain costs. Ultimately, this course equips learners with the discipline required to build a sustainable token economy and realize measurable business value from production-grade AI services.</p>
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