Course Objective & Scope
Generative AI and agent applications that cost a few hundred dollars in a pilot can spiral into unpredictable bills at enterprise scale due to bloated context windows, multi-step agent loops and unthrottled API calls. This course adapts the FinOps discipline to LLM and AI workloads so costs become visible, predictable and tied to unit business value.
Token pricing mechanics, prompt caching, tiered model routing, RAG/vector database costs and GPU capacity commitments are worked through with real calculators. Participants build an AI unit-economics model, a budget-alert policy and an AI FinOps dashboard blueprint for their organisation.
Who Should Attend?
- FinOps, cloud cost management and IT budgeting teams
- AI platform, LLMOps and enterprise architecture managers
- Product owners of external AI products and internal enterprise assistants
- CFO office, technology procurement and financial control managers
Key Takeaways
- Calculate full AI unit cost per transaction, conversation or completed workflow
- Spot cost leaks driven by input/output token ratios, system prompt bloat and runaway agent loops
- Cut inference spend without losing quality using prompt caching, batch APIs and tiered model routing
- Optimise financially between pay-as-you-go APIs, Provisioned Throughput (PTU) and reserved GPU capacity
- Establish unit chargeback/showback tags, token quotas and anomaly circuit-breakers
Curriculum
Token Economics and the Anatomy of AI Spend
- Price differentials across input, output and reasoning tokens
- Hidden costs of RAG: embeddings, reranking, vector indexing and guardrail calls
- Compounding token consumption in multi-step agent workflows (tool calls, retries, reflection)
AI Unit Economics and Budget Forecasting
- Moving from "price per million tokens" to "cost per resolved ticket or completed task"
- 12-month budget forecasting across user adoption, query frequency and context growth
- Detecting negative-margin scenarios where inference cost exceeds business value
Architectural and Operational Cost Optimisation Levers
- Prompt compression, context pruning, prompt caching and semantic caching
- Tiered model routing: sending routine tasks to small models and complex ones to frontier models
- Using Batch APIs for asynchronous workloads and fine-tuned small models at scale
Purchasing Models: Pay-as-you-go, PTU and GPU Commitments
- Break-even analysis between pay-as-you-go APIs and Provisioned Throughput (Azure PTU, Bedrock, Vertex)
- Lowering unit cost via high-utilisation reserved GPUs and vLLM/SGLang serving
- Enterprise commit discounts and multi-provider negotiation strategies
AI FinOps Governance, Chargeback and Cost Guardrails
- Tagging by business unit, project and workflow for showback/chargeback reporting
- Enforcing rate limits, budget caps, agent max-iteration breakers and anomaly alerts at the LLM Gateway
- Hands-on lab: building a FinOps optimisation plan for an enterprise assistant and agent workload
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