AI FinOps and Cost Engineering Specialist

Foundations of AI costs: tokens, inference, GPU, storage and vector databases. Explains how these drivers create spend and how to analyze and attribute costs within a product or service context.

Breaks down major AI cost categories (tokens, inference, GPU, storage, vector DB) and how they interact.

Presents analytical methods to identify and prioritize cost-reduction levers.

Explains how to translate findings into reports usable by product and ops teams.

Token and model economics: managing prompt length, context windows, caching, batching, model routing and quality/cost experiments to optimize spend and outcomes.

Examines how prompt size and context windows affect token consumption and related costs.

Introduces operational techniques (caching, batching, summarization) and strategies for model routing and fallback.

Describes how to design and measure experiments to balance cost versus response quality.

Infrastructure cost optimization: managed API vs self-hosting cost comparison, GPU utilization and autoscaling, RAG storage and vector index costs, and observability cost management.

Provides a precise comparison of cost elements between managed and self‑hosted options to inform financial tradeoffs.

Explains GPU operating practices and autoscaling to maximize compute ROI.

Covers sizing and cost of vector indexes/RAG storage and how to run observability without overspending.

Governance and reporting for AI costs: budgets, alerts, approval workflows, chargeback/showback models, optimization backlogs and maintaining controls without blocking innovation.

Describes setting up budgets, alerts and approval flows tailored to AI spending.

Outlines chargeback/showback approaches to make costs transparent to stakeholders.

Presents how to organize an optimization backlog and practices to keep durable controls without stifling innovation.