Training programme overview
Understand and optimize AI-related costs: token usage, GPU expenses, storage, vector DBs, evaluations, model routing and caching, budgets, and unit economics.
Measuring and tracking consumption (tokens, GPU, storage, vector DB).
Cost models and unit economics to guide model selection decisions.
Routing, caching and model-selection strategies to lower expenses.
Budget forecasting and optimization scenarios based on real metrics.
Cost governance: limits, alerts and reporting for engineering teams.
Training · Intermediate
Courses included in this training programme
AI Cost Foundations
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.
4 lessons · 20 activities · 0 exercises · 3 videos
Token and Model Economics
Token and model economics: managing prompt length, context windows, caching, batching, model routing and quality/cost experiments to optimize spend and outcomes.
4 lessons · 24 activities · 12 exercises · 3 videos
Infrastructure Cost Optimization
Infrastructure cost optimization: managed API vs self-hosting cost comparison, GPU utilization and autoscaling, RAG storage and vector index costs, and observability cost management.
4 lessons · 24 activities · 12 exercises · 3 videos
Governance and Reporting
Governance and reporting for AI costs: budgets, alerts, approval workflows, chargeback/showback models, optimization backlogs and maintaining controls without blocking innovation.
4 lessons · 20 activities · 0 exercises · 3 videos
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