Lilly Tech Systems
Designing AI Cost Optimization
Take control of your AI infrastructure and API spending with battle-tested optimization strategies. This course covers the complete cost stack — from GPU instance selection and inference cost reduction to API budget management, training optimization, and real-time cost monitoring. Every lesson includes production code, real pricing data, and actionable strategies used by engineering teams managing $10K to $1M+ monthly AI budgets.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. AI Cost Landscape
Where AI costs come from (training, inference, storage, data), cost breakdown by component, real cost examples from $1K/month to $1M/month, and building a cost awareness culture.
2. GPU Cost Optimization
On-demand vs spot vs reserved pricing across AWS/GCP/Azure, right-sizing GPU instances, GPU sharing with MIG and time-slicing, idle GPU detection, and cost comparison tables.
3. Inference Cost Reduction
Model quantization savings (FP32 to INT8 = 4x), model distillation, speculative decoding, request batching, caching (save 40-60%), tiered model routing, and $/request calculations.
4. API Cost Management
Token optimization strategies, prompt compression, cheaper model routing for simple queries, semantic caching, budget alerts, chargeback models, and a cost tracking system.
5. Training Cost Optimization
Spot instance training with checkpointing, mixed precision savings, curriculum learning, data subset selection, hyperparameter search budgets, and transfer learning cost analysis.
6. Cost Monitoring & Budgeting
Real-time cost dashboards, cost anomaly detection, budget enforcement, team/project cost allocation, FinOps for AI, Grafana dashboard examples, and alerting rules.
7. Best Practices & Checklist
Cost optimization checklist by category, ROI calculation for AI projects, when to stop optimizing, and comprehensive FAQ accordion.