Lilly Tech Systems
AI Infrastructure Cost Optimization
GPU cloud costs can spiral out of control without deliberate optimization. Learn to identify cost drivers, leverage spot and preemptible instances, right-size your infrastructure, implement FinOps practices, and build a culture of cost awareness across your AI organization.
What You'll Learn
Comprehensive strategies for reducing AI infrastructure costs without sacrificing performance.
Cost Drivers
Understand where your AI budget goes: compute, storage, networking, and managed services.
Spot Instances
Save 60-90% on training with spot/preemptible instances and fault-tolerant job design.
Right-sizing
Match instance types to workload requirements for optimal cost-performance ratio.
FinOps
Implement financial operations practices for AI: budgets, chargebacks, and optimization cycles.
Course Lessons
Follow the lessons to build a comprehensive AI cost optimization strategy.
1. Introduction
The AI cost challenge, why GPU spending grows exponentially, and the framework for optimization.
2. Cost Drivers
Breaking down AI costs: compute, storage, networking, data transfer, and managed service fees.
3. Spot/Preemptible
Leveraging spot and preemptible instances for training with checkpointing and fault tolerance.
4. Right-sizing
Matching GPU types, memory, and instance families to actual workload requirements.
5. FinOps
Implementing FinOps for AI: budgets, forecasting, chargebacks, and optimization workflows.
6. Best Practices
Comprehensive checklist and strategies for ongoing AI infrastructure cost management.
Prerequisites
What you need before starting this course.
- Experience with cloud billing and cost management tools
- Understanding of GPU instance types across cloud providers
- Basic knowledge of ML training and inference workloads
- Familiarity with cloud resource management (IaC, auto-scaling)