Module 19: Cost Optimization & FinOps for AI Systems
🚀 Problem Statement
A GenAI platform might incur costs of $180K/month, with a breakdown such as: $80K for LLM inference, $40K for GPU instances (embedding + re-ranking), $30K for cloud storage (vector DB + blob), and $30K for compute (K8s). Management often seeks a significant cost reduction (e.g., 40%) without degrading the user experience.
🧠The Engineering Story
The Villain: "The Unlimited Budget Myth." During initial development, expensive models like GPT-4 might be used for all queries without caching, batching, or model routing. Every query — even simple ones — then incurs high costs.
The Hero: "The Cost-Aware Architecture." Every LLM call has a cost tag. Semantic caching eliminates 40% of calls. Model routing directs 70% of queries to cheaper models. Dynamic batching reduces GPU instance requirements.
The Plot:
- Implement per-query cost tracking: embed cost, retrieve cost, generate cost
- Semantic caching layer (40% hit rate = 40% cost reduction)
- Model routing: classify intent → route to cheapest adequate model
- Spot instances for batch workloads, reserved for steady-state
- Right-size vector DB: move cold embeddings to cheaper storage
The Twist (Failure): The Quality Cliff. Reducing costs too aggressively (e.g., by 50%) can cause P95 answer quality to drop significantly. If users stop trusting the system, support tickets will spike, and the "savings" could be offset by lost productivity.
Interview Signal: Can present a cost optimization plan with quantified trade-offs.
🧠Cost Optimization Levers
| Lever | Savings | Risk | Implementation Effort |
|---|---|---|---|
| Semantic cache | 30-40% LLM cost | Stale responses | Medium |
| Model routing | 40-60% LLM cost | Quality degradation on edge cases | Medium |
| Dynamic batching | 50-70% GPU cost | Slight latency increase | Low |
| Spot instances | 60-80% compute for batch | Job preemption / interruption | Medium |
| Prompt optimization | 20-30% token cost | Prompt engineering iteration time | Low |
| Embedding model distillation | 50% embedding cost | Small accuracy drop | High |