Luca BertonAI infrastructure costs are spiraling. GPU instances cost 10-50x more than standard compute. But regulated enterprises can't just move to the cheapest spot instance — compliance requirements constrain optimization options. Here's how to optimize within those constraints.
Where AI Infrastructure Money Goes
- GPU compute (60-70%) — Training and inference workloads
- Storage (15-20%) — Training data, model artifacts, logs, audit trails
- Networking (5-10%) — Data transfer, especially in multi-region setups
- Compliance overhead (10-15%) — Logging, monitoring, encryption, isolation
Optimization Strategies
Right-Size GPU Allocation
Most AI workloads use 30-40% of allocated GPU capacity. Implement GPU sharing (MIG for A100/H100, time-slicing for smaller GPUs) for inference workloads. Use profiling to match model requirements to GPU specs — not every model needs an H100.
Intelligent Workload Scheduling
Training jobs are batch workloads — they can tolerate scheduling delays. Use spot/preemptible instances for training (with checkpointing for interruption recovery). Keep inference on reserved instances for SLA compliance. Schedule training during off-peak hours for better pricing.
Model Optimization
Smaller models cost less to serve. Invest in: quantization (FP16, INT8), knowledge distillation, pruning, and model architecture optimization. A 4x smaller model doesn't just cost 4x less — it serves 4x more requests per GPU.
Tiered Storage for Compliance Data
Compliance requires long retention periods (7-10 years for financial services). Use tiered storage: hot storage for active audit trails, warm for recent history, cold/archive for long-term regulatory retention. Automate lifecycle policies.
Kubernetes Recipes
A practical guide for container orchestration and deployment by Grzegorz Stencel & Luca Berton (Apress).
Watch on Skillshare →The 30% Target
Consistently, we see 30% cost reduction in regulated AI infrastructure through right-sizing, scheduling optimization, and model optimization — without touching compliance controls. The key is measuring before optimizing. You can't cut what you can't see.