Inference Cost Levers
- Batch small requests; enable dynamic batching
- Use quantized or distilled models when acceptable
- Cache responses; tune top‑k/top‑p for LLMs to reduce compute
- Use streaming and early‑exit policies
Training Cost Levers
- Spot/preemptible instances with checkpointing
- Sharded training and mixed precision
- Data pruning and efficient curriculum
Architecture Tips
- Separate control plane and data plane; right‑size per workload
- Prefer serverless autoscaling for spiky traffic; reserved for steady loads
- Track cost per tenant/model/version; auto‑remediate anomalies