AI Inference Cost Review overview

Reviews AI and LLM inference costs across the organization to optimize spend while maintaining quality -- analyzing API cost breakdowns by model, feature, and team with token-level granularity, evaluating prompt engineering efficiency by measuring token counts against output quality metrics, reviewing caching layer effectiveness including semantic cache hit rates and cost avoidance, assessing model selection appropriateness by comparing quality-to-cost ratios across model tiers for each use case, identifying opportunities to shift workloads from expensive frontier models to fine-tuned smaller models, tracking cost trends against usage growth to detect non-linear cost scaling, reviewing batch vs real-time inference allocation for latency-tolerant workloads, and benchmarking per-request costs against industry norms. Produces AI cost dashboard, optimization recommendation report, and model-tier allocation review. Excludes model training and fine-tuning.