Foundation Model Cost Optimisation — Reduce AI Spend

I will audit and optimise your foundation model spending — implementing model routing (using smaller, cheaper models for simple queries and frontier models only for complex ones), prompt token compression, semantic caching for repeated or similar queries, batch API usage for non-real-time workloads, and a cost monitoring dashboard. Foundation model costs without active management scale linearly with usage, regardless of whether more capable (and expensive) models are genuinely needed for each query — a well-implemented routing system that sends 70% of queries to a small efficient model and 30% to a frontier model typically costs 40-60% less than sending all queries to the frontier model with minimal quality difference.

The optimisation covers query complexity classification for routing, small vs. frontier model routing implementation, semantic cache with embedding-based similarity matching, batch API configuration, cost monitoring dashboard, and a before/after cost comparison.