AI Cost Academy
Reduce costs with engineering tactics
Prioritize the engineering changes that lower AI spend fastest without creating quality regressions or workflow drag.
Course goal
Choose two high-impact cost reductions for the next sprint.
Built for founding engineers, staff engineers, ml engineers. Work through the modules in order if you want the full picture, or jump directly to the lesson that matches the job in front of you right now.
LLM cost optimization playbook
Prioritize prompt compression, caching, smaller models, batching, and retrieval optimization with a clear savings vs effort ranking.
RAG vs fine-tuning cost tradeoffs
Choose RAG, fine-tuning, or full-context for knowledge-heavy or behavior-heavy workloads with a clear cost and maintenance comparison.
Switching to cheaper AI models without losing quality
Use a practical evaluation loop to cut cost while protecting user-facing quality and latency.
Track AI cost by feature, team, and customer
Make optimization work measurable by tying spend to product features, teams, and customer segments.
LLM spend tracking for product teams
Use feature-level and workflow-level analysis to see where optimization work will move margin the most.