AI Cost Optimization

What you’ll learn
Understand your AI bill
Know what each token type costs and why
See how a short question turns into thousands of billed tokens
Do the napkin math on what a request costs

Track spend across your org
Put one gateway in front of all your AI calls
Break spend down by feature, team, and user
Find the one or two features driving most of the bill

Cut costs with caching
Arrange prompts so provider caches actually hit
Catch the small mistakes that quietly break caching
Answer repeat questions without calling the model

Keep agent context small
Learn why stale tool output is the first thing to cut
Summarize long histories without dropping key facts
Store agent memory outside the context window

Replace big prompts with small models
Understand how a small model can take over one high-volume job
Stop paying for the same long prompt on every call
Learn what breaks when the small model goes stale

Route requests to the right model
Send easy requests to cheap models and save the big ones
Build a router and its evals from scratch
Compare routers on cost and quality, not demos

Stop agents from burning money
Estimate multi-agent costs before you build
Catch loops early, before the retries pile up
Fix a broken research agent as your capstone

ACOEURTSTERYRDPOETYRTAYZFGERTERON

you must be registered member to see linkes Register Now