Why the llm model routing savings calculator matters
Routing is the highest-leverage AI cost lever available because most production prompts are trivial classification and formatting work that never needed a frontier model. This page turns that decision into a handful of inputs you can defend in a budget review: volume, unit cost, rate of adoption, and time. The output is a planning baseline, not a promise — it tells you whether the idea deserves a vendor quote, a pilot, or a pass.
- • Biggest swing factor: the share of calls a small model can handle
- • Second-order factor: frontier-model call cost
- • Often ignored: eval tooling, which you need to keep the router honest
What actually changes the answer
the share of calls a small model can handle moves this number first, then frontier-model call cost. Run a conservative case and an upside case before you commit. If the maths only works in the upside case, treat it as a time-boxed test with a kill date rather than a line in next year's plan.
What to do with the result
Start with the top three prompt templates by volume, route those, and measure quality with a fixed eval set. Expand the routed share only while eval scores hold.
Related guides
Long-form playbooks on the same topic, written by the RevenueLab editorial team.
FAQ
What does the llm model routing savings calculator work out?
It applies Net savings = (volume × deflection rate × cost per item) − tool cost to the values you enter for llm calls per month, share routed to the cheap model, cost per frontier-model call, router + eval tooling per month. Routing is the highest-leverage AI cost lever available because most production prompts are trivial classification and formatting work that never needed a frontier model.
How accurate is this llm model routing savings calculator?
Treats the cheap model as free relative to the frontier model, which slightly overstates savings — subtract the small model's cost from your per-call figure for a stricter answer. Replace the defaults with your own invoice, usage export, payroll data, statement, or vendor quote before making a commitment — the maths is exact, so the answer is only as good as the inputs you feed it.
Which input should I stress-test first?
the share of calls a small model can handle. Re-run with a pessimistic value for it; if the decision flips, that assumption is the thing you need real data on before signing anything. After that, check frontier-model call cost and eval tooling, which you need to keep the router honest.
Which scenario should I start from?
Start with the preset closest to your situation — lean case, expected case, scaled case — then edit the sliders. Presets are realistic starting points, not benchmarks to match, and every change updates the result instantly.
What should I do after running the numbers?
Start with the top three prompt templates by volume, route those, and measure quality with a fixed eval set. Expand the routed share only while eval scores hold. A useful planning benchmark to compare against: Most production apps can route 50–75% of calls to a small model with no quality complaint.
Can I share or save this calculation?
Yes. Your inputs are written into the page URL, so copying the link shares the exact scenario you are looking at — the person who opens it sees the same numbers. You can also export the inputs and results to CSV or PDF from the result card and keep it with the rest of your workings.
How this calculator is built
Independently maintained
Written by Sam Doshi and the RevenueLab editorial team. We don't sell the data feeds this tool is built on.
Sourced from primary data
Benchmarks come from public AdSense / Stripe / IRS disclosures and reader-submitted data — never third-party "$X per view" claims. Full methodology.
Last editorial review
Reviewed on a rolling quarterly cycle. Dated reviews are published on the methodology record for each calculator.
Editorial standards
See our editorial policy and disclaimer. Results are estimates, not advice.