Projected savings before you commit
See estimated monthly and annual cost delta against your current model before you run a single test prompt.
FinOps Use Case
Identify lower-cost model alternatives backed by quantified quality and reliability impact — not gut feel.
The Problem
Most teams know they're overpaying for AI API calls but can't quantify the risk of switching. Evaluating cheaper alternatives manually means comparing spreadsheets without live pricing, benchmark, or uptime data.
Engineering teams often overpay for AI API calls because switching models feels risky. TokenPrice.dev's FinOps workflow quantifies the trade-off: projected monthly savings, quality delta, reliability delta, and a confidence score — so the decision is data-driven.
What You Get
See estimated monthly and annual cost delta against your current model before you run a single test prompt.
Benchmark scores and quality deltas are computed against live data so you know exactly how much quality you're trading.
30-day price volatility is factored in. You get a range, not a false-precision point estimate.
Profile once, regenerate briefs as prices change. No spreadsheets to maintain.
How It Works
Profile your workload
Enter requests/day, average token size, provider constraints, and your current model.
Generate a decision brief
Ranked candidates with projected savings, quality/reliability deltas, and a primary action.
Simulate before migrating
Shadow-run the candidate model to validate projections before full migration.
FAQ
Projections use live pricing data updated daily. Uncertainty bands reflect 30-day price volatility, so you see a realistic range rather than a point estimate.
Yes. Each decision brief ranks all viable candidates. You can drill into any candidate and run separate simulation tracks.
Use Cases
All FinOps use cases →
Model Data
Explore 350+ models →
Price Changes
Track price volatility →