Modeler
Scenario Studio for AI cost and margin
Scenario Studio for AI cost and margin is built for teams whose AI costs include retries, caching, routing, seats, gateway fees, and taxes. Use it to decide how the full scenario behaves when several cost layers compound at once. Keep the workload assumptions consistent across options, then inspect the cited prices and last-checked dates before committing budget.
Open Scenario Studio - Model the whole workload →
The decision this page helps you make
Model seats, retries, prompt caching, fallback routing, gateway fees, and tax into one monthly AI cost, then compare against a subscription. Save and share.
The practical question is how the full scenario behaves when several cost layers compound at once. Use the same workload assumptions for every option so the comparison reflects billing differences instead of different inputs.
Start with these inputs
- Workload: Seats, requests, tokens, retries.
- Optimization: Caching, batch, fallback routing.
- Finance view: Monthly total, subscription comparison, shareable scenario.
What the result includes
| Area | What ByteCosts shows |
|---|---|
| Workload | Seats, requests, tokens, retries |
| Optimization | Caching, batch, fallback routing |
| Finance view | Monthly total, subscription comparison, shareable scenario |
How to use the result
- Run a realistic base case and a heavier-usage case before choosing a provider or plan.
- Compare alternatives with identical traffic, token, seat, runtime, and retry assumptions.
- Open the cited provider source before a purchase or production billing decision.
Formula
scenarioCost = modelCost + subscriptionCost + gatewayFees + taxes, where modelCost includes seats, requests, token mix, cache hit rate, batch share, and retries.
Assumptions
- All scenario inputs are explicit and remain in the browser unless the user shares a URL.
- Model prices come from committed data and do not refresh at runtime.
- Gateway fees, taxes, and subscriptions are included only when the user adds them.
- Shared URLs should avoid sensitive customer or internal volume details.
Example scenario
Model a team workload with seats, daily requests, input/output tokens, retry overhead, and a gateway fee. Save the scenario URL so finance and engineering compare the same assumptions.
How to read the example
| Step | Example input | What to inspect |
|---|---|---|
| Base layer | Seats, requests, input tokens, output tokens | Raw model cost |
| Adjustment layer | Cache, retries, batch share, gateway fee | Compounded monthly total |
| Finance layer | Subscription comparison and tax | Plan-vs-API budget readout |
Interpretation guide
- Use scenarios to compare one workload across models, not to declare a universal winner.
- Duplicate a scenario before changing a single driver such as retry rate or cache hit rate.
- Treat large swings as signals to instrument the real workload before committing spend.
Limitations
Scenario Studio for AI cost and margin is a planning tool, not a billing guarantee. It uses the visible assumptions and committed source-backed data available at the page's last update.
Check the cited provider page and your own production logs before signing a contract, changing price, or committing infrastructure spend.
Frequently asked questions
What should I enter first in Scenario Studio for AI cost and margin?
Start with workload: seats, requests, tokens, retries. Add optional adjustments only after the base case is understandable.
Is the result a guaranteed invoice forecast?
No. It is a planning estimate based on the visible workload assumptions and source-backed public prices. Taxes, negotiated discounts, undocumented limits, and production behavior can change the final invoice.
Where do the prices and assumptions come from?
ByteCosts keeps provider source links, confidence information, and last-checked dates attached to pricing records. User-entered workload assumptions remain separate from published vendor facts.
Scenario Studio for AI cost and margin. ByteCosts. https://bytecosts.com/tools/scenario-studio/