Growth forecast
| Scenario | Monthly Cost |
|---|---|
| Current usage | — |
| +25% growth | — |
| +50% growth | — |
| +100% growth | — |
| Annual projection | — |
| 3-year projection | — |
Calculate context window utilization, remaining tokens, overflow risk, and safe output capacity.
| Scenario | Monthly Cost |
|---|---|
| Current usage | — |
| +25% growth | — |
| +50% growth | — |
| +100% growth | — |
| Annual projection | — |
| 3-year projection | — |
| Metric | Value |
|---|---|
| Main result | — |
| Monthly / unit metric | — |
| Annual / secondary metric | — |
| Status | — |
Context utilization shows how close the request is to the model limit. Higher utilization increases truncation or failure risk.
Leave buffer for system overhead, tool messages, citations, and unexpected model output.
With a 128k context window and 50k total used tokens, utilization is about 39%, leaving a large safe zone.
Context window usage measures how much of a model’s maximum token capacity is consumed by prompts, retrieved text, history, and output reserve.
The calculator combines your usage assumptions with editable prices, fixed costs, and volume assumptions to estimate cost, savings, or capacity.
It is a planning estimate. Actual bills can differ because providers change prices, apply tiers, add taxes, or bill extra features separately.
The largest drivers are usage volume, output length, tool calls, review work, fixed platform costs, and the price per unit you enter.
Reduce unnecessary tokens, batch low-priority work, use smaller models where possible, cache repeated context, and review provider pricing regularly.
Common mistakes include ignoring retries, forgetting fixed monthly costs, using outdated token prices, and assuming every task needs the most expensive model.
Use it before launching, scaling, or changing an AI workflow so you can estimate budget impact before real usage grows.
| Scenario | Estimate |
|---|---|
| Current usage | — |
| +25% usage | — |
| +50% usage | — |
| +100% usage | — |