Forecast usage before quotas trigger throttling surprises. Model growth, caching, and concurrency with confidence today. Keep teams within budget using clear token planning rules.
Enter pricing, usage, and traffic assumptions. The form uses a responsive 3-column, 2-column, and 1-column layout by screen size.
Sample planning scenarios help validate your assumptions before using live production numbers.
| Scenario | Monthly Budget | Avg Input | Avg Output | Cache Hit | Users/Day | Req/User/Day | Planned Monthly Cost | Peak TPM | Budget-Safe User Quota |
|---|---|---|---|---|---|---|---|---|---|
| Pilot Support Bot | $300.00 | 1,200 | 500 | 40% | 150 | 6 | $237.17 | 5,610 | 9 requests |
| Internal Analyst Copilot | $1,200.00 | 900 | 350 | 55% | 800 | 12 | $1,372.36 | 46,667 | 14 requests |
| Customer Success Assistant | $5,000.00 | 1,500 | 700 | 65% | 3,000 | 8 | $4,594.05 | 165,000 | 13 requests |
Values are illustrative and assume model prices entered in the calculator. Replace them with your actual contract pricing for precise planning.
The calculator combines pricing, traffic, growth, and throughput assumptions into one planning model.
Uncached Input Tokens / Request = Avg Input Tokens × (1 − Cache Hit Rate)
Cost / Request = (Uncached Input ÷ 1,000,000 × Input Price) + (Cached Input ÷ 1,000,000 × Cache Price) + (Avg Output ÷ 1,000,000 × Output Price)
Daily Requests = Active Users per Day × Requests per User per Day
Planned Monthly Requests = Daily Requests × Days in Cycle × (1 + Growth %) × (1 + Headroom %)
Planned Monthly Cost = Cost / Request × Planned Monthly Requests
Peak RPM = (Daily Requests ÷ (Active Hours × 60)) × Peak Burst Factor
Peak TPM = Peak RPM × (Avg Input Tokens + Avg Output Tokens)
Budget-Safe Requests per User/Day = floor(((Monthly Budget ÷ Cost / Request) ÷ Days in Cycle) ÷ Active Users per Day)
These formulas help you set realistic quotas, estimate peak throughput, and catch budget or capacity problems before launch.
Follow these steps to build a reliable token quota plan for your product or internal AI workflow.
This calculator begins with the most reliable planning anchor: observed usage. Enter active users per day and requests per user to calculate baseline daily traffic, then extend it across the billing cycle. A team with 500 users and 6 requests generates 3,000 daily requests before growth assumptions. Keeping baseline demand separate from future planning makes reviews clearer and prevents budget discussions from being distorted by optimistic forecasts and seasonal noise in reporting periods.
Token cost planning works best when pricing is split by token type. The calculator estimates uncached input cost, cached input cost, and output cost independently, then combines them into cost per request. That structure reveals which behavior drives spend. If output tokens rise from 400 to 700, the monthly budget impact may be larger than expected. Testing several token-length scenarios creates a stronger range for procurement, forecasting, and quota decisions with confidence.
Growth and headroom are separate controls for different risks. Growth reflects expected adoption increases during the cycle, while headroom covers uncertainty, spikes, and operational variance. When both percentages are applied, the planner produces a multiplier that scales requests, tokens, and cost together. This method is stronger than adding a flat buffer later. Teams can justify percentages using trend data, release plans, campaign calendars, and previous peak days captured in production analytics and operations dashboards.
Quota planning must also respect throughput limits. The calculator estimates average request rate within active hours, then applies a burst factor to model peak requests per minute. It converts peak traffic into peak tokens per minute and compares that value with the provider TPM limit. This highlights throttling risk early. If utilization is too high, teams can improve caching, reduce output length, spread traffic, or request a higher service quota before launch.
The final outputs turn technical estimates into policy-ready numbers. Budget-safe daily requests, recommended requests per user, recommended tokens per user, and budget gap provide clear limits for product teams. CSV and PDF exports make approvals faster because stakeholders can review assumptions and results in one place. Recalculating weekly with fresh telemetry keeps quotas aligned with changing usage patterns, pricing updates, and model behavior across environments, regions, and deployment tiers.
Common planning questions for budget, quota, and capacity assumptions.
It converts budget, token pricing, usage, and throughput assumptions into quota-ready limits. You can estimate monthly cost, safe request caps, peak token demand, and export the results for reviews.
Use recent production logs or representative test prompts. Average at least one week of traffic, then separate input and output tokens. Recheck after feature launches because prompt templates and user behavior can change token length.
Use it when repeated prompts, system instructions, or reused context produce cacheable input tokens. If you are unsure, start conservatively and compare planned costs with actual billing before increasing the rate.
Growth models expected adoption increase. Headroom protects against uncertainty, spikes, and measurement error. Keeping them separate makes planning assumptions clearer and allows finance or engineering teams to approve each risk buffer independently.
Reduce tokens per request, lower per-user quotas, improve caching, spread traffic across more hours, or request a higher quota from the provider. The goal is to lower peak demand, not only monthly spend.
Update weekly for active products and after any pricing, model, prompt, or feature change. Frequent refreshes keep quotas aligned with telemetry and prevent budget drift or throttling surprises during busy periods.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.