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LLM Inference Token Budget Planner

The LLM Inference Token Budget Planner estimates the total number of input and output tokens a production workload may consume over a selected planning horizon. It combines expected task volume with average prompt and completion sizes, then adds explicit allowances for retries, failed calls, system prompts, tool traces, and other token overhead.

The planner is useful for API budget preparation, quota requests, internal chargeback, and early capacity discussions. Its result is a workload budget rather than a provider invoice: token counting rules differ by tokenizer and model, and cached or discounted tokens may be billed differently. Use measured averages from representative traffic whenever possible, then compare the planned total with actual usage after launch.

Workload assumptions

tasks
tokens
tokens
%
%
periods
Result
—
total tokens across the plan
Base workload tokens—
Retry and overhead allowance—
Budgeted tokens per task—
Million-token units—

1. Enter task volume
Use the number of inference tasks expected in one planning period.

2. Set token averages
Enter average input and output tokens per successful task from logs or a representative sample.

3. Add retry allowance
Include repeated calls caused by transient errors, validation failures, or user retries.

4. Include overhead
Account for system instructions, tool messages, metadata, and other tokens not captured in the basic prompt and response averages.

5. Choose the horizon
Multiply the workload across the number of periods covered by the plan.

6. Review the budget
Use total tokens, tokens per task, and million-token units for purchasing or quota planning.

Base tokens = Tasks × (Input tokens + Output tokens) × Periods Total token budget = Base tokens × (1 + Retry rate) × (1 + Overhead rate)

Rates are entered as percentages and converted to decimals. The calculation assumes the averages remain stable across the selected periods.

What the result means

The main result is the estimated token volume to reserve for the entire plan, including the selected allowances.

Estimates depend on the model, serving stack, hardware, quantization, batching strategy, and workload mix. Validate the result with measurements from your own environment before committing capacity or budget.

Given

  • 100,000 tasks
  • 1,200 input tokens per task
  • 350 output tokens per task
  • 3% retry allowance
  • 8% overhead
  • 1 period

Calculation

Base = 100,000 × (1,200 + 350) = 155,000,000 tokens. Total = 155,000,000 × 1.03 × 1.08 = 172,422,000 tokens.

Result

172,422,000 tokens, or 172.422 million-token units.

This is the planning volume to reserve before applying any provider-specific pricing tiers, cache discounts, or committed-use terms.

Should system prompts be included in input tokens?

Yes. If your measured input average already includes system and tool messages, set the separate overhead rate to zero or use it only for unmeasured extras.

How should I estimate output tokens?

Use a percentile or average from realistic responses. For strict budgets, a higher percentile is safer than relying only on the mean.

Does the planner account for cached tokens?

No. It estimates physical token volume. Apply cache hit rates and provider-specific discounts separately when converting the budget into cost.

What happens when retries vary by request type?

Create separate estimates for materially different workflows and add the totals. A single blended retry rate can hide a small but expensive failure-prone segment.

How is this different from a cost calculator?

This page estimates token demand. A cost calculator multiplies token categories by prices and may treat input, output, cached, and batch tokens differently.