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LLM Batch Processing Token Budget Planner

The LLM Batch Processing Token Budget Planner estimates token volume for offline or deferred workloads processed in repeated batch runs. It combines task payload tokens with per-task metadata or wrapper tokens, multiplies the workload across scheduled batches, and adds an allowance for records that may need to be reprocessed.

This structure fits document enrichment, classification backfills, evaluation runs, catalog generation, and other jobs where completion time is flexible but total volume must be reserved. Keep input and output averages tied to the same task definition, and avoid counting metadata twice when it is already included in tokenizer measurements. The result can be used for quota planning or as the volume input to a separate pricing model.

Batch workload assumptions

tasks
tokens
tokens
%
tokens
batches
Result
—
total batch token budget
Payload tokens—
Metadata tokens—
Reprocessing allowance—
Tokens per batch run—

1. Enter records per run
Use the number of tasks submitted in one complete batch.

2. Set payload tokens
Provide average input and output tokens for each record.

3. Add wrapper metadata
Include IDs, instructions, schemas, or other per-record tokens not present in the payload averages.

4. Allow for reprocessing
Estimate records repeated because of validation failure, job interruption, or changed outputs.

5. Enter repeated runs
Specify how many equivalent batch executions are in the plan.

6. Review totals
Compare payload, metadata, reprocessing, and per-run token volumes.

Base batch tokens = Tasks × (Input + Output + Metadata) × Batch runs Total budget = Base batch tokens × (1 + Reprocessing rate)

What the result means

The main result is the token reserve for all scheduled batch runs and expected reprocessing.

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

  • 500,000 tasks per run
  • 900 input tokens
  • 180 output tokens
  • 40 metadata tokens
  • 2% reprocessing
  • 4 runs

Calculation

Base = 500,000 × (900 + 180 + 40) × 4 = 2,240,000,000 tokens. Total = 2,240,000,000 × 1.02 = 2,284,800,000 tokens.

Result

2,284,800,000 tokens.

Each run consumes an average planned budget of 571,200,000 tokens.

Should invalid records be included in task count?

Include records that are likely to reach the model and consume tokens. Records rejected before model submission do not need token budget.

What belongs in metadata tokens?

Count textual wrappers such as JSON field names, output schemas, fixed instructions, and record identifiers when they are sent to the model.

How do I handle multiple task types in one batch?

Estimate each materially different type separately and add the totals. A blended average can conceal a small group of very long records.

Does the budget include provider batch discounts?

No. It estimates token volume only. Apply the relevant batch price or discount when converting tokens into spend.

Why use a reprocessing allowance instead of success rate?

Batch workflows often rerun only failed or rejected records. An explicit reprocessing percentage maps directly to that operational pattern.