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AI Translation Token Budget Planner

This planner estimates the monthly token allowance for an AI translation service. It combines source tokens, translated output tokens, reusable instruction or context tokens, and retry overhead for each completed document or segment. Localization teams can use the result to set provider quotas, compare compact and verbose prompting strategies, and estimate the effect of review-driven retranslations. The output ratio is explicit because translated text may be shorter or longer than the source depending on the language pair.

Translation token assumptions

tasks
tokens
%
tokens
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USD
Result
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Recommended monthly translation token budget
Tokens per completed task—
Base monthly tokens—
Output tokens per attempt—
Estimated token cost—

1. Enter completed task volume

Use the number of translations expected to finish successfully in the month.

2. Estimate source length

Use tokenizer measurements from a representative sample, not word count unless converted consistently.

3. Set the output ratio

Reflect the typical translated-token length relative to source tokens for the language pair.

4. Add prompt context and retries

Include instructions, glossary snippets, examples, and retranslations that are billed again.

5. Choose a budget buffer

Add headroom for unusually long content or seasonal volume changes.

Output tokens per attempt = Source tokens × Output ratio ÷ 100 Tokens per attempt = Source tokens + Output tokens + Context tokens Tokens per completed task = Tokens per attempt × (1 + Retry rate ÷ 100) Recommended monthly budget = Tasks × Tokens per completed task × (1 + Buffer rate ÷ 100)

What the result means

The recommendation is a buffered quota covering expected input, output, context, and retry usage for the planned task volume.

Tokenizer behavior and output length vary by model and language pair; replace estimates with measured usage when possible.

Given: 50,000 tasks, 600 source tokens, a 115% output ratio, 120 context tokens, 6% retries, a 10% buffer, and $3.50 per million tokens.

Calculation: Output = 600 × 1.15 = 690. Tokens per attempt = 600 + 690 + 120 = 1,410. Tokens per completion = 1,410 × 1.06 = 1,494.6. Budget = 50,000 × 1,494.6 × 1.10 = 82,203,000.

Result: Plan for 82.203 million tokens, valued at about $287.71.

Can word count be entered as source tokens?

Not directly. Convert a representative sample with the provider’s tokenizer or use an empirically measured tokens-per-word ratio.

Why can translated output exceed source tokens?

Different languages segment words and characters differently. The same meaning can produce a larger token count in the target language.

Should glossary text be included every time?

Include glossary or style-guide tokens whenever they are sent with each request. Exclude context that is not billed or is fully cached at no cost.

How are human corrections represented?

Only corrections that trigger another model call belong in the retry rate. Human review labor is outside this token budget.

Is a shared buffer suitable for every language pair?

A portfolio-level buffer is convenient, but separate plans are more accurate when language pairs have very different output ratios and retry rates.