Translation Cost Estimator
Estimate the budget and timeline for translating your website, app, or content. Account for word count, language count, and engineering overhead.
Formula
Cost = (Words × Languages × Rate) + Engineering Overhead
The core cost is the volume of words multiplied by the number of target languages and the per-word rate. Engineering overhead (file management, integration, QA) is added as a percentage markup. Time is estimated based on average daily translator throughput, assuming parallel work across languages.
Worked Examples
Example 1: App Localization
Problem:10k words, 5 languages, $0.15/word, 20% overhead.
Solution:Base: 10k * 5 * $0.15 = $7,500. Overhead: $1,500. Total: $9,000.
Result:$9,000 (~7 days)
Frequently Asked Questions
What is the average cost per word for translation?
It varies by language pair. Common languages (Spanish, French) cost $0.10-$0.15/word. Complex or Asian languages (Japanese, Arabic) often cost $0.15-$0.25/word. Specialized content (Legal, Medical) costs more.
What is the difference between Translation and Localization?
Translation is converting text from one language to another. Localization (L10n) adapts the entire experience (dates, currency, images, cultural nuances) to the target locale.
What is Engineering Overhead?
The cost/time spent by developers extracting strings from code, managing file formats (JSON, XML), and re-integrating translated files. Good automation reduces this significantly.
Should I use Google Translate (Machine Translation)?
For user-facing content, raw MT is usually not good enough. 'MTPE' (Machine Translation Post-Editing) uses AI for a first pass and humans to fix it, saving 30-50% cost.
What is a TMS?
A Translation Management System (like Lokalise, Smartling) automates the workflow, stores 'Translation Memory' (never translate the same sentence twice), and connects to your codebase.
How fast do translators work?
A professional translator averages 2,000-2,500 words per day. Reviewers can handle 4,000-5,000 words per day.
What is Internationalization (i18n)?
The technical process of preparing your code to accept translations (e.g., extracting hardcoded strings into resource files). You cannot localize without i18n.
How do I calculate ROI of localization?
Compare the Total Cost of Localization against the incremental revenue generated from the new markets. Usually, the top 5 languages cover 80% of global GDP.
What is 'Minimum Fee'?
Agencies often charge a minimum (e.g., $25) per language per project. Frequent small updates can be expensive; batching them saves money.
Background & Theory
The Localization Cost & Time Estimator provides a realistic budget for taking a product global, accounting for the hidden costs of project management and engineering.
Concept Overview
Cost = Volume (Words) × Languages × Rate. But efficiency matters: Translation Memory (TM) lowers the effective word count by reusing past translations. Engineering overhead can double the cost if the process is manual.
Key Variables & Intuition
- Word Count: The primary driver.
- Language Count: Linear multiplier. Adding 10 languages = 10x cost.
- Cost Per Word: Varies by quality level (Machine vs. Human vs. Specialist).
- Overhead: The "hidden tax" of file management and QA.
Assumptions
- Standard professional human translation rates.
- Parallel processing (all languages translated simultaneously).
- No Translation Memory savings applied (worst-case scenario).
Limitations & Edge Cases
- Minimum Fees: Small updates (5 words) cost disproportionately more due to minimums.
- Context: Translating without seeing the UI often leads to errors, requiring rework (cost).
- RTL: Right-to-Left languages (Arabic/Hebrew) require extra engineering/design work.
Practical Tips
- Use Keys: Never change string IDs (Keys); it breaks Translation Memory.
- Context Screenshots: Give translators visual context.
- Batch Updates: Don't send 1 word a day; send 100 words a week.
- Pseudo-localization: Test your app with fake foreign characters to break the layout before paying for real translation.
Common Mistakes
- Hardcoding strings (requires code refactor).
- Concatenating strings (e.g., "You have " + n + " items") which creates grammar errors in other languages.
- Embedding text in images.
History
Localization (L10n) has transformed from a manual, afterthought process to a continuous, AI-driven workflow essential for global growth.
Origins & Why It Emerged
In the software box era, "International Versions" were released months after the US version. Strings were often hardcoded. As the internet globalized commerce, simultaneous release (SimShip) became a requirement.
How It Evolved in Practice
The standard process was: Email Excel file -> Translator -> Email back -> Engineer Copy-Paste. This was slow and error-prone. The "Gettext" standard helped. In the 2010s, cloud-based TMS platforms introduced "Continuous Localization," integrating with GitHub to translate code branches automatically.
Modern Usage Today
Today, Neural Machine Translation (NMT) provides a "good enough" baseline instantly. The role of humans has shifted to "review and refinement" (MTPE). Companies now localize into 20+ languages routinely.
Common Misconceptions
- "English is enough": 75% of internet users prefer to buy in their native language.
- "Translation is just words": It's also formatting, currency, and cultural appropriateness.
- "Once and done": Continuous deployment means continuous translation updates.