Is AI Translation Good Enough for Your App? An Honest Answer

Short answer: for UI strings, yes — if and only if the AI gets context, a glossary, and a human skim. Raw machine translation earned its bad reputation honestly; modern LLMs are a different tier, but they fail in new ways that matter for apps. Here's where the line actually sits in 2026, so you can decide per language and per string type.

Why "machine translation" stopped being one thing

Developers' horror stories mostly come from statistical MT: Google Translate rendering "honey" (the food) as cariño (the endearment), entity soup in XML, no notion of what an app even is. Informal developer tests consistently rank LLMs far above legacy MT — one gamedev comparison scored ChatGPT ~80% acceptable for Polish against DeepL's 40%, with Google Translate behind both. LLMs understand register, UI conventions, and instructions like "this is a button label, keep it short."

The three failure modes that remain

• Terminology drift. Your "Workspace" becomes three different Japanese words across releases. The fix is a glossary — pinned translations for product terms, enforced on every string. This is the feature developers most often ask for after trying chat-based translation.
• Structural damage. Chat interfaces mangle escapes, drop %1$s placeholders, or invent keys. The fix is structural: parse the file, lock placeholders, translate only text, validate output against the source — which is exactly how Localize Your App processes every supported format.
• Confident nonsense in low-resource languages. Quality tracks training data. Spanish, German, Japanese, French are strong; smaller languages deserve a native speaker's pass before you ship.

A decision rule you can actually use

• UI strings (labels, buttons, settings): AI with glossary + placeholder validation + your own skim in a side-by-side editor. This is the Localize Your App default workflow, and it's production-grade for major languages.
• Store listings and marketing copy: AI draft, then polish your top 2–3 markets by hand — keywords especially are research, not translation.
• Legal, medical, payments text: human review, always. The savings aren't worth the risk.

The math that makes review affordable

The reason this debate even exists is cost: human translation runs $0.10+/word/language, so a 10-language app is thousands of dollars — see the full cost breakdown. AI translation of the same app costs tens of dollars, which means you can afford a native-speaker review of your single most important market and still spend 95% less. Inviting a reviewer into the side-by-side editor — source, translation, and glossary in one view — is a far smaller ask than mailing someone a strings file.

Try it on your own strings

The honest test is your own app: upload your .strings, strings.xml, or .arb file, translate into a language you or a friend can judge, and read the output side by side. Verdicts beat opinions.