In the era of AI coding agents, adding basic i18n is much easier than it used to be. You can often just ask your coding agent to implement internationalization and list the languages you want. A typical starting set might be English, Spanish, Portuguese, French, German, Japanese, Korean, Chinese, Arabic, and Indonesian.
One thing that seems to matter is not just translating UI text but structuring the site properly. Language-based routing like /en/, /es/, /ja/, along with correct hreflang tags and sitemap entries, helps search engines understand that your site serves multiple languages.
Once those pages get indexed, something interesting can happen: visitors start showing up from countries you never intentionally targeted.
People sometimes worry about translation quality. In practice, for many products it doesn’t have to be perfect at the beginning. Unless you’re building a very high-touch service, reasonable machine translation is often good enough to start with, and you can improve it later.
Another thing I’ve noticed is that markets can look very different depending on the language. A product that feels crowded in the US market can sometimes look much less competitive elsewhere.
If you’re experimenting with free or early-stage tools, this can be especially useful because discovery isn’t limited to one language ecosystem. For example, when tools get listed on discovery sites like LeanVibe, the listings themselves can be translated as well, which sometimes surfaces projects to users in other countries who would never have seen them otherwise.
Multilingual support isn’t just about accessibility. Sometimes it quietly becomes a distribution channel.