Deploying a Private DeepLX Translation API with Docker

Fast and accurate translation (vs. traditional translators)#

• When I use other translators for low‑resource languages, the most common issues are poor accuracy and missing parts of sentences (especially for colloquial expressions). DeepL, as one of the first translators to train its models with AI, is naturally more accurate than many general‑purpose translators.

High translation speed with virtually no concurrency limits (vs. LLM translation)#

• Translations based on Gemini or OpenAI are certainly accurate, but they’re constrained by API rate limits and can’t easily handle high‑concurrency scenarios (for example, using Immersive Translate to auto‑translate Twitter). By contrast, DeepL can deliver translation speed comparable to traditional translators while maintaining a solid level of accuracy.

However, the official DeepL service itself is relatively slow, has usage quotas, and doesn’t provide an open API, which turns many users away. DeepLX, on the other hand, is open source on GitHub, doesn’t impose request limits (DeepL may still rate‑limit by IP), and by default listens on the local 1188 port. It also offers multiple installation options.