Writing and content decision
DeepL Pro
Build a narrower replacement for text and basic document translation is realistic for a single developer, but DeepL’s proprietary training data and polished enterprise features mean a complete parity product is not feasible to reproduce cheaply.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.
What a replacement has to do
- Take user text or document → run a translation model → apply glossaries/rules → return translated text or file (preserving layout).
What it still won’t have
- Proprietary model quality trained on DeepL’s private corpora
- Translation quality improvements from proprietary expert-labeled data
- Enterprise polish: large-scale reliability, SSO & advanced team administration out of the box
- Voice/real-time meeting translation features
What remains hard
- Proprietary data
Trained on proprietary data by thousands of language experts, our specialized LLM delivers unparalleled accuracy and personalized experiences exactly where you need them.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 20 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal self-hosted translation service using: Next.js for the frontend, FastAPI for the backend, PostgreSQL for storage, and a self-hosted open-source translation model (or a paid cloud inference endpoint) for inference. In scope: web UI to submit text and upload DOCX/PDF/PPTX, server-side document parsing and re-generation preserving basic layout, a translation pipeline that calls the model and applies a user-editable glossary, CRUD for glossaries, user auth (email/password) and per-user usage tracking, and an HTTP API endpoint mirroring the web UI. Out of scope: real-time voice conferencing, enterprise SSO integration, custom model training. Include error handling for file parsing, model timeouts, and invalid inputs; include unit tests for parsing, glossary application, and API endpoints; provide Dockerfiles and a deployment manifest for a single small cloud VM plus a GPU inference instance.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score61
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productDeepL product home
- official pricingDeepL Pro pricing & plans
- official docsDeepL features
- open sourceOpenNMT/OpenNMT-py
- open sourcemarian-nmt/marian
Integrity checks
What held up, and what did not.
