Language AI platform
How to use
DeepL.
Translating text and office documents, improving tone and phrasing, maintaining terminology with glossaries and style rules, and embedding reviewed multilingual workflows into products.
DeepL is a Language AI platform for text, document, voice, and writing workflows with glossaries, style control, and developer APIs. Translating text and office documents, improving tone and phrasing, maintaining terminology with glossaries and style rules, and embedding reviewed multilingual workflows into products. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to DeepL
Get into the official DeepL experience with the right account and a setup you understand.
- 01
Start at https://www.deepl.com/ and confirm the domain before entering account or payment information.
- 02
Availability: DeepL is available on the web, in desktop and mobile applications, through browser and productivity integrations, and via HTTP APIs and official client libraries.
- 03
A supported app or browser, or a DeepL API plan and confidential server-side key, plus authorized source material, language-specific reviewers, terminology assets, and format QA for documents.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful DeepL session
Learn the interaction loop using a small task with a clear outcome.
- 01
Define the audience, locale, purpose, source authority, terminology, tone, formatting, privacy class, legal risk, and native-review requirements.
- 02
Clean the source, protect placeholders and markup, create an approved glossary, and test a representative passage before translating a full document.
- 03
Translate with the correct locale, formality, glossary, and file mode, keeping the source and machine output as separate versioned artifacts.
- 04
Have a qualified reviewer verify meaning, omissions, numbers, names, terminology, tone, layout, accessibility, and legal effect before publication.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of DeepL output
Turn generated code into a change you would put your name on in review.
- 01
Restate the intended behaviour and the acceptance criteria before reading a single generated line.
- 02
Read the diff rather than the chat summary; the summary describes intent, the diff describes what actually changed.
- 03
Run the test suite, then write a test that would fail if the change were wrong, and confirm it passes for the right reason.
- 04
Check the edges the model tends to skip: error paths, null and empty cases, concurrency, and migration or rollback behaviour.
- 05
Trace every new dependency, network call, and file write, and confirm each one is genuinely required.
- Review the diff, never the description of the diff.
- A passing test suite proves the tests pass, not that the change is correct.
- Keep changes small enough that a human can actually review them.
Privacy & permissions
Use DeepL safely
Give the tool enough repository access to help, and no more than that.
- 01
Decide what the tool may read and what it may write before connecting a repository.
- 02
Scope tokens and integrations to the narrowest repository, branch, and permission set that still works.
- 03
Keep secrets out of the context window: use environment variables and secret stores, never inline credentials.
- 04
Require review before generated code touches authentication, payments, permissions, migrations, or deletion paths.
- 05
Audit what the integration retained after the session, and revoke access that is no longer needed.
- Fluent translation can still reverse meaning, omit qualifiers, mishandle names, or produce the wrong regional register. DeepL API is intended for general content and not high-risk EU AI Act applications; plan-level data handling differs, and API keys must never be placed in client code or query parameters. Use native review for legal, medical, safety, employment, financial, and public-facing material, and validate the rendered document after translation.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use DeepL for the work it does best.
Each workflow is a separate guide with its own steps and review checkpoints.
Combine source preparation, glossaries, machine translation, native review, and layout QA for a dependable multilingual deliverable.
↗ 02 WorkflowBuild a reliable DeepL API localization pipelineAutomate text and document translation with secure keys, protected markup, glossary versioning, usage controls, and human release gates.
↗Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.