Amazon Translate

Neural machine translation API inside AWS with custom terminology support

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What is Amazon Translate?

Amazon Translate is AWS's neural machine translation service, called from an application or used to translate documents held in S3 in bulk. It covers a wide language list, supports both real-time and asynchronous batch jobs, and can be constrained with custom terminology files so product names and domain vocabulary stay fixed. Active custom translation goes further by letting you supply parallel examples that steer the model toward your own material. Its main argument is integration: translation becomes one step in a pipeline alongside storage, queues and analytics. There is no consumer interface, so it is infrastructure for developers.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Real-time neural machine translation API for text content
  • Batch translation of documents stored in Amazon S3
  • Custom terminology files that lock product and domain terms
  • Active custom translation guided by your own parallel examples
  • Language detection and automatic source language identification
  • Native integration with Lambda, S3, Comprehend and other AWS services

Pros & cons

Strengths

  • Fits existing AWS pipelines without adding another vendor
  • Custom terminology fixes the errors generic engines make
  • Batch mode handles large document sets without custom glue code

Watch out for

  • AWS setup and permission knowledge are needed before it does anything
  • No editor or review workflow for non-technical translators
  • Quality on creative or idiomatic text trails the best engines

Best for & use cases

aws-based applications, bulk document translation and developer pipelines

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FAQ

How is this different from a translation website?

It has no interface for translating. You call an API or run a batch job, and the output goes back to your application or into storage, so it suits systems rather than individuals working with text by hand.

What are custom terminologies used for?

They map source terms to fixed target terms, so brand names, part numbers and regulated vocabulary are not reinvented in every sentence. For technical documentation that alone often decides whether output is usable.

When should I use batch mode?

For documents already stored in S3 and for large volumes where waiting is acceptable. Real-time calls suit user-facing requests, while batch jobs are cheaper for bulk work and easier to schedule.