Compare translation API features

An overview of the features and limitations of the major machine translation APIs

This page was last updated on January 14th, 2021. We generally compile this comparison only from public or publicly derivable information. You can send updates and suggestions to [email protected].

At ModelFront, we're often asked for advice on the capabilities of the major machine translation systems.

Translation quality is highly subjective, and language-, domain- and application-specific. We don't find any single system to be the best in general across all those dimensions. We recommend running a good evaluation on multiple systems with your actual data.

But as important as raw quality - and often a decisivie factor on effective quality - are the features and limitations of each API.

For example, Google Translate supports German and French, and customization. But did you know that Google Translate doesn't yet support customization for pairs like German to French? That's because most APIs still translate via English. And DeepL now supports basic customization with its glossary feature, but its not yet available in the API or apps or plugins that connect to the API.

Language coverage varies a lot even for basic support. If you want to translate between Cantonese and Mayan, or Portuguese and Brazilian Portuguese, you'll have to use Microsoft or DeepL. But if you need Yakut, Udmurt or Papamiento, there's only Yandex.

Here's our current list of the most popular self-serve translation APIs with public per-character pricing.

{"next":{"next":null,"collection":"landscapes","path":"_landscapes/options.md","id":"/options","content":"Measuring quality and risk are fundamental to successful translation at scale. Both human and machine translation benefit from sentence-level and corpus-level metrics.\n\nMetrics like BLEU are based on string distance to human reference translations and cannot be used for new incoming translations, nor for the human reference translations themselves.\n\nWhat are the options if you want to build or buy services, tools or technology for measuring the quality and risk of new translations?\n\n---\n\n### Humans\n\nWhether just an internal human evaluation in a spreadsheet, user-reported quality ratings, an analysis of translator post-editing productivity and effort, or full post-editing, professional human linguists and translators are the gold standard.\n\nThere is significant research on human evaluation methods, and quality frameworks like MQM-DQF and even quality management platforms like **TAUS DQF** and **ContentQuo** for standardizing and managing human evaluations, as well as translators and language service providers offering quality reviews or continuous human labelling.\n\n---\n\n### Features\n\nTranslation tools like **Memsource**, **Smartling** and **GlobalLink** have features for automatically measuring quality bundled in their platforms. Memsource's feature is based on machine learning.\n\n\n### Tools and services\n\n**Xbench**, **Verifika** and **LexiQA** directly apply exhaustive, hand-crafted linguistic rules, \nconfigurations and translation memories to catch common translation errors, especially human translation errors.\n\nThey are integrated into existing tools, and their outputs are predictable and interpretable. LexiQA is unique in its partnerships with web-based translation tools and its API.\n\nModelFront [partners](/access/) like GlobalDoc's **LangXpert** and **translate5** integrate ModelFront technology as smart features in their translation systems.\n\n\n---\n\n### Open-source libraries\n\nIf you have the data and the machine learning team and want to build your own system based on machine learning, there is a growing set of open-source options.\n\nThe most notable quality estimation frameworks are **TransQuest** from Tharindu Ranasinghe, **OpenKiwi** from Unbabel and **DeepQuest** from the research group led by Lucía Specia. TransQuest offers pretrained models. **Zipporah** from Hainan Xu and Philipp Koehn is the best-known library for parallel data filtering.\n\nThe owners of those repositories are also key contributors to and co-organizers of the WMT shared tasks on Quality Estimation and Parallel Corpus Filtering.\n\nMassively multilingual libraries and pretrained models like **LASER** are not specifically for translation or translation quality, but a solid baseline for an\nunsupervised approach to parallel data filtering when combined with other techniques like language identification, regexes and round-trip translation.\n\n\n### Internal systems\n\nUnbabel, eBay, Microsoft, Amazon, Facebook and others invest in in-house quality estimation research and development for their own use,\nmainly for the content that flows through their platforms at scale.\n\nThe main goal is to use raw machine translation for as much as possible, whether in efficient hybrid translation workflows for localization or customer service, or just to limit catastrophes on user- and business-generated content that is machine translated by default.\n\nTheir approaches are based on machine learning.\n\n\n### Systems accessible as APIs, consoles or on-prem\n\n**ModelFront** is the first and only API for translation risk prediction based on machine learning. With a few clicks or a few lines of code, you can access a production-strength system.\n\n[Our approach](/our-approach/) is developed fully in-house, extending ideas from the leading researchers in quality estimation and parallel data filtering, and from our own experience inside the leading machine translation provider.\n\nWe've productionized it and made it accessible and useful to more players - enterprise localization teams, language service providers, platform and tool developers and machine translation researchers.\n\nWe built in security, scalability, and support for 100+ languages and 10K+ language pairs, locales, encodings, formatting, tags and file formats, integrations with the top machine translation API providers and automated customization.\n\nWe provide our technology as an [API and console](https://console.modelfront.com) for convenience, as well as on-prem deployments. You can also [access](/access/) our technology through our partners and other projects that we support.\n\nWe continuously invest in curated parallel datasets and manually-labeled datasets and track emerging risks\ntypes as translation technology, use cases and languages evolve.\n\n\n\n","output":null,"excerpt":"

Measuring quality and risk are fundamental to successful translation at scale. Both human and machine translation benefit from sentence-level and corpus-level metrics.

\n","previous":{"next":{"collection":"landscapes","path":"_landscapes/options.md","id":"/options","url":"/options","relative_path":"_landscapes/options.md","draft":false,"categories":["landscape"],"title":"Options for translation quality and risk","description":"How to build or buy services, tools or technology for measuring translation quality and risk","layout":"default","tag":"translation-risk-prediction","category":"landscape","slug":"options","ext":".md","tags":["translation-risk-prediction"]},"collection":"landscapes","path":"_landscapes/compare.md","id":"/compare","content":"*This page was last updated on {% include date.html %}. We generally compile this comparison only from public or publicly derivable information. You can send updates and suggestions to [[email protected]](mailto://[email protected]).*\n\nAt ModelFront, we're often asked for advice on the capabilities of the major machine translation systems.\n\nTranslation quality is highly subjective, and language-, domain- and application-specific. We don't find any single system to be the best in general across all those dimensions. We recommend running [a good evaluation](/what-is-a-good-evaluation/) on multiple systems with your actual data.\n\nBut as important as raw quality - and often a decisivie factor on effective quality - are the **features and limitations** of each API.\n\nFor example, Google Translate supports German and French, and customization. But did you know that Google Translate doesn't yet support customization for pairs like German to French? That's because most APIs still [translate via English](https://modelfront.com/direct-translation/). And DeepL now supports basic customization with its glossary feature, but its not yet available in the API or apps or plugins that connect to the API.\n\nLanguage coverage varies a lot even for basic support. If you want to translate between Cantonese and Mayan, or Portuguese and Brazilian Portuguese, you'll have to use Microsoft or DeepL. But if you need Yakut, Udmurt or Papamiento, there's only Yandex.\n\nHere's our current list of the most popular self-serve translation APIs with public per-character pricing.\n\n
\n {{ page | jsonify }}\n
\n\n\n\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LanguagesCustomizationContextPricing
for 1M chars
Google
Translate API
108terminology

AutoML,
to and from English only
$20
$80 for custom
LingvaNex
Translate API
105$5
Yandex
Translate API
93terminology

custom models
in private beta
$6
₽447
Microsoft
Translator Text API
73terminology

Custom Translator,
to and from English only,
limited pairs
$10
$40 for custom
Amazon
Translate API
73terminology

adaptive customization,
to and from English only
$15
$60 for custom adaptive
ModernMT
API
47terminology

adaptive customization
for all pairs
document-level$15
\n $50 for custom adaptive
\n
IBM
Watson Language Translator
39terminology

custom models
$20
$100 for custom
Baidu
翻译API
28terminology

custom models

vertical-specific models
¥49
custom unlisted
DeepL
API
11€20
Alibaba
Machine Translation
15
limited pairs
commerce-specific model$33
\n
\n\nOnly features available via API are considered. For example, Google supports gender variants, and DeepL supports context awareness across lines, but those features are only available in their consumer websites and applications, not via their APIs.\n\nLanguage counts consider the listed languages, no matter how similar, and assume essentially all pairs of those languages are supported unless otherwise noted.\n\nPricing considers the paid rate at moderate volume, not free quota or volume discounts, nor prepaid or subscription discounts. Most of the APIs offer free quota, and charge small additional fees for training custom models and keeping them deployed. There is also continued widespread unpaid use of unofficial APIs based on reverse-engineering consumer translation applications.\n\nSome systems use different architectures and even approaches for different language pairs or content types.\n\nFor more details, please see the documentation of each API.\n\n---\n\n##### Other machine translation APIs\nThere are other self-serve APIs to consider, notably those from **Naver**, **eBay**, **Tencent** and **Youdao**. There are also APIs like the **Intento** and the **Rakuten** RapidAPI that aggregate many translation APIs and more.\n\n##### Other machine translation features\nThere are other features to consider, like locale, formality, data privacy, language identification, HTML and template translation, input correction, transliteration, speech input, latency, batching, offline models and on-prem deployment, that are out of the scope of this comparison for now.\n\nFor example, Google Translate produces French mostly with formal *vous*, Parisian French word choices but Canadian French punctuation. If you want informal Canadian French out of the box, you'll have to make some compromises - none of the major APIs produces it by default.\n\n##### Other machine translation providers\nSYSTRAN, Unbabel, Lilt, Tilde, Omniscien, KantanMT, PangeaMT, TextShuttle, Globalese, Iconic, AppTek, SAP and more train custom machine translation, but do not offer a self-serve API with public per-character pricing. SYSTRAN offers an open-source library, OpenNMT.\n\n##### Other machine translation players\nApple, Facebook and Rakuten have their own machine translation deployed in their own consumer platforms, but do not offer an official API. Facebook does offer an open-source library, Fairseq, and pre-trained models.\n\nTo the best our knowledge at this time, many others with machine translation in their platforms, like Twitter and AirBnB, as well as translation providers and CAT tools like Lionbridge and SDL, use the APIs listed above or on-premise deployments of other providers and do not develop their own machine translation from scratch.\n\n\n##### Machine translation libraries\n\nMany major machine translation providers also offer open-source libraries and even pre-trained models.\n
\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
OwnersUsers
FairseqFacebook
Marian NMTMicrosoftUnbabel, Rakuten, eBay, Tilde, Project Bergamot, Opus-MT
ModernMT
based on Fairseq
ModernMT
OpenNMTSYSTRAN, Ubiqus
seq2seqGoogle
SockeyeAmazon
\n
\n \n##### Pre-trained machine translation models\n\nOpus-MT offers pretrained Marian NMT models for many language pairs. They are also available via Hugging Face.\n\nFacebook offers pre-trained Fairseq models for a limited number of languages.\n\n---\n\n### Updates\n\n##### November 2020 - Amazon\nAmazon launches **adaptive** translation as Active Custom Translation, joining ModernMT, and launches more than a dozen new languages, bringing its total number of languages to 73.\n\n##### September 2020 - Microsoft\nMicrosoft launches **Assamese** first, bringing its total number of languages to 78. It also updated its system for training custom models.\n\n##### August 2020 - LingvaNex\nLingvaNex launches their translation API with **more than 100 languages**.\n\n##### August 2020 - Microsoft\nMicrosoft launches **Odia (Oriya)**, **Dari**, **Pashto** and **Sorani** and **Kurmanji Kurdish**, bringing its total number of languages to 77.\n\n##### June 2020 - Microsoft\nMicrosoft launches **Kazakh**, bringing its total number of languages to 72.\n\n##### April 2020 - Microsoft\nMicrosoft launches **Marathi**, **Gujarati**, **Punjabi**, **Malayalam** and **Kannada**, bringing its total number of languages to 71.\n\n##### April 2020 - DeepL\nDeepL launches **Brazilian Portuguese** in addition to Portuguese, joining Microsoft in offering two varieties as a target language. In contrast, Google's only option is Brazilian Portuguese.\n\n##### March 2020 - ModernMT\nModernMT announces **free machine translation** for April and May, unlimited other than by its hardware constraints.\n\n##### March 2020 - Yandex\nYandex launches **Yakut**, bringing its total number of languages supported to 97. However it's one of 4 languages not yet available in the API.\n\n##### March 2020 - DeepL\nDeepL adds **Chinese** and **Japanese**, its first non-European languages, bringing its total number of languages supported to 11. \n\n##### February 2020 - Google\nGoogle adds **Kinyarwanda**, **Odia (Oriya)**, **Tatar**, **Turkmen** and **Uyghur**, bringing its total number of languages supported to 108.\n\n##### January 2020 - Yandex\nYandex launches **Chuvash**.\n\n##### January 2020 - Microsoft\nMicrosoft launches **Irish Gaelic**.\n\n##### October 2019 - ModernMT\nModernMT launches **human-in-the-loop**, **document-level context** and **context adaptation**.\n\n---\n\n### Thanks\n\nWe're very thankful to all who have contributed to this comparison.\n\n","output":null,"excerpt":"

This page was last updated on\nJanuary\n14th,\n2021. We generally compile this comparison only from public or publicly derivable information. You can send updates and suggestions to [email protected].

\n","previous":null,"url":"/compare","relative_path":"_landscapes/compare.md","draft":false,"categories":["landscape"],"title":"Compare translation API features","description":"An overview of the features and limitations of the major machine translation APIs","layout":"default","image":"https://modelfront.com/assets/images/compare/compare.png","tag":"machine-translation","category":"landscape","slug":"compare","ext":".md","tags":["machine-translation"]},"url":"/options","relative_path":"_landscapes/options.md","draft":false,"categories":["landscape"],"title":"Options for translation quality and risk","description":"How to build or buy services, tools or technology for measuring translation quality and risk","layout":"default","tag":"translation-risk-prediction","category":"landscape","slug":"options","ext":".md","tags":["translation-risk-prediction"]},"collection":"landscapes","path":"_landscapes/compare.md","id":"/compare","content":"*This page was last updated on {% include date.html %}. We generally compile this comparison only from public or publicly derivable information. You can send updates and suggestions to [[email protected]](mailto://[email protected]).*\n\nAt ModelFront, we're often asked for advice on the capabilities of the major machine translation systems.\n\nTranslation quality is highly subjective, and language-, domain- and application-specific. We don't find any single system to be the best in general across all those dimensions. We recommend running [a good evaluation](/what-is-a-good-evaluation/) on multiple systems with your actual data.\n\nBut as important as raw quality - and often a decisivie factor on effective quality - are the **features and limitations** of each API.\n\nFor example, Google Translate supports German and French, and customization. But did you know that Google Translate doesn't yet support customization for pairs like German to French? That's because most APIs still [translate via English](https://modelfront.com/direct-translation/). And DeepL now supports basic customization with its glossary feature, but its not yet available in the API or apps or plugins that connect to the API.\n\nLanguage coverage varies a lot even for basic support. If you want to translate between Cantonese and Mayan, or Portuguese and Brazilian Portuguese, you'll have to use Microsoft or DeepL. But if you need Yakut, Udmurt or Papamiento, there's only Yandex.\n\nHere's our current list of the most popular self-serve translation APIs with public per-character pricing.\n\n
\n {{ page | jsonify }}\n
\n\n\n\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LanguagesCustomizationContextPricing
for 1M chars
Google
Translate API
108terminology

AutoML,
to and from English only
$20
$80 for custom
LingvaNex
Translate API
105$5
Yandex
Translate API
93terminology

custom models
in private beta
$6
₽447
Microsoft
Translator Text API
73terminology

Custom Translator,
to and from English only,
limited pairs
$10
$40 for custom
Amazon
Translate API
73terminology

adaptive customization,
to and from English only
$15
$60 for custom adaptive
ModernMT
API
47terminology

adaptive customization
for all pairs
document-level$15
\n $50 for custom adaptive
\n
IBM
Watson Language Translator
39terminology

custom models
$20
$100 for custom
Baidu
翻译API
28terminology

custom models

vertical-specific models
¥49
custom unlisted
DeepL
API
11€20
Alibaba
Machine Translation
15
limited pairs
commerce-specific model$33
\n
\n\nOnly features available via API are considered. For example, Google supports gender variants, and DeepL supports context awareness across lines, but those features are only available in their consumer websites and applications, not via their APIs.\n\nLanguage counts consider the listed languages, no matter how similar, and assume essentially all pairs of those languages are supported unless otherwise noted.\n\nPricing considers the paid rate at moderate volume, not free quota or volume discounts, nor prepaid or subscription discounts. Most of the APIs offer free quota, and charge small additional fees for training custom models and keeping them deployed. There is also continued widespread unpaid use of unofficial APIs based on reverse-engineering consumer translation applications.\n\nSome systems use different architectures and even approaches for different language pairs or content types.\n\nFor more details, please see the documentation of each API.\n\n---\n\n##### Other machine translation APIs\nThere are other self-serve APIs to consider, notably those from **Naver**, **eBay**, **Tencent** and **Youdao**. There are also APIs like the **Intento** and the **Rakuten** RapidAPI that aggregate many translation APIs and more.\n\n##### Other machine translation features\nThere are other features to consider, like locale, formality, data privacy, language identification, HTML and template translation, input correction, transliteration, speech input, latency, batching, offline models and on-prem deployment, that are out of the scope of this comparison for now.\n\nFor example, Google Translate produces French mostly with formal *vous*, Parisian French word choices but Canadian French punctuation. If you want informal Canadian French out of the box, you'll have to make some compromises - none of the major APIs produces it by default.\n\n##### Other machine translation providers\nSYSTRAN, Unbabel, Lilt, Tilde, Omniscien, KantanMT, PangeaMT, TextShuttle, Globalese, Iconic, AppTek, SAP and more train custom machine translation, but do not offer a self-serve API with public per-character pricing. SYSTRAN offers an open-source library, OpenNMT.\n\n##### Other machine translation players\nApple, Facebook and Rakuten have their own machine translation deployed in their own consumer platforms, but do not offer an official API. Facebook does offer an open-source library, Fairseq, and pre-trained models.\n\nTo the best our knowledge at this time, many others with machine translation in their platforms, like Twitter and AirBnB, as well as translation providers and CAT tools like Lionbridge and SDL, use the APIs listed above or on-premise deployments of other providers and do not develop their own machine translation from scratch.\n\n\n##### Machine translation libraries\n\nMany major machine translation providers also offer open-source libraries and even pre-trained models.\n
\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
OwnersUsers
FairseqFacebook
Marian NMTMicrosoftUnbabel, Rakuten, eBay, Tilde, Project Bergamot, Opus-MT
ModernMT
based on Fairseq
ModernMT
OpenNMTSYSTRAN, Ubiqus
seq2seqGoogle
SockeyeAmazon
\n
\n \n##### Pre-trained machine translation models\n\nOpus-MT offers pretrained Marian NMT models for many language pairs. They are also available via Hugging Face.\n\nFacebook offers pre-trained Fairseq models for a limited number of languages.\n\n---\n\n### Updates\n\n##### November 2020 - Amazon\nAmazon launches **adaptive** translation as Active Custom Translation, joining ModernMT, and launches more than a dozen new languages, bringing its total number of languages to 73.\n\n##### September 2020 - Microsoft\nMicrosoft launches **Assamese** first, bringing its total number of languages to 78. It also updated its system for training custom models.\n\n##### August 2020 - LingvaNex\nLingvaNex launches their translation API with **more than 100 languages**.\n\n##### August 2020 - Microsoft\nMicrosoft launches **Odia (Oriya)**, **Dari**, **Pashto** and **Sorani** and **Kurmanji Kurdish**, bringing its total number of languages to 77.\n\n##### June 2020 - Microsoft\nMicrosoft launches **Kazakh**, bringing its total number of languages to 72.\n\n##### April 2020 - Microsoft\nMicrosoft launches **Marathi**, **Gujarati**, **Punjabi**, **Malayalam** and **Kannada**, bringing its total number of languages to 71.\n\n##### April 2020 - DeepL\nDeepL launches **Brazilian Portuguese** in addition to Portuguese, joining Microsoft in offering two varieties as a target language. In contrast, Google's only option is Brazilian Portuguese.\n\n##### March 2020 - ModernMT\nModernMT announces **free machine translation** for April and May, unlimited other than by its hardware constraints.\n\n##### March 2020 - Yandex\nYandex launches **Yakut**, bringing its total number of languages supported to 97. However it's one of 4 languages not yet available in the API.\n\n##### March 2020 - DeepL\nDeepL adds **Chinese** and **Japanese**, its first non-European languages, bringing its total number of languages supported to 11. \n\n##### February 2020 - Google\nGoogle adds **Kinyarwanda**, **Odia (Oriya)**, **Tatar**, **Turkmen** and **Uyghur**, bringing its total number of languages supported to 108.\n\n##### January 2020 - Yandex\nYandex launches **Chuvash**.\n\n##### January 2020 - Microsoft\nMicrosoft launches **Irish Gaelic**.\n\n##### October 2019 - ModernMT\nModernMT launches **human-in-the-loop**, **document-level context** and **context adaptation**.\n\n---\n\n### Thanks\n\nWe're very thankful to all who have contributed to this comparison.\n\n","output":null,"excerpt":"

This page was last updated on\nJanuary\n14th,\n2021. We generally compile this comparison only from public or publicly derivable information. You can send updates and suggestions to [email protected].

\n","previous":null,"url":"/compare","relative_path":"_landscapes/compare.md","draft":false,"categories":["landscape"],"title":"Compare translation API features","description":"An overview of the features and limitations of the major machine translation APIs","layout":"default","image":"https://modelfront.com/assets/images/compare/compare.png","tag":"machine-translation","category":"landscape","slug":"compare","ext":".md","tags":["machine-translation"]}
Languages Customization Context Pricing
for 1M chars
Google
Translate API
108 terminology

AutoML,
to and from English only
$20
$80 for custom
LingvaNex
Translate API
105 $5
Yandex
Translate API
93 terminology

custom models
in private beta
$6
₽447
Microsoft
Translator Text API
73 terminology

Custom Translator,
to and from English only,
limited pairs
$10
$40 for custom
Amazon
Translate API
73 terminology

adaptive customization,
to and from English only
$15
$60 for custom adaptive
ModernMT
API
47 terminology

adaptive customization
for all pairs
document-level $15
$50 for custom adaptive
IBM
Watson Language Translator
39 terminology

custom models
$20
$100 for custom
Baidu
翻译API
28 terminology

custom models

vertical-specific models
¥49
custom unlisted
DeepL
API
11 €20
Alibaba
Machine Translation
15
limited pairs
commerce-specific model $33

Only features available via API are considered. For example, Google supports gender variants, and DeepL supports context awareness across lines, but those features are only available in their consumer websites and applications, not via their APIs.

Language counts consider the listed languages, no matter how similar, and assume essentially all pairs of those languages are supported unless otherwise noted.

Pricing considers the paid rate at moderate volume, not free quota or volume discounts, nor prepaid or subscription discounts. Most of the APIs offer free quota, and charge small additional fees for training custom models and keeping them deployed. There is also continued widespread unpaid use of unofficial APIs based on reverse-engineering consumer translation applications.

Some systems use different architectures and even approaches for different language pairs or content types.

For more details, please see the documentation of each API.


Other machine translation APIs

There are other self-serve APIs to consider, notably those from Naver, eBay, Tencent and Youdao. There are also APIs like the Intento and the Rakuten RapidAPI that aggregate many translation APIs and more.

Other machine translation features

There are other features to consider, like locale, formality, data privacy, language identification, HTML and template translation, input correction, transliteration, speech input, latency, batching, offline models and on-prem deployment, that are out of the scope of this comparison for now.

For example, Google Translate produces French mostly with formal vous, Parisian French word choices but Canadian French punctuation. If you want informal Canadian French out of the box, you'll have to make some compromises - none of the major APIs produces it by default.

Other machine translation providers

SYSTRAN, Unbabel, Lilt, Tilde, Omniscien, KantanMT, PangeaMT, TextShuttle, Globalese, Iconic, AppTek, SAP and more train custom machine translation, but do not offer a self-serve API with public per-character pricing. SYSTRAN offers an open-source library, OpenNMT.

Other machine translation players

Apple, Facebook and Rakuten have their own machine translation deployed in their own consumer platforms, but do not offer an official API. Facebook does offer an open-source library, Fairseq, and pre-trained models.

To the best our knowledge at this time, many others with machine translation in their platforms, like Twitter and AirBnB, as well as translation providers and CAT tools like Lionbridge and SDL, use the APIs listed above or on-premise deployments of other providers and do not develop their own machine translation from scratch.

Machine translation libraries

Many major machine translation providers also offer open-source libraries and even pre-trained models.

Owners Users
Fairseq Facebook
Marian NMT Microsoft Unbabel, Rakuten, eBay, Tilde, Project Bergamot, Opus-MT
ModernMT
based on Fairseq
ModernMT
OpenNMT SYSTRAN, Ubiqus
seq2seq Google
Sockeye Amazon
Pre-trained machine translation models

Opus-MT offers pretrained Marian NMT models for many language pairs. They are also available via Hugging Face.

Facebook offers pre-trained Fairseq models for a limited number of languages.


Updates

November 2020 - Amazon

Amazon launches adaptive translation as Active Custom Translation, joining ModernMT, and launches more than a dozen new languages, bringing its total number of languages to 73.

September 2020 - Microsoft

Microsoft launches Assamese first, bringing its total number of languages to 78. It also updated its system for training custom models.

August 2020 - LingvaNex

LingvaNex launches their translation API with more than 100 languages.

August 2020 - Microsoft

Microsoft launches Odia (Oriya), Dari, Pashto and Sorani and Kurmanji Kurdish, bringing its total number of languages to 77.

June 2020 - Microsoft

Microsoft launches Kazakh, bringing its total number of languages to 72.

April 2020 - Microsoft

Microsoft launches Marathi, Gujarati, Punjabi, Malayalam and Kannada, bringing its total number of languages to 71.

April 2020 - DeepL

DeepL launches Brazilian Portuguese in addition to Portuguese, joining Microsoft in offering two varieties as a target language. In contrast, Google's only option is Brazilian Portuguese.

March 2020 - ModernMT

ModernMT announces free machine translation for April and May, unlimited other than by its hardware constraints.

March 2020 - Yandex

Yandex launches Yakut, bringing its total number of languages supported to 97. However it's one of 4 languages not yet available in the API.

March 2020 - DeepL

DeepL adds Chinese and Japanese, its first non-European languages, bringing its total number of languages supported to 11.

February 2020 - Google

Google adds Kinyarwanda, Odia (Oriya), Tatar, Turkmen and Uyghur, bringing its total number of languages supported to 108.

January 2020 - Yandex

Yandex launches Chuvash.

January 2020 - Microsoft

Microsoft launches Irish Gaelic.

October 2019 - ModernMT

ModernMT launches human-in-the-loop, document-level context and context adaptation.


Thanks

We're very thankful to all who have contributed to this comparison.