Azure vs. AWS: 6 Things to Consider When Choosing a Cloud Translation Platform

Summary

Question: Which machine translation platform supports more languages, Azure or AWS?

Answer: Azure supports 133 languages, while AWS supports 75.

Question: Which platform offers better built-in tools for translating scanned documents?

Answer: Azure includes built-in OCR and translation, while AWS requires integrating Amazon Textract.

Question: Which provider is more cost-effective for custom machine translation at scale?

Answer: Azure is typically more affordable, offering lower rates for custom translation and flexible volume pricing.

This blog kicks off a new LanguageLine series where our Solutions Architects share real-world insights from client implementations, platform evaluations, and technical research. Drawing from broad industry experience, we explore the practical trade-offs, challenges, and advantages of translation technology to help organisations build smarter, more effective language access strategies.

Insights From the Field: A LanguageLine Solutions Architect Series

As global content demands grow, delivering high-quality multilingual experiences is more essential than ever. We regularly evaluate tools to meet evolving translation needs, especially where AI and machine translation are involved.

One common question: Should we use Azure AI Translator or Amazon Translate for enterprise translation workflows?

We recently put them head-to-head. Here's what we found.

Features: Depth and Breadth Matter

When it comes to features, Azure clearly takes the lead with broader language support, enhanced deployment options, and native tools for more advanced use cases.

• Language Support: Azure supports 133 languages for translation (versus AWS’s 75) and offers containerised deployments for 129 languages, which is critical for clients requiring on-premise or hybrid models.
• Optical Character Recognition (OCR) + Translation: Azure includes built-in OCR capabilities for scanned PDFs in 111 source languages and 87 target languages. In contrast, AWS users need to integrate Amazon Textract to achieve similar functionality, adding friction to workflows.
• Advanced Features: Azure provides auto-language detection, custom dictionaries, and transliteration, whereas AWS offers limited support for these or does not support them at all.

AWS holds its own in a few areas. Its Custom Terminology feature is available for all 75 supported languages and dictionary support is broader.

Key takeaway: For organisations managing diverse content types, file formats, and translation needs, Azure provides a more robust, integrated solution.

Learn More: Translation As a Feature: Your Enterprise Platform Has It, But Are You Managing the Risk

Cost Considerations: A Layered Picture

Both Azure and AWS offer generous free tiers (2 million characters per month for 12 months), making them equally accessible for trials or low-volume use. But costs begin to diverge in production settings.

• Text vs. Document Translation: Azure separates pricing for text and document translation. Text translation is more affordable at $10 per million characters, compared to AWS’s flat rate of $15. For organisations translating large volumes of plain text, Azure’s model offers greater flexibility.
• Custom Translation: Azure remains more cost-effective here, charging $40 per million characters for custom translation, while AWS’s Active Custom Translation (ACT) comes in at $60.
• Training Costs: AWS has the advantage when it comes to training. It doesn’t charge to train or host custom models. Azure, by contrast, caps training at $300 and charges $10 per month for hosting.

Key takeaway: For organisations with high-volume or complex translation needs, Azure’s commitment tiers and volume discounts offer predictable, scalable pricing. While AWS provides negotiable enterprise rates, Azure is generally the more cost-effective option, especially for custom machine translation use cases.

Context Is Everything

Both Azure and AWS are robust platforms built on world-class infrastructure. But the right choice depends on your specific needs:

• Need flexible pricing across different content types? Azure may be the stronger option.
• Want to train custom models without incurring hosting fees? AWS could be the better fit.
• Handling high-volume, multilingual content in regulated environments? Azure stands out with its OCR capabilities, containerised deployment, and broad language support.

LanguageLine Can Help

Choosing a platform is only the first step.

LanguageLine doesn’t just help you choose between Azure and AWS. We help you optimise, operationalise, and scale these tools as part of a broader language access strategy.

This includes:

• Designing workflows aligned with your compliance, quality, and turnaround goals
• Using risk-based routing to decide when machine translation is appropriate
• Integrating custom glossaries, post-editing, and linguistic quality assurance
• Backing every solution with expert linguists and enterprise-grade support

We bring more than technical expertise. We bring strategic insight built on decades of experience with regulated industries, public sector organisations, and global enterprises.

With LanguageLine, you're not just using a translation engine; you're deploying a complete, reliable solution tailored to your goals and guided by our proven expertise.

Case Study: How a Major Credit Card Company Scaled eLearning with Machine Translation and Post Editing

Let’s Talk

Have questions about deploying machine translation securely and effectively? Our solutions architects are here to help you build a programme that balances cost, quality, and control and keeps your stakeholders aligned every step of the way.

To learn more about our content and translation solutions, please visit our website.

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