Let's be real about a situation that happens all the time when scaling large websites: you're managing a global website. Your boss wants the site live in 15 markets by Q2. You've got translators working in Crowdin, content flowing through DatoCMS, and you're supposed to sign off on everything being “good to go”. There's just one tiny problem: you don't speak German, Japanese, Portuguese, or the other 12 languages you're supposedly managing. You can't review the translations yourself. You can't afford to hire 20 native speakers for full-time QA. You can't just trust that everything is fine and hope for the best when the CEO asks if the Spanish site is ready. You need a system that works, and you can set one up using DatoCMS and Crowdin together.

When teams talk about localization, it’s often framed as a finishing step. Ship the product, extract the strings, translate, and move on. However, as Szymon Metkowski, Language and UX Consultant at Metkowski Translations, explains in this episode of The Agile Localization Podcast, that mindset is exactly what causes broken interfaces, confusing microcopy, and lost user trust. In this conversation with host Stefan Huyghe, Szymon unpacks what UX-aware localization really looks like in practice. From why microcopy failures can quietly kill conversions, to how AI can help or hurt depending on how it’s used, the message is clear: if language isn’t considered during design, no amount of post-launch fixes will fully save the experience.

OpenAI hype has made many teams believe that a simple API call to GPT-4o is all they need to translate their product. This might work for a simple one-pager or situations where you don’t necessarily need the translation to feel native to your audience. However, there is a big difference between translating a sentence in a chat box and localizing a product at scale. OpenAI is a powerful engine, but it is not a localization platform. It lacks the infrastructure to manage a product. To truly understand why, we need to look at the gap between a raw AI model and a professional localization ecosystem. Besides the comparison, we want to show you how to get the maximum of both worlds: OpenAI and Crowdin.

In the world of fitness, there is a golden rule: If it’s not on Strava, it didn’t happen. With over 150 million users across 185 countries, Strava is the world’s largest digital community for active people. It is the place where professional cyclists and weekend joggers alike go to hunt for Kudos, battle for King of the Mountain titles, and share their latest Strava Art. In 2025, Strava reached a valuation of $2.2 billion in a funding round led by Sequoia Capital. With nearly $500 million in annual recurring revenue, Strava is the top unicorn of the fitness world. We spoke to Eduardo D'Antonio, Globalization Director at Strava, who created a globalization strategy for them. Learn how Strava created a complex localization strategy using Crowdin and went global in just 6 weeks.

This month’s update is headlined by our official n8n integration, which allows you to build custom, no-code automations. We are also introducing "Predicting Ambiguity" to our AI Pipeline to prevent translation errors before they happen, and launching the Corporate Translation Portal to give your entire organization a secure way to get translations that don't require high-quality standards instantly. From expanding our game localization tools for Xbox and Epic Games to rebuilding our Google Drive integration for enterprise-grade security, here is everything we’ve released this January.

A new case study from Crowdin’s eCommerce client – Suitsupply. Suitsupply is an Amsterdam-born menswear brand. As a premium global retailer, Suitsupply operates under a core principle: everything they do must be done the right way – sustainably, ethically, and with their customers at the heart. This commitment to quality extended even to their global content and localization strategy. To understand how Suitsupply overcame its scaling challenges, we spoke to Samer Mikhaeil, E-commerce Operations Specialist at Suitsupply. He explained that the challenge was that their previous translation management system (TMS) couldn't keep pace. It lacked: - automation - integrations - AI capacity to scale their content. This created a bottleneck of slow, manual processes that prevented them from delivering a consistent brand experience across all markets.