In the fitness world, the unofficial motto is: “If it’s not on Strava, it didn’t happen”. As the world’s largest digital community for active people, Strava supports over 150 million users across 185 countries.
When the company reached a $2.2 billion valuation and acquired the UK’s leading personalized running coach app, Runna, they faced a massive challenge: building a localization tech stack that could scale at speed.
Challenge: Speed vs. Complexity
When Eduardo D’Antonio joined Strava as Globalization Director, he inherited the task of localizing both Strava and the newly acquired Runna. The requirement was clear: build a globalization strategy from scratch and take Runna global first in record time.
Eduardo needed a translation management system that offered more than just a translation memory. He needed a central hub capable of handling 25+ different content repositories, providing deep automation, and integrating with the engineering stack.
Most traditional TMS providers quoted implementation timelines of 6 to 9 months – a timeframe that didn’t fit Strava’s rapid growth trajectory.
Solution: Choosing Crowdin for Agility
Strava chose Crowdin as the center of their localization architecture. The decision was driven by four key pillars:
- Integrations: The ability to connect Figma, GitHub, Contentful, Intercom, Iterable, Intento, Kevel, Strapi, and other tools into one ecosystem.
- Implementation Speed: While others spoke in months, Crowdin allowed for a setup measured in weeks.
- Cost-Effectiveness: A scalable pricing model that matched their growth.
- Customization: A partner-level relationship where feature requests were heard and implemented quickly.
Results: Global in 6 Weeks
By leveraging Crowdin’s AI-driven capabilities and automated connectors, Strava achieved what seemed impossible. They built their entire globalization department and tech stack in just six weeks.
Runna was localized into seven key languages (Dutch, French, German, Italian, Japanese, Portuguese, and Spanish) almost instantly. By utilizing Neural Machine Translation (NMT) and Large Language Models (LLMs) via Crowdin, Strava optimized their workflow to be faster, better, and significantly more cost-effective.
Impact in Numbers:
- Implementation Time: 6 Weeks from start to finish.
- Volume: Over 35 million words managed within Crowdin.
- Strava Reach: 13 languages supported.
- Runna Reach: 8 languages launched.
Speed as a Competitive Advantage
By removing the language barrier, Strava and Runna have unlocked localized coaching and community features for millions of athletes, driving global engagement in their users’ native languages.
The success of the Strava and Runna project proves that the traditional, slow-moving localization model is being replaced by agile, AI-powered ecosystems. With the right technology partner, global expansion doesn’t have to take years – it can happen in weeks.