Lokalise gives developers two programmatic ways to manage localization workflows: the REST API and the MCP Server. In any MCP vs API localization decision, the key point is that one does not replace the other. The REST API is built for scripted, repeatable, deterministic automation, such as CI/CD pipelines, batch imports, and webhook-driven deployments.
How to build the business case that turns localization from a line item into your highest-return growth lever. Your board wants growth from global markets. Your competitors are already there. And your localization team has the tools to deliver. So why is localization still stuck in the "cost center" conversation? It's a familiar tension. The data overwhelmingly shows that localization drives revenue — through discoverability, conversion, and loyalty. Yet in too many organization
You'd think speaking the same language would be enough. But when Apple—one of the most recognized brands on the planet—ranks dead last for localization in the UK, a native English-speaking market, it's clear that "going global" takes a lot more than translation. Lokalise surveyed 1,000 consumers across eight countries to find out which brands actually feel local, and here's what we found. Key takeaways McDonald's ran
GenAI isn’t just changing how translations are produced. It’s reshaping the entire localization workflow. Most teams didn’t build their workflows for this shift. They’re still relying on a mix of spreadsheets, standalone MT engines, and manual file handoffs. It’s familiar, and for a while, it worked. But it was never designed for the scale teams are dealing with now.
A translation changed, but no one knows who did it or when. The release is already live. What do you do? When multiple contributors work on the same project, there’s a good chance that strings get edited, approved, overwritten, or renamed. Without a clear localization audit trail, small changes turn into production issues, and tracking them down takes time you don’t have. This lack of visibility slows you down significantly. It makes troubleshooting harder, weakens accountability,
LLMs are fluent in generating outputs, but they're not faithful to your brand. When a general-purpose LLM translates your product UI into 15 languages, it doesn't know which terms are trademarked, which phrases have legal restrictions, or which features are deprecated. It makes a statistical guess. At scale, this guesswork can lead to major inconsistencies, compliance risks, and a post-editing overload. The challenge: AI models’ probabilistic outputs are not ideal
Your campaign killed it at home. Then you launched it in Brazil, and the tagline became a punchline. Sound familiar? Nearly 3 in 10 marketing and company leaders say it's happened to them, and the cleanup cost thousands of dollars. Lokalise surveyed 392 marketing and sales leaders who localize content across multiple countries and languages to find out what actually goes wrong, and what they'd do differently. What came back wasn't a polished post-mortem. It was an honest reckoning from
You're optimizing for AI search. But are you doing it in every language that matters? We surveyed 1,000 global marketing, SEO, and localization leaders and found that brands investing in localized content are already seeing stronger AI search visibility. Meanwhile, companies that haven't started optimizing say budget is the biggest thing holding them back. Here's what the data shows. Key takeaways 63% of global companies a
Behind the scenes of localization with one of Europe’s leading digital health providers
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