Lokalise Blog - All about localization

Editor’s picks

AI translation vs human translation cost

AI vs human translation cost: How to cut localization costs by up to 97%

As of 2026, the Total Cost of Ownership (TCO) for enterprise localization has shifted from a per-word human model (~$0.20/word) to an orchestrated AI model (~$0.002/word). This 100x efficiency is driven by AI Orchestration, which automates context retrieval (RAG) and eliminates manual project management overhead. This shift comes from AI orchestration. These systems combine large language models with retrieval-augmented context, terminology databases, translation memory, and automated q

Updated on March 26, 2026·Mia Comic

Global Growth & Strategy·Product & News

The global expansion reality check: Where localization is costing companies revenue

Global expansion is back on the table for 2026. But many teams are moving into new markets faster than their localization strategy can keep up. To see how companies are actually preparing for international growth, Lokalise surveyed 500 global business leaders. What we found was a gap between intention and execution, especially around translation quality, regional investment, and budget priorities. For product, marketing, and growth leaders, the takeaway is straightforward: localiz

Updated on May 13, 2026·Brittany Wolfe

headless cms localization workflow tools

Localization Best Practices

Headless CMS localization tools: Build a hands-off translation workflow

Headless CMS platforms are built for speed. You publish once, every frontend updates. But then localization shows up, and the “fast” stack suddenly depends on a rather slow routine: export strings, email a file, chase approvals, import, fix broken formatting, repeat. If you’re a developer or content manager, you’ve felt the cost. Releases slip and translators work from stale content without even knowing it. Engineers get pulled into one-off fixes. Users end up seeing mixed terminology a

Updated on March 27, 2026·Mia Comic

Global Growth & Strategy·Localization Best Practices

6 best translation memory software for every use case

Every time you launch a new campaign, you have to translate everything across multiple languages — product UI, marketing pages, help docs, and more. Without translation memory, translators start from scratch every time — even for content they've already translated. You pay full rates again. Every team uses its own terminology again. And your localization process delays market entry. Translation memory (TM) software stores every translated string as a reusable segment in a d

Updated on June 11, 2026·Shreelekha Singh

AI Translation

The fine-tuning trap in AI translation

Fine-tuning sounds like the clean way to improve AI translation quality. You train the model on your content with the expectation it’ll learn your style. In practice, generic fine-tuning is where enterprise translation programs get stuck. The issue is, the model absorbs everything in the training mix. This includes old releases, mixed brands, and inconsistent phrasing, which means you end up with contextual contamination. That’s when the model starts making confident ch

Updated on February 11, 2026·Mia Comic

AI Translation

Term base best practices: How to build a living terminology system

Most term bases fail because they live somewhere where nobody works. A spreadsheet gets created, a few people bookmark it, and then the real work happens in the editor, in Slack, in Figma, and in whatever AI tool is generating the next draft. That gap is expensive. Terminology drifts, reviewers rewrite the same phrases, and “small” naming mistakes turn into brand inconsistency in translation, SEO issues, and support tickets that shouldn’t exist. This guide covers term base best pr

Updated on February 10, 2026·Mia Comic

ChatGPT vs. a localization platform: Which translation solution is right for your business?

Translation used to be a bottleneck for businesses looking to grow internationally. Thanks to AI, now it can happen in seconds, which changes how teams make decisions about localization. ChatGPT can produce quick translations for many everyday needs. But when quality requirements rise or the workload grows, teams start running into familiar problems: inconsistent terminology, formatting and placeholder issues, missing context, and a lack of review workflows. That’

Updated on February 3, 2026·Mia Comic

Automating context management in translation.webp

AI Translation

How to automate context management in translation

Most localization teams struggle with finding the right context. Translators jump between the TMS, Jira, Figma, Slack, and old docs just to understand a single string. Reviewers approve copy without ever seeing the screen it lives on. Developers spend hours every week explaining where text appears and what it must not break. All of this context switching in localization slows releases and drives up translation rework. On paper, it looks like “bad translation.” In reality, it

Updated on February 3, 2026·Mia Comic

Editor’s picks

Developer Guides & Tutorials

Lokalise articles - Page 3

Editor’s picks

Laravel localization: A step-by-step guide with examples

How to translate languages in Python with Google Translate and DeepL (plus more)

Angular i18n: internationalization & localization with examples

AI vs human translation cost: How to cut localization costs by up to 97%

The global expansion reality check: Where localization is costing companies revenue

Headless CMS localization tools: Build a hands-off translation workflow

6 best translation memory software for every use case

The fine-tuning trap in AI translation

Term base best practices: How to build a living terminology system

ChatGPT vs. a localization platform: Which translation solution is right for your business?

How to automate context management in translation

Editor’s picks

Laravel localization: A step-by-step guide with examples

How to translate languages in Python with Google Translate and DeepL (plus more)

Angular i18n: internationalization & localization with examples

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