A step by step guide of what to expect when building AI translation on a bare model API – told as the decisions you’d make yourself, with the bill attached.
At 50 strings you read every line yourself; at 5,000, you can’t. Checks catch problems like missing text or broken formatting, but it won’t tell you if the translation is good.
Brand consistency requires building four systems – glossary, style guides, translation memory, and contextual retrieval. Each with its own upkeep burden.
Shipping quality translations at scale requires an AI evaluation system. That comes with its own price tag.
Nothing has to break for the work to continue. Models change, rules age, and your benchmarks go stale. You are now in charge of a full-time operation.
Get the step-by-step guide: what you’ll build, what you’ll maintain, and what it will cost you.
The model API is the cheap part. This is what it actually costs to automate AI translation, give it your company's context, learn to trust the output, and keep the system running.
Read more The real cost of building in-house AI translation
The meeting is going well. Translation costs are down, turnaround times are shorter, and AI is taking on more of the work. Then your VP asks a question: “How do you know the quality of AI translations is good enough?” The expensive part usually starts once you try to answer that question inside a system you built: the real cost of buildi
Read more AI translation quality evaluation: Looking beyond human review
Managing translations in a growing product can quickly become repetitive and error-prone, especially when dealing with frequent content updates or multiple languages. Lokalise helps automate this process, and with the right setup you can build a full AI-powered translation pipeline that runs with minimal manual input. The pipeline is the cheap part. What it costs to keep is
Read more Building an AI-powered translation flow using Lokalise API and webhooks
Behind the scenes of localization with one of Europe’s leading digital health providers
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