If you’re a tech leader building multilingual platforms or products, then you know that you need a translation API. But do you know how much depends on the choice of API that you make? Of all the elements of production infrastructure that you need to juggle, this can feel like it should be the simplest. There are lots of translation APIs available, and at first glance, they seem pretty similar. There are also plenty of ways to avoid making a choice at all, and just going with what you already have available. APIs from general-purpose LLMs like OpenAI, Claude or Google Gemini offer translation, so if you already have one of these integrated, you can add it with an extra API call. Your procurement team might have committed spend with a cloud platform that could be used for that hyperscaler’s translation API. All easy, all involving little or no friction. Until, that is, you go into production and start discovering how your translation API performs in practice, at scale. That’s usually the moment when the consequences of not evaluating options, and assessing them against your specific requirements, make themselves known. And it’s not pretty. The risks of badly scoped translation APIs Choosing the obvious solution risks escalating latency that makes products unusable. It can expose you to hidden scaling costs that put profitability at risk. And if it introduces unpredictability in translations, it can cause serious reputational damage. If you’ve tied your architecture to a badly scoped API, it’s costly and time-consuming to unpick your dependencies once these problems arise. This is why DeepL has just launched a Buyer’s Guide to Translation APIs . It gives technology leaders all the background they need on the AI translation landscape, the differences between translation API types, the sections they need in an RFP, and the questions they should ask in each of them. The first thing to understand is that there are crucial differences between the four main types of Translation API: free tools (best treated as a compliance risk), hyperscaler APIs (Amazon Translate API, Azure Translator API), General-purpose LLM APIs (Open AI API, Claude API, Google Gemini when accessed through an API) and APIs from purpose-built Language AI platforms, like DeepL . These differences play out across many different dimensions of API performance, with a real impact on your business model. They impact cost and scalability, the terms you can agree with customers, the sectors you can compete in, your developer experience and pipeline, and your capacity for evolving and innovating in the future. Perhaps the most important elements to understand are how different translation APIs balance quality and latency, how predictable their translations will be, and how much control you’ll have over them. Beware quality that comes with escalating latency costs

Specialized Language AI and General-purpose LLMs deliver the highest quality in benchmark tests, but there’s a crucial difference in how they achieve that quality, which has big implications for API deployments. To achieve similar translation quality to DeepL, general-purpose LLMs need to operate in high-reasoning mode. This comes with a computationally expensive inference workload, which in turn increases latency. Compared to DeepL, OpenAI GPT-5.2 is 4x slower, Claude Opus 4.6 is 6x slower and Google Gemini 3.1 Pro is 29x slower. The impact of this multiplies in production. A 4x higher latency might not sound so bad, but in production environments the consequences escalate quickly. You’re dealing with a 4x higher inference window, which exponentially increases concurrent connection requirements. Limits are exceeded, errors escalate, and latency increases again. Before long, multilingual products and translation pipelines start to become unworkable. Are translations predictable enough for your pipeline? If your deployment pattern depends on translation consistency, then the differences between different types of API become even more significant. The probabilistic nature of general-purpose LLMs trades away determinism. Translations may be good, but they won’t all be good in the same way, and this wreaks havoc with products that rely on translation consistency, or downstream comparisons of translated strings. If you want to add glossaries, that’s a separate API call with extra costs, greater latency and more complexity. In contrast, specialized APIs like DeepL come with customization features to determine output already built in. They include glossaries, translation memories and style rules. Besides the highest quality and the lowest latency, they also offer the most predictable output. They are the only API option that doesn’t require these elements to be traded-off against one another. What impact will inference workloads have on pricing? This combination of quality, low latency and predictability impacts on product viability, customer experience and reputation. It also impacts directly on the bottom line. Bigger inference workloads for general-purpose LLMs don’t just mean increased latency. With token-based pricing they mean unpredictable costs and greater financial risk as well. Unlike with character-based pricing, the amount you pay is not connected to the output you generate, or what your customers pay for. This all means that your choice of API can have a significant impact on margin and profitability. Choosing your translation API is a decision worth taking. We created our guide to make it easy to take. Read it in full.