ML Env

Use casesSelling across languages

Translate marketing emails and campaign copy in your brand voice

Drafts per-market versions of newsletters and campaign copy, with your voice guide and past approved translations steering the tone.

How it works
You submit the source campaign together with a short voice guide per market and two or three past approved translations as examples; the large model drafts each market's version with that tone carried over instead of defaulting to flat textbook prose. It runs as a batch job — campaign in, per-market drafts out, into your review flow. Price and date fields are merged in from data, not translated.
Data you need
The source campaign, a short written voice guide per market (formality, exclamation-mark tolerance, do/don't phrases), and ideally a few past approved translations as examples. If no voice guide exists yet — common in smaller shops — you can start without one and build it from the reviewer's edits over the first few campaigns. Legally sensitive promo mechanics (discount conditions, prize-draw terms) go in as fixed approved text, not ad hoc translation.
What to expect
Wordplay, rhymes and cultural references usually do not survive translation — expect to rewrite headlines per market rather than translate them, because the model will produce something plausible for a pun, and plausible and dead on arrival are compatible. Quality is strong in the major EU languages (German, French, Spanish, Italian, Dutch, Polish) and noticeably weaker in smaller ones, where your reviewer will be editing more heavily.
Where people stay involved
A market-fluent person signs off before sending — marketing copy is exactly where literal translation embarrasses you. If you have no in-market reviewer for a language, do not ship machine-drafted campaigns in it.

Which model, and what it costs to run

Qwen3-8B

No public benchmark ranks this particular job, so this is not a leaderboard pick — it is where we would start: small, permissively licensed, and strong wherever it has been measured. We prove it on your own content before anything ships.

Licence
Apache-2.0
Weights at 4-bit
5 GB
Context
32K tokens
Publisher
Alibaba (Qwen Team)

What the hardware costs

One 48 GB card holds it

Rent in the EU
$1.60/hrScaleway, Paris (PAR2)
Buy the card
$7,569new, one-off
Or rent it by the token
$0.04 / $0.04per M in / out · DeepInfra

Hardware only, third-party prices from 2026-07. The figure excludes the KV cache, which grows with context length and how many people use it at once — sized properly in a conversation, not guessed here. Renting by the token is cheaper up front; why our customers still self-host is below.

European languages

openGPT-X European LLM Leaderboard · FLORES-200 translation, BLEU · 10 models

Previous-generation models — the board has not been rerun on the 2026 roster, so read the order of magnitude, not a ranking of today's models. And BLEU scores adequacy, not tone: it cannot tell you whether the register fits your brand. That is judged on your own copy.

EuroLLM-9B25.6 · EULlama 3.1 70B24.3Mixtral 8x7B17.3Mistral-Nemo 12B17.1Teuken-7B sigma17 · EULlama 3.1 8B17Llama 3 8B15.9Teuken-7B v0.615 · EUCommand-R 35B13.8Mistral-7B v0.313.3
Higher BLEU is closer to a human translationEU-sovereign model

Independent measurement · openGPT-X / Fraunhofer IAIS · captured 2026-07-10

The API is cheaper per token. Here is why our customers don't use it.

We will not pretend otherwise: renting a model by the token from a serverless API costs less per million tokens than a card we run for you. We show that price on every use-case page. What it does not include is the part a shop with a customer database actually pays for.

  1. 01

    Your data never leaves hardware you can point at

    A serverless "we don't retain your data" is a clause in a contract. Running the model on a card in Amsterdam is a fact of architecture: your catalogue, tickets and customer records are never sent to a third party at all. For a GDPR audit, that is the difference between a promise and a floor plan.

  2. 02

    The price cannot move without your say-so

    A serverless rate card is somebody else's lever. The provider can raise the price, retire the model, or change the terms, and your cost moves with it. The same model on the same card costs the same next year — you own the number.

  3. 03

    The model cannot be taken away

    Hosted APIs deprecate models on their own schedule; the one you built on can be gone in a quarter. An open-weight model on your own hardware runs for as long as you keep the lights on. No vendor can end-of-life it out from under you.

And the price gap closes with volume: past a card you keep busy — very roughly four billion tokens a month — owning is cheaper outright, even before the three reasons above.

Getting a case like this one from a conversation to production takes about two months, and you can stop at the end of any phase.

Next

Tell us what your team does by hand

Describe the process that takes the most time. We will say plainly whether a model is the right tool for it.