Use casesSelling across languages
Read all your reviews, in every language, as one weekly digest
Translates and tags every review from every market, then writes a short weekly digest in your language with translated quotes attached.
- How it works
- You feed in review exports — your shop's review table, marketplace CSVs, a Trustpilot export. The small model translates and tags each review by product, topic (sizing, delivery, quality, service) and sentiment; the large model then writes a short digest in your language: what changed this week, which products draw complaints in which market, with verbatim quotes translated and the originals attached. You stop being blind to what French customers say because nobody on the team reads French.
- Data you need
- A review export or feed you can produce yourself — marketplace CSVs, a Trustpilot export, or your shop's review table. There is no built-in scraper; you supply the files. No historical labelling is needed. If you only get a handful of reviews a month, this is overkill — read them with one-off translation instead.
- What to expect
- Sarcasm and irony translate badly and can flip sentiment tags, and tagging is weaker on very short or slangy reviews and in lower-resource languages than in English, German or French. Counts in the digest are the model's counts — treat them as indicative, not reporting figures — and duplicate or cross-posted reviews inflate apparent complaint volume unless you deduplicate the export first. Star-rating maths should come from your database, not the model.
- Where people stay involved
- Someone reads the digest and decides what to act on. Before acting on a specific claim — 'the zip breaks' — check the underlying reviews, because a summary can over-weight one vivid complaint.
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
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.
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.
- 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.
- 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.
- 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.
Also in selling across languages
All 16 →Translate your product catalogue with your own word list locked
Draft catalogue translations into EU languages with your brand, material and category terms applied from your own glossary, ready for review.
Let your support team chat with customers in any EU language
Live translation between agent and customer in chat — the customer writes in their language, your agent reads and replies in their own.
Draft replies to customer emails in the customer's language
Reads a customer email in any language, pulls the relevant passages from your policies, and drafts a reply in their language for an agent to send.
Check translated pages before they go live
A pre-publish check over translated pages: leftover source sentences, mixed formality, wrong units and currency, broken placeholders, glossary breaches.
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