Put safety notices and staff info into every language your team speaks
Translate internal notices, safety instructions and onboarding basics into every language your warehouse or shop-floor team actually speaks.
- How it works
- Warehouse and shop-floor teams in the EU are routinely multilingual — Dutch, Polish, Romanian, Ukrainian, English. The large model translates internal notices, safety instructions and onboarding basics into each target language, keeping the formatting, so everyone reads the rules in a language they actually understand. Batches run on your own documents in the Amsterdam data centre; nothing goes to an external translation service.
- Data you need
- The source documents in one language, and a native-speaker colleague per target language for the initial quality check.
- What to expect
- Good working translation for internal use, strongest between major EU languages; smaller languages need closer review. It is not certified translation — employment contracts and legal documents still need a professional. Idioms and company slang translate badly without a glossary, and a forklift safety rule with a flipped negation is not a theoretical risk, which is exactly why the native-speaker check exists.
- Where people stay involved
- A native speaker reviews at least the first batches and anything safety-critical before it goes on the wall.
Which model, and what it costs to run
Qwen3-8B
This job runs in bulk rather than to a waiting person, so size is not the constraint — we take the strongest independent score on sticking to the document that we may serve freely and that fits on a single card.
- 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.
Sticking to the document
Measured on public documents, by a model acting as judge. Read it beside the answer rate: the lowest hallucination rates on this board belong to models that simply decline more often.
Independent measurement · Vectara · board updated May 11, 2026
Reading a long document whole
No benchmark measures this
No current benchmark ranks today's open models here. HELMET showed that the popular test — finding a planted sentence — predicts nothing, and its own table has not been rerun on 2026 models.
So we do not show a chart here. We measure it on your own content, in the first week, and you see the result before anything ships.
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.
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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.