ML Env

Use casesVoice of customer

Read customer feedback in any EU language without translating it first

Tags, clusters and summarises customer feedback across EU languages at once, with output in English and original-language quotes preserved.

How it works
A shop selling into five EU countries gets reviews in five languages, and the usual result is that only the languages the team speaks get read. The Qwen models handle major European languages natively and BGE-M3 embeddings are multilingual, so tagging, clustering and digest-writing run across the whole corpus at once, with output in English — or whichever language the team works in — and original-language quotes preserved alongside. Less a separate use case than the property that makes every other job on this list work for a cross-border shop.
Data you need
The same feedback exports as elsewhere, with a language or country field per item if available; language is detectable from the text if not, with occasional errors on very short items.
What to expect
Coverage is strongest for the widely spoken languages — German, French, Spanish, Dutch, Italian, Portuguese, Polish — and noticeably weaker for smaller EU languages such as Estonian, Maltese and Slovene; spot-check on your own reviews before trusting any of them, and be especially sceptical of the smaller ones. Idioms and sarcasm suffer more in cross-language analysis than in-language, and theme counts can skew if one language community reviews at a different rate than another, so report per country where volumes allow.
Where people stay involved
For any decision hinging on a specific foreign-language complaint, have a speaker — or at least a second translation — verify the model's reading before acting. Nuance, idiom and regional consumer-law vocabulary are where quality drops.

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

Vectara Hallucination Leaderboard · HHEM · 17 of 18 models measured

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.

Phi-43.7% · answers 80.7% ⚠Llama 3.3 70B4.1% · answers 99.5%Gemma 3 12B4.4% · answers 97.4%Qwen3-8B4.8% · answers 99.9%Mistral Small 3.25.1% · answers 97.9%Granite 4.0 Small5.2% · answers 100%DeepSeek-V3.25.3% · answers 96.6%Qwen3-14B5.4% · answers 99.9%
Shorter is betterFree to serveConditions apply⚠ answered under 95%

Independent measurement · Vectara · board updated May 11, 2026

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.

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