ML Env

Use casesVoice of customer

Draft a 'what buyers say' summary for each product page

Drafts a short, balanced 'what buyers say' block for each product page from its reviews — into a review queue, never straight onto the site.

How it works
For each product with enough reviews, the large model drafts a short balanced summary of what reviewers consistently praise and criticise ('buyers like the fabric weight; several mention the colour runs darker than photographed'), each point traceable to the underlying reviews. Drafts go into a review queue for human approval, not straight onto the site. It is a copywriting accelerator grounded in real reviews, not an autopublisher.
Data you need
Per-product review sets of reasonable size — a summary of three reviews is worse than just showing the three reviews — and a shop platform where a human-approved text block can be placed on the product page.
What to expect
The compliance bar here is the highest on this list: under the UCPD and the Omnibus Directive, a published summary that cherry-picks praise or misrepresents the balance of reviews is a misleading commercial practice, so the summary must honestly reflect the negatives — which takes discipline when the tool makes glossing easy. The model's known failure modes, omission and over-smoothing, point in the same risky direction. Summaries also go stale as new reviews arrive and need a refresh-and-reapprove cycle.
Where people stay involved
Every summary is approved by a human before publication, and re-approved when regenerated — this is customer-facing marketing text with legal weight, not an internal report.

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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