Use casesOperations & logistics
Spot what changed in a new supplier price list
Diffs a supplier's new price list against the old one: what went up and by how much, what was discontinued, what is new, where MOQs changed.
- How it works
- The large model extracts each price list — often a differently formatted PDF or Excel every time — into a normalised table, then diffs the new version against the previous one. The change report leads with the SKUs you actually buy: price rises, discontinued items, new items, changed minimum order quantities. You read one report instead of manually comparing two forty-page PDFs.
- Data you need
- The old and new price lists as text-layer PDF, Excel or CSV, plus an export of the SKUs you buy — from your ERP, purchasing spreadsheet or webshop. Scanned lists need OCR first. Currency and VAT-inclusive/exclusive handling should be set by explicit rules, not left to the model.
- What to expect
- Reliable on straightforward tables; weakest on merged cells, tiered pricing and footnote conditions, which should be hand-checked. If a supplier renumbers or renames SKUs between versions, the diff reports them as discontinued-plus-new rather than matching them, and pack-size or unit changes can look like price changes — a buyer needs to sanity-check those rows.
- Where people stay involved
- A buyer reviews the change report before updating purchase prices or webshop prices anywhere. An extraction error in a price table is exactly the kind of mistake that must be caught before it propagates.
Which model, and what it costs to run
Qwen3-32B
This job runs in bulk rather than to a waiting person, so size is not the constraint — we take the strongest independent score on structured output and tool calls that we may serve freely and that fits on a single card.
- Licence
- Apache-2.0
- Weights at 4-bit
- 18 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
- $0.12 / $0.12per M in / out · Nebius AI Studio · EU
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.
Structured output and tool calls
Measured in a sandbox, on somebody else's functions. It tells you which models are capable of the shape of the job, not which one will survive contact with your API.
Independent measurement · UC Berkeley (Gorilla project) · board updated 2026-04-12
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 operations & logistics
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Tags every email to your purchasing or logistics inbox — delay, invoice, complaint — and routes it to the right queue, on your own infrastructure.
Pull order lines out of supplier PDFs and emails into a spreadsheet
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Check supplier order confirmations against what you actually ordered
Compares each supplier confirmation against your purchase order and flags changed prices, quantities, substituted items and pushed delivery dates.
A morning digest of every delivery problem, in one email
One plain-language email each morning: the day's delayed, failed and held shipments, and the customers and orders behind each tracking number.
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