Draft alt text for product images from the product data you already have
Composes alt text from product titles, attributes and image metadata — a bulk improvement over empty alt attributes, not true image description.
- How it works
- No vision model is currently served, so this is honestly text-only: the model composes alt text from the product's title, attributes and image filename or position ('front view', 'detail'), producing concise, screen-reader-friendly descriptions instead of empty alt attributes or keyword stuffing. It works best on plain catalogue shots of the product itself, where the record and the photo describe the same thing.
- Data you need
- Product title and attributes per SKU, plus whatever image metadata you have — filename conventions, image order, variant association. Almost every shop platform exports this.
- What to expect
- A useful bulk upgrade for catalogues full of empty alt attributes, at very low cost. But the model has not seen the image: if a photo shows the blue variant while the record leads with red, the alt text will be wrong. Results depend heavily on your image-naming and variant-linking discipline — many shops have little, and get correspondingly generic output. If your product titles are keyword-stuffed, the alt text inherits that unless you clean the inputs. Do not treat it as true image description or, by itself, as accessibility compliance — if you need real image description, wait until a vision model is on the roster.
- Where people stay involved
- Someone should scan the output, especially for lifestyle and ambience shots, where text derived from product data can plainly mismatch what the photo shows.
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
Writing in your voice
No benchmark measures this
There is no benchmark for this and there is unlikely ever to be one. Anyone who shows you a chart ranking models on marketing copy has drawn it from a model's opinion of another model.
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.
Also in catalogue content
All 14 →Turn supplier spec sheets into first-draft product descriptions
Drafts product descriptions overnight from the supplier data you already hold, in your shop's voice — a review job instead of a from-scratch writing job.
Pull sizes, materials, colours and other attributes out of messy supplier text
Reads free-text supplier info and fills a fixed field list — material, dimensions, colour, EAN — returning structured rows for your PIM or shop admin.
Sort new products into the right category in your shop
Embeddings shortlist the most plausible categories for each new product; a small model picks one and explains why — workable even on large category trees.
Write page titles and meta descriptions for every product and category page
Drafts an SEO title and meta description for every page from real product attributes, within your character limits — economical even at long-tail scale.
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