Show genuinely related products on every product page, even for brand-new items
Precomputed related-products sections from description similarity, so brand-new items with zero sales history get sensible suggestions from day one.
- How it works
- 'Similar products' are precomputed for every item from description and attribute embeddings, so new products with zero sales history immediately get a sensible related-items section. This is content-based similarity — the standard cold-start complement to behavioural 'customers also bought' data, which small shops rarely have enough of. It runs as a periodic batch job; the output is plain product-ID lists your shop template renders.
- Data you need
- Product titles, descriptions and attributes, exportable from any standard shop platform. The richer and more distinctive the text, the better the neighbours; thin or copied supplier descriptions give weak results. A category structure helps constrain suggestions to sensible sets.
- What to expect
- Solves the cold-start problem well: every product gets neighbours from day one. But textual similarity is not purchase intent — the nearest neighbour of a product is often its direct competitor, not its accessory, and 'frequently bought together' needs order history, not embeddings; we won't dress this up as behavioural recommendations. Similarity is computed from text only (no vision model is served), so visually driven ranges — fashion, decor, jewellery — where the photo carries the meaning will match less well than text-rich catalogues. Shops whose catalogue is mostly boilerplate supplier text will see generic or near-duplicate suggestions until descriptions improve.
- Where people stay involved
- A merchandiser sets the rules layer on top: exclude out-of-stock, cap price deviation, and decide whether direct substitutes may appear next to the add-to-cart button, since they compete with the sale. Spot-check suggestions for top sellers before switching the section on.
Which model, and what it costs to run
Qwen3-8B
No public benchmark ranks this particular job, so this is not a leaderboard pick — it is where we would start: small, permissively licensed, and strong wherever it has been measured. We prove it on your own content before anything ships.
- 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.
Search over your own catalogue
No benchmark measures this
MTEB and BEIR rank embedding models, but their evaluation sets sit inside the models' training data. Scores fall by double-digit nDCG on a private corpus, so the ranking does not transfer to your catalogue.
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 search & discovery
All 12 →Let shoppers search in their own words, not your catalogue's words
Meaning-based search next to your keyword search, so loosely phrased queries find the right products even when no words match your titles.
Stop losing sales on 'no results found' pages
When keyword search returns nothing, the query falls through to embedding search and shows the closest products instead of a dead end.
Handle misspellings, synonyms and mixed-language queries without maintaining dictionaries
Multilingual embeddings put 'trui', 'pullover' and 'jumper' near each other, cutting the need for hand-maintained synonym lists in an EU shop.
Let customers describe the look and find matching products
Shoppers describe a style in plain words — 'minimalist Scandinavian living room, oak and off-white' — and get products whose text matches it.
Next
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.