ML Env

Use casesMarketing

Expand a one-line campaign idea into a working brief

Expands a one-line campaign idea into a structured brief: angle, key messages, deliverables per channel, timeline skeleton and open questions.

How it works
You type something like 'spring clearance on the garden line, mid-March, mostly email and Meta' and it returns a structured brief: proposed angle, key messages, audience segments to consider, a channel-by-channel deliverables list, a timeline skeleton and open questions. It is an interactive thinking partner for the planning hour, not an autonomous planner.
Data you need
The idea, your channels and rough budget bracket, audience notes, and ideally two or three past campaign briefs so the output matches how your team actually works. Without those examples it still works, but expect a fairly generic template on the first pass.
What to expect
This is structure and completeness, not strategy — it will not know that your audience ignores Instagram or that March is your worst month. Its main value is the checklist effect: a channel-by-channel deliverables list makes it easier to spot a missing landing page or email before launch, but you still have to read the list. It does not have ideas about your market.
Where people stay involved
The marketing lead prunes hard. The model lists plausible options with equal confidence whether or not they make sense for your business; choosing is the human's job.

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.

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.

  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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