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Longer notes from the market for teams buying training data: how sourcing routes compare, what public deals reveal about prices, and the checks worth doing before you sign.

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    About 4 minutes

    Licensed, collected or synthetic: three ways to source training data

    You can license data that already exists, pay a vendor to collect or label new data to your specification, or generate synthetic data. Pick by the gap in your model: licensing buys breadth, collection buys fit, and synthetic data buys scale once you can check it against real examples.

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    About 4 minutes

    What the 2024 to 2026 data deals tell us about prices

    The public figures run from about $25,000 for one wound-down startup's codebase to $203 million in Reddit's licensing contracts. They show that price follows uniqueness and scale, that big deals are multi-year, and that most buyers will never see a comparable number, because nearly all deals stay private.

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    About 4 minutes

    A data provenance checklist for training sets

    Provenance is the paper trail that shows you were allowed to train on a dataset. Ask every vendor for the same five things in writing: the origin of each component, a license that names AI training, contributor agreements, the method used to remove personal data, and a manifest of exactly what was delivered.

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    About 4 minutes

    Buy a dataset off the shelf or commission one?

    Buy off the shelf when a catalog dataset already matches your task, language and label schema closely enough to pass your evaluation, because it is faster and usually cheaper. Commission when it does not, when you need ownership or exclusivity, or when the behavior you want has never been recorded. Test a sample of the catalog set before deciding.

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ZIP code first, then an email for our answer, then a sketch of the dataset. A person here compares the sketch with the vendors in the index and writes back naming the ones whose own terms fit.

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