How to price data

Most people asking how to price data jump straight to a figure. The figure matters less than the shape of the deal.

The same archive can be a single payment, a yearly fee, a charge per thousand rows or an exclusive grant, and each shape changes what a buyer will accept, how much paperwork you need and how predictable the money is for the seller.

What marketplaces show

Marketplaces make this visible. AWS Data Exchange lets a provider publish subscription offers with a separate price for each term, from one to 36 months. Snowflake Marketplace supports flat-fee subscriptions and usage-based plans built from a per-query charge, a monthly fee and a monthly cap. Private deals are more flexible still, but the same building blocks apply.

A short rule of thumb: the less the data changes and the more a buyer wants to own its advantage, the more the deal tilts toward a one-off exclusive licence. The more it changes and the more buyers there are, the more it tilts toward subscriptions or usage pricing sold to many.

1. 1

   Describe the unit you are selling

   Write one sentence a buyer could sign: 'Twelve years of anonymised support tickets, about 1.4 million threads, delivered once as JSON'. If you can't name the unit, you can't price it.

2. 2

   Pick the model from how the data behaves

   Use the table above. Static data leans one-off, live data leans subscription, sliceable data leans per unit, data used in a warehouse leans usage tiers.

3. 3

   Estimate the buyer's alternative

   Ask what it would cost the buyer to collect or build the same thing, and how long it would take. That replacement cost is a floor most serious buyers recognise.

4. 4

   Estimate the buyer's upside

   What does having it now do for them: a better model, a faster product launch, a sharper forecast? You rarely get a share of that upside, but it tells you how high the ceiling sits.

5. 5

   Look for public reference points

   Listings on public marketplaces and published buyer programmes show what comparable data is offered at. Treat them as asking prices, not closed deals.

6. 6

   Quote a range with the levers named

   Give the seller and the buyer a range and say what moves it: exclusivity, a longer term, cleaner documentation, a larger sample. A range survives diligence; a single number usually doesn't.

• Scarcity: data a buyer cannot get elsewhere, such as private codebases or internal records, is priced against nothing.
• Rights: a clean chain of ownership and permission to license raises confidence; any doubt lowers the offer or ends it.
• Personal data: removing it or de-identifying it costs work up front but widens the pool of buyers.
• Documentation: a data dictionary, a sample and a description of how it was collected shorten diligence.
• Freshness and history: long time series and recent records each have buyers; stale snapshots have fewer.
• Exclusivity and term: longer and narrower grants are worth more to the buyer and cost the seller future sales.

• Pricing on volume alone. A terabyte of noise is worth less than a clean gigabyte a buyer can use tomorrow.
• Using the seller's sunk cost as the price. What it cost to collect sets expectations, not value.
• Granting exclusivity for free because a buyer asked. Exclusivity is the most valuable concession in most data deals.
• Leaving the unit vague. 'Per record' means nothing until you say what a record is and what happens to duplicates.
• Ignoring the broker's fee. If you are brokering, agree your commission with the seller before quoting the buyer, so the range covers it.

Marketplace mechanics also change. The pricing options described here come from each platform's own documentation on the date below; check the current terms before relying on them.