How much is your company's data worth to AI labs?
What drives the value of business data to AI labs, what raises and lowers the price, and how to get a reliable valuation before you sell.
By Databounties Editorial Team · Updated 6 October 2026 · 7 min read
Key points
- There's no public price list. Value depends on the dataset itself, and anyone quoting a price before seeing your data is guessing.
- The biggest price drivers are scarcity, sector, depth of history, volume, data quality and exclusivity.
- Specialist operational data (lab, process, financial, engineering) is worth far more than generic business data.
- Exclusive licences usually command a substantial premium over non-exclusive ones.
There's no public price list for business data. The big licensing deals between AI companies and publishers or platforms have been widely reported, but deals with specialist businesses are private, and what any one dataset is worth depends on what's actually in it. This guide explains what drives the value of company data, and how to get a valuation you can rely on.
What raises and lowers value
| Lower value | Higher value |
|---|---|
| Generic records that are easy to find elsewhere | Specialist records that have never been public |
| One system, under three years of history | Several linked systems, five or more years of history |
| Final results only | Inputs, decisions and outcomes, including failures |
| Inconsistent fields and big gaps | Consistent structure and clear definitions |
| Unclear ownership or restrictive contracts | Clear rights and a documented anonymisation method |
| Already licensed widely | Available exclusively |
The six factors that drive price
1. Scarcity
The most important factor. If buyers can get similar data elsewhere, or generate it synthetically, the price falls. Records of specialist work that has never been published, such as failed experiments, fab yield excursions or the judgement calls in an audit, are scarce by nature.
2. Sector
Labs are building AI for specific kinds of work. Sectors where AI is expected to have a big economic impact and where real data is hard to find (life sciences, chemistry, semiconductors, finance, legal, insurance) attract the most interest.
3. Depth of history
Ten years of consistent records show how work changes over time, across market cycles and through edge cases. Value grows with history, especially beyond five years.
4. Volume and coverage
More records help, but coverage matters more. Data that captures the full workflow (inputs, decisions, outcomes) is worth more than a single table of final results.
5. Quality and structure
Consistent fields, few gaps, clear definitions and links between systems (an invoice tied to an order tied to a delivery) all raise the price. Messy data still sells, but the cost of cleaning it comes off the price.
6. Exclusivity and licence terms
An exclusive licence gives one buyer data its competitors can't get, and is priced accordingly. Non-exclusive licences pay less each time but can be sold more than once. Term length, permitted uses and refresh rights also affect the price.
What lowers the value
- Unclear ownership, or contracts that restrict sharing.
- Personal data that can't be anonymised without destroying the useful signal.
- Short history, or big gaps in it.
- Data that is mostly generic, such as standard marketing emails or boilerplate documents.
- The same data already being widely licensed.
How to get an accurate number
Be wary of anyone who quotes a price from a form. A reliable valuation needs someone to look at the actual schema, volumes and a sample, under NDA, and test it against what buyers are currently paying for. That's what our free valuation call is for. Before that, read the step-by-step guide to selling company data.
Sources
- 1.
- 2.The price of AI training data, from $5M to $250M
Quartz, 2026
- 3.Will we run out of data? Limits of LLM scaling based on human-generated data
Epoch AI (Villalobos et al.), arXiv, 2022
Frequently asked questions
Is there a typical price for a company dataset?
Not a reliable one. Deals are private, and prices depend heavily on sector, scarcity, history, volume and exclusivity. Two datasets from the same industry can differ in value many times over. The only reliable way to price yours is to have someone review the schema and a sample under NDA and test it against current buyer demand.
Is data priced per record?
Sometimes, especially for large uniform datasets, but most company data deals are priced as a flat licence fee for the whole dataset, sometimes with staged payments or refresh fees for ongoing updates.
Can I sell the same data more than once?
Yes, if you license it non-exclusively. Each licence pays less than an exclusive deal, but you can license the same data to several buyers. Exclusive licences pay more because the buyer gets data their competitors can't have.
Does anonymisation reduce the value of data?
Rarely by much. AI labs want patterns and workflows, not identities. Well-anonymised data keeps nearly all its value and is far easier to sell, because buyers can actually use it.
