Sell on MultiVendor
Sell licensed datasets
Data engineers and publishers can sell datasets from a branded shop on MultiVendor Market with a free sample, documentation and explicit license terms. Offer research, commercial and redistribution licenses or subsets as options, and get paid directly through your own Stripe or PayPal account.
Shopping instead? Browse licensed datasets on MultiVendor Market →
Tips for selling licensed datasets
- Ship a data dictionary, collection notes and a readme with every dataset.
- Provide a representative free sample in the same format as the full data.
- Confirm you own the data or have rights to resell it.
- Remove or anonymize personal data unless you have a lawful basis; check local privacy rules.
- Offer formats such as CSV and Parquet, and license tiers, as options.
Start a dataset marketplace
Launch a dataset marketplace for a domain such as agriculture, retail or language data, with your own brand and domain. Require documentation, samples and data-rights statements from sellers, and let each one connect their own payment account.
Ideas to start from
- → A consented speech and language dataset marketplace
- → A synthetic data marketplace for testing and training
- → A marketplace for cleaned open-data compilations
How selling works on MultiVendor
- Step 1
Open your shop
Create a seller account, add your logo and shop description. Free to apply.
- Step 2
List your licensed datasets
Add photos, prices and options like colour or size. Set shipping, downloads or schedules.
- Step 3
Get paid directly
Buyers pay you through your own Stripe or PayPal. MultiVendor never holds your money.
Questions about selling licensed datasets
Can I sell scraped web data?
Only if collection followed the sites' terms and applicable laws, and the data contains no personal information without a lawful basis. Many scraped datasets aren't safe to sell.
Should I document biases?
Yes. Disclosing known gaps and biases builds trust and helps buyers use the data responsibly.
How do I handle large files?
Compress and split very large datasets into logical parts, and describe each part's contents.