The main options include:

Trading fees

Charge a small fee when users buy or sell event contracts. This is often the strongest core revenue source because higher trading volume directly increases platform revenue. The fee can be structured differently for makers and takers to encourage liquidity.

Market creation fees

Businesses, organizations, or professional users can pay to create or sponsor specific markets. This works particularly well for niche events and specialized forecasting markets.

Withdrawal or transaction fees

Small fees can be applied to withdrawals or certain payment transactions, although they should remain competitive so they do not discourage users.

Premium subscriptions

Advanced users can pay for features such as detailed market analytics, historical data, advanced trading tools, alerts, or portfolio insights.

API and data monetization

A platform with valuable prediction data can offer API access or sell analytics to businesses, researchers, media companies, or financial organizations.

For most new platforms, I would recommend starting with low trading fees as the primary revenue engine, then adding premium features, API access, and other monetization channels as the user base grows. The goal is to monetize activity without making the platform expensive enough to reduce liquidity and user participation.

The revenue model also needs to be designed alongside the platform's liquidity strategy, market structure, payment system, and regulatory requirements. Current prediction-market platforms use different fee structures, so there is no universal fee percentage that works for every business.

If you're planning the business model and technical architecture together, this breakdown of prediction market revenue models is useful because it covers the different ways a platform can generate revenue and how they can fit into the overall business model.