The pricing model for AI tools is shifting towards usage-based pricing, making it easier for companies to justify ROI and shorten sales cycles.

Pricing strategies for AI agents can vary; if the service is used by humans, it may follow a per-user pricing model, while machine-to-machine services might adopt usage-based pricing.

Charging by activity and changing pricing based on workflows are effective strategies for AI companies, moving towards value-based pricing rather than cost-based pricing.

There are four pricing models for AI: activity-based, workflow-based, outcome-based, and agent-based pricing.

AI can facilitate bespoke contracts that align with customer value, moving away from rigid pricing models and allowing for more tailored agreements, which are essential for AI companies to effectively manage pricing and value delivery.

Value-based pricing is common in AI applications, where vendors charge based on the perceived savings or value they provide to customers, rather than just margins.

To succeed, AI companies need to understand their unit economics and how to price their services effectively based on the value they provide.

The mission behind Paid is to provide AI companies with the tools they need to manage pricing and understand their business fundamentals better, as traditional SaaS pricing models don't work for AI businesses, which require new approaches like outcome-based and agent-based pricing.

Experimentation is key in pricing strategy, especially for AI products. Testing different pricing models helps determine what resonates with customers and drives value.