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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Based on the information gathered, here's the best approach to pricing an AI-powered product:
Multi-tiered Pricing Structure
Implementing a multi-tiered pricing approach is essential for AI products. Based on our pricing frameworks, we recommend structuring your offering into at least three tiers (Essentials, Pro, and Enterprise) that address different customer segments and needs.
Consider Your Go-to-Market Strategy
Your pricing model should align with your go-to-market strategy. For enterprise-focused AI solutions, emphasize value-based pricing with customization options. For products targeting broader markets, focus on scalable pricing that grows with customer usage.
AI-Specific Value Metrics
Select pricing metrics that directly connect to the value your AI delivers. Options include:
- Usage-based metrics (API calls, processing time, tokens)
- Outcome-based metrics (tasks automated, insights generated)
- User-based metrics (seats, active users)
- Hybrid approaches combining multiple metrics
Feature Distribution Across Tiers
Strategic feature allocation is critical for AI product pricing:
- Core AI functionality in all tiers (Smart AI Responses, Basic Task Automation)
- Mid-tier additions like customization options and expanded reporting
- Enterprise-exclusive features such as custom model training, advanced integrations, and dedicated support
Add-on Strategy
Create a strategic add-on structure where:
- Lower tiers can access premium features à la carte
- Advanced capabilities like "Custom LLMs" are positioned as add-ons even in higher tiers
- Channel-specific features (like "Omnichannel Engagement") can be purchased individually
Usage Analysis and Pricing Power
Continuously analyze your product's usage patterns to ensure your pricing metrics align with how customers derive value. Measure your pricing power across segments to identify where you can optimize price points.
Packaging for Margin Improvement
As your AI product matures, rethink packaging to improve margins by:
- Moving high-cost features to higher tiers
- Creating add-on structures for compute-intensive functions
- Implementing anti-commoditization packaging that emphasizes unique value
Research-Based Price Setting
Utilize quantitative and empirical pricing research methods, including:
- Van Westendorp surveys to determine optimal price points
- Conjoint analysis to identify ideal package combinations
- Feature prioritization using Max Diff to understand what customers value most
By implementing these strategic pricing approaches, your AI product will be positioned to capture appropriate value while scaling effectively with your business growth.