This growing excitement has a direct impact on pricing strategies. GenAI isn’t just another feature — it represents a paradigm shift that demands innovative pricing models capable of capturing its unique value. Traditional subscription-based approaches are being challenged as software-as-a-service (SaaS) companies grapple with new ways to align costs with outcomes and usage.
How GenAI is redefining SaaS pricing
GenAI is fundamentally reshaping the traditional relationship between pricing and usage. By enabling businesses to “do more with less,” AI challenges the relevance of seat-based pricing models while driving innovation in outcome-based, consumption-based and hybrid strategies.
Companies such as Zendesk are leading the way with hybrid approaches, charging per seat for human users and per resolved ticket for AI agents. This shift reflects a broader trend: SaaS companies are aligning their pricing strategies with the dynamic value AI delivers.
Key pricing models in the age of AI
1. Outcome-based pricing: Paying for results
Outcome-based pricing ties costs directly to measurable results, offering a transparent and ROI-aligned approach. Examples include:
- Zendesk’s AI agents: Zendesk charges customers per ticket resolved by AI, ensuring businesses pay only for successful outcomes.
- Intercom’s Fin: Intercom uses a $0.99-per-resolution model, eliminating the risks of charging for unused or underperforming features and building customer trust.
Advantages: Outcome-based pricing offers transparency by tying costs to success metrics such as resolved tickets and tasks completed. This alignment allows customers to perceive direct value from their spending.
Drawbacks: Attribution disputes can arise when outcomes involve multiple tools or manual inputs. Additionally, scalability can become challenging if success metrics are unclear or poorly calibrated.
2. Consumption-based pricing: Charging for usage
Consumption-based models align pricing with resource usage, providing flexibility for businesses of varying sizes. Examples include:
- OpenAI: OpenAI charges based on tokens processed, reflecting the granular usage of its language models. This approach scales easily, from startups to large enterprises.
- Synthesia: Synthesia implements a per-minute video-generation model with tiered access, allowing smaller creators and enterprise users to choose plans tailored to their needs.
Advantages: Consumption-based pricing is highly scalable and flexible, adapting seamlessly to customer usage patterns. It allows businesses to pay only for what they consume, reducing waste and maximizing efficiency.
Drawbacks: This model introduces revenue volatility, as usage can fluctuate unpredictably across customers and time. Customers also face the risk of unexpected cost spikes, requiring careful monitoring to avoid surprises.
3. Hybrid subscription models: Balancing predictability and flexibility
Hybrid subscription models combine the stability of subscription fees with the adaptability of consumption- or outcome-based pricing. These models are particularly effective for AI-driven tools, capturing value from both predictable base fees and variable AI-powered usage. Examples include:
- Microsoft’s Copilot: Priced at 60%-70% of the base product fee, Copilot reflects AI’s productivity enhancements while combining predictable subscription revenue with a variable usage fee that scales with the value delivered.
- Salesforce’s Einstein 1: Einstein 1 combines base subscription fees with usage-based components, balancing stable revenue streams with flexibility for customers scaling their AI adoption.
Advantages: Hybrid subscription models provide predictability through stable base fees while offering flexibility with variable charges for AI-powered features. This approach meets the needs of diverse customer segments, encouraging broader AI adoption.
Drawbacks: These models require sophisticated systems to track and manage both fixed and variable charges effectively. Customers may need education to understand the value proposition and navigate the model’s complexity.
Trends and innovations in AI pricing
The growing trend of premium pricing for Copilot-style AI add-ons (AI features assist with tasks within existing user-centric software), ranging from 30%-110% above the base per-seat cost, underscores the growing value attributed to AI-driven productivity tools. This range reflects a strategic balance: Some companies prioritize usage with lower add-on prices, while others focus on monetization with higher premiums.
Examples such as Microsoft’s Copilot and Salesforce’s Einstein 1 highlight how hybrid pricing models blend predictable subscription revenue with flexible, usage-based components (see Figure 3).
