.png)
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
In the rapidly evolving landscape of AI solutions, one question consistently challenges SaaS executives: how should we price our AI offerings to accurately reflect the value they deliver? Usage-based pricing (UBP) has emerged as a popular model, but does consumption truly correlate with the outcomes customers care about? This fundamental question deserves deeper examination as companies increasingly integrate AI capabilities into their product suites.
The Rise of Usage-Based Pricing in AI
Usage-based pricing has gained significant traction across the SaaS industry, with OpenView Partners' 2022 report showing that 45% of SaaS companies now offer some form of consumption pricing—a dramatic increase from just 34% in 2021. For AI-powered solutions, this pricing approach seems intuitively logical: customers pay based on the computing resources, API calls, or specific AI features they consume.
The appeal is clear. As CEO of Snowflake Frank Slootman noted, "Usage-based pricing creates a tight coupling between the value we deliver and what we charge." This consumption pricing model theoretically creates perfect alignment between vendor and customer interests—the more value customers extract, the more they use, and consequently, the more they pay.
When Usage-Based Pricing Works for AI
Usage-based pricing demonstrates strong outcome correlation with value in several AI scenarios:
When usage directly drives business outcomes: For predictive maintenance AI that prevents equipment failures, each prediction has quantifiable value, making per-prediction pricing logical.
When consumption scales with customer success: AI-powered customer service platforms often charge per interaction because more customer inquiries typically indicate business growth.
When resource consumption reflects complexity: Machine learning operations that require extensive computing resources for complex problems naturally align with resource-based pricing.
According to Andreessen Horowitz research, companies with strong usage alignment between pricing and value delivery show 38% higher net revenue retention compared to those with traditional subscription models.
The Misalignment Challenge
However, usage doesn't always correlate with value in AI implementations. Several common scenarios highlight where usage-based pricing creates misalignment:
1. Efficiency Penalties
AI solutions that become more efficient over time can create paradoxical incentives. As Andrew Ng, founder of DeepLearning.ai, explains: "The most advanced AI systems often require fewer resources to deliver the same or better results." Under usage-based models, vendors are essentially penalized for making their systems more efficient.
For instance, an AI text summarization tool might initially process documents inefficiently, requiring significant computing resources. As the algorithm improves, it delivers better summaries while consuming fewer resources—creating a pricing dilemma where improvements reduce revenue.
2. Quality vs. Quantity Disconnect
Not all AI interactions deliver equal value. A single, high-quality AI recommendation that leads to a million-dollar decision creates enormous value despite minimal "usage."
OpenAI's pricing model for GPT-4 illustrates this challenge. While charging per token makes administrative sense, it doesn't differentiate between a token that provides transformative insight and one that's merely conversational filler.
3. Value Realization Timing Gaps
The value of AI insights often materializes long after the computing resources are consumed, creating what pricing expert Madhavan Ramanujam calls a "temporal value gap." An AI analysis might require substantial computing resources today, but its business impact may not be realized for months.
Aligning Pricing with AI Value: Alternative Approaches
When usage doesn't correlate with outcomes, alternative pricing strategies can create better alignment:
Outcome-Based Pricing
Tying costs directly to measurable business results creates strong value alignment. AI fraud detection vendors like Feedzai have pioneered models where pricing correlates with fraud prevention metrics rather than just transaction volume.
"The fundamental question should be: what are customers actually trying to achieve with AI?" says pricing strategist Steven Forth. "The closer your pricing can get to that outcome, the stronger the alignment."
Tiered Value Metrics
Combining fixed tiers with carefully selected value metrics can balance predictability with value alignment. HubSpot's pricing evolution demonstrates this approach, where plans include usage components that scale with customer success indicators.
Hybrid Models
Some of the most sophisticated AI pricing models combine base subscriptions with usage components and outcome guarantees. This approach recognizes that AI value comes from both ongoing capabilities and specific high-value interactions.
Implementation Considerations
Before implementing usage-based pricing for AI solutions, executives should consider:
Value discovery: Conduct customer research to understand exactly how and where your AI creates measurable value.
Measurement infrastructure: Ensure you can accurately track not just usage but actual outcomes that matter to customers.
Customer education: Usage-based pricing requires transparent communication about how consumption translates to value.
Pilot programs: Test pricing models with a subset of customers to identify unintended consequences before full deployment.
Conclusion: Finding the Right Alignment
Usage-based pricing isn't inherently good or bad for AI solutions—it's a tool that must be carefully matched to the specific value delivery mechanism of your offering. The strongest pricing models recognize that value alignment sometimes means consumption pricing, sometimes outcome-based approaches, and often a thoughtful combination of both.
As AI capabilities continue to evolve and mature, the companies that succeed will be those that continuously refine their pricing to maintain tight alignment with the actual value their AI delivers—not just the resources it consumes. By focusing on this alignment, SaaS executives can build pricing models that grow revenue while genuinely reflecting the transformative potential of their AI solutions.