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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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This article expands on a discussion originally shared by robbylit on Reddit — enhanced with additional analysis and frameworks.
OpenAI recently made a counterintuitive pricing move by pushing Projects, previously a paid feature, down to its free tier. This strategic decision runs counter to the industry trend where most SaaS companies are actively removing features from free plans to drive conversions. But OpenAI's approach reveals a sophisticated retention strategy that could deliver greater long-term value than immediate monetization.
The key insight: Project's value lies not just in its functionality but in creating powerful switching costs through user investment. By analyzing this pricing shift, we can uncover valuable lessons about modern SaaS retention strategies.
Why Free Features Can Drive More Revenue Than Paywalled Ones
When OpenAI moved Projects to the free plan, they weren't just being generous – they were deploying a strategic retention mechanism. Projects encourages users to organize chats, add custom instructions, and upload context. These actions represent significant user investment that creates powerful switching costs.
Once users have organized their work into Projects, the friction of migrating to a competitor increases dramatically. Even if they never convert to paid plans, these free users are less likely to churn to competitors like Claude or other AI tools, preserving OpenAI's market dominance.
This approach follows the classic "give away the razor, sell the blades" playbook, but updated for AI tools. The Projects feature itself becomes the hook that keeps users in the ecosystem until their usage naturally grows to require paid features.
How Feature Placement Drives Monetization Through Usage Depth
Projects doesn't just create retention – it fundamentally changes how users interact with ChatGPT:
- Deeper engagement: Adding custom instructions and context requires more sophisticated usage than basic prompting
- Usage acceleration: Organized projects encourage more frequent and extensive AI interactions
- Limit encounters: As usage deepens, free users naturally hit usage limits more frequently
- Natural upgrade path: The combination of investment and limits creates organic conversion pressure
Analysis of successful freemium models shows that features encouraging deeper engagement often drive more conversions than directly paywalled functionality. By letting users build workflow dependencies in the free tier, OpenAI is playing a long-term monetization game rather than seeking immediate revenue.
The Strategic Value of Non-Paying Users in AI Platforms
OpenAI's strategy reveals an important shift in how leading companies view freemium models. Free users aren't just potential paying customers – they provide strategic value through:
- Data collection: Projects users feed more context about their workflows, preferences, and business needs into ChatGPT
- Usage patterns: Higher engagement provides OpenAI with richer data on how people actually use AI tools
- Model improvement: This contextual information potentially improves their AI models through usage patterns
- Market dominance: A larger free user base creates network effects and market leadership
Industry analysis of AI business models suggests the companies that win won't necessarily be those that monetize fastest, but those that build the most valuable data moats. OpenAI appears to be optimizing for this longer-term advantage rather than short-term revenue.
The Growing Problem of Credit-Based Pricing Models
While examining OpenAI's strategy, it's worth noting a related trend highlighted in the original discussion: the growing fatigue around credit-based pricing models.
Figma recently specified their previously vague "Limited" AI credits as 150/day with a 500/month ceiling. This approach follows what we might call the "Lovable playbook" – using daily caps to encourage regular engagement while setting monthly limits to protect margins.
However, this approach is creating customer pushback. Feedback from SaaS operators indicates customers increasingly prefer straightforward seat-based pricing over credit systems. As one pricing expert noted: "Customers are basically saying 'just tell me what I'm paying, I don't want to do math every month.'"
The irony is that many companies adopted credit systems thinking they would be more transparent than usage-based pricing, but in practice, they've created a new form of complexity. With each product implementing its own credit system with different "exchange rates," customers face the cognitive burden of managing multiple token economies.
How To Apply OpenAI's Strategy To Your SaaS Pricing
What can other SaaS companies learn from OpenAI's approach? Here are practical applications:
Identify investment features: Look for features where user setup creates natural switching costs (saved templates, configurations, uploaded content)
Evaluate placement carefully: Consider moving such features to free tiers if they create dependency without excessive costs
Create clear usage boundaries: Set limits that free users will naturally encounter as they deepen engagement
Build upgrade paths aligned with usage depth: Ensure paid features connect logically to deepening usage patterns
Consider data value: Assess whether free user behavior generates strategic value beyond direct monetization
When evaluating your pricing strategy, the key question shouldn't just be "What will people pay for?" but rather "What features create dependency that naturally leads to expanded usage?"
Feature Commoditization: The Calendly Example
Another trend worth noting is the rapid commoditization of AI features. Calendly recently brought their AI note-taking feature out of beta, entering an increasingly crowded space alongside Gong, Zoom, HubSpot, Notion, and Fireflies.
This pattern recalls earlier waves of feature commoditization, such as when in-app messaging became standard across SaaS products. The differentiation quickly shifts from "Do you have it?" to "Is yours meaningfully better?"
For SaaS companies, this suggests that timing is critical in feature monetization. Features that can command premium pricing today may need to be included in standard packages tomorrow as they become table stakes.
Conclusion: Retention-First, Not Revenue-First
OpenAI's strategy with Projects exemplifies a sophisticated approach to freemium that prioritizes retention over immediate revenue. By creating natural switching costs and encouraging deeper usage patterns, they're setting the foundation for more sustainable long-term monetization.
As competition intensifies across SaaS categories, companies should consider whether certain features might create more value in free tiers by driving retention and usage depth than they would generate as directly monetized features behind paywalls.
The most successful pricing strategies will be those that align naturally with user behavior patterns rather than artificially restricting features that users have already invested in learning and configuring.