Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
So here's what I learned about pricing Bubble AI MVPs after watching too many founders make the same mistakes I almost did. You know that feeling when you've built something cool with AI and Bubble, and suddenly you're staring at a blank pricing page wondering "what the hell do I charge for this?"
The conventional wisdom says start free, add value, then charge. But here's the thing - AI MVPs aren't like regular SaaS products. They're weird beasts that consume API costs, need constant training, and often solve problems people didn't know they had.
I've been in this situation multiple times - both as someone building AI prototypes and as a consultant helping founders figure out their pricing. The gap between what pricing gurus tell you and what actually works for AI MVPs is massive.
Here's what you'll learn from my experience:
Why traditional SaaS pricing models break with AI MVPs
The hidden costs that kill most AI MVP pricing strategies
A framework for pricing that actually accounts for AI uncertainty
Real examples from AI MVPs I've worked with
When to use usage-based vs subscription pricing for AI features
The reality is that most AI MVP pricing is either way too optimistic about costs or way too conservative about value. Let me show you what I learned from getting this wrong before getting it right.
Industry Reality
What every AI founder gets told about pricing
The typical advice for AI MVP pricing sounds logical on paper. Start with a freemium model, let users experience the value, then convert them to paid plans. Most accelerators and business guides will tell you to:
Follow SaaS pricing best practices - Tiered pricing, annual discounts, feature gating
Price based on value delivered - Calculate ROI for customers and charge a percentage
Start low and expand - Hook users with low entry points, expand over time
Use competitor pricing as anchors - Research what similar tools charge
Test price sensitivity - A/B test different price points to find the sweet spot
This advice exists because it works for traditional SaaS. You build software once, serve it to many users, and your costs are predictable. Marketing costs, hosting, support - all relatively stable and scalable.
The problem? AI MVPs don't follow SaaS economics.
Every user interaction costs you money in API calls. Model performance varies wildly. Some users will hammer your AI endpoints while others barely touch them. Your biggest power users might actually be your least profitable customers.
But here's where it gets really tricky - the value AI delivers is often hard to measure immediately. Unlike a CRM where you can track deals closed or a marketing tool where you can measure conversions, AI often provides efficiency gains or insights that compound over time.
Most pricing frameworks assume you know your unit economics. With AI MVPs, especially early on, your unit economics are all over the place. One user might cost you $50/month in API calls while another costs $5, but both might derive similar value.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
I learned this lesson the hard way when I was consulting with a startup building an AI-powered customer support tool on Bubble. They came to me after three months of bleeding money with a "freemium" model that was bankrupting them.
The founders had built this clever AI chatbot that could handle complex customer queries. Really impressive stuff - it could understand context, access knowledge bases, even handle complaints with empathy. They launched with a free tier allowing 100 AI interactions per month, then $29/month for unlimited.
Sounds reasonable, right? Wrong.
Their power users were companies with high-volume support needs. These users would hit 3,000+ AI interactions monthly. At their OpenAI API costs of roughly $0.02 per interaction, they were spending $60+ to serve customers paying $29. They were losing money on every customer who actually used their product.
When I dug into their analytics, the picture got worse. Their free tier users averaged 150 interactions monthly - already 50% over their "limit" but they weren't enforcing it properly. They thought being generous would drive conversions. Instead, they were training users to expect unlimited AI for free.
The typical response would be "just raise prices." But their customers were small businesses who balked at anything over $50/month. They were stuck between API costs that demanded higher pricing and a market that couldn't afford those prices.
This wasn't just bad pricing - it was a fundamental misunderstanding of AI economics. They were treating API calls like database queries when they should have been treating them like consulting hours.
That's when I realized traditional SaaS pricing advice doesn't just fail for AI MVPs - it can actively destroy them.
Here's my playbook
What I ended up doing and the results.
After seeing this pattern repeat with multiple AI startups, I developed what I call the "AI-First Pricing Framework." It's designed specifically for the weird economics of AI MVPs built on platforms like Bubble.
Step 1: Map Your Real Unit Economics
Forget vanity metrics. Track these three numbers religiously:
Cost Per AI Action (CPAA) - Include API costs, processing time, and overhead
Actions Per User Per Month (APUPM) - How much do different user segments actually use your AI?
Value Per Action (VPA) - What's each AI interaction worth to the user?
For the customer support startup, CPAA was $0.025, but APUPM ranged from 50 (light users) to 3,000+ (power users). Massive variance.
Step 2: Segment by Usage Intensity, Not Company Size
Traditional SaaS segments by team size or revenue. AI MVPs need to segment by usage patterns. I created three tiers:
Explorers (0-200 actions/month) - Want to test the waters
Adopters (200-1,000 actions/month) - Integrating into workflows
Power Users (1,000+ actions/month) - Mission-critical dependency
Step 3: Hybrid Pricing Model
Pure subscription doesn't work. Pure usage-based scares users. The solution? A hybrid approach:
Base subscription fee - Covers platform access, basic features, and includes a reasonable action allowance
Usage-based overage - Clear pricing for actions beyond the included amount
Usage caps with upgrade prompts - Prevents runaway costs while encouraging plan upgrades
For the support tool, we restructured to:
Starter: $39/month + 300 actions included + $0.10 per additional action
Growth: $99/month + 1,200 actions included + $0.08 per additional action
Scale: $249/month + 3,500 actions included + $0.06 per additional action
Step 4: Build in AI Uncertainty Buffers
AI costs fluctuate. Models improve but get more expensive. New providers emerge. Build 40-50% margins into your AI-related costs, not the typical 20% SaaS margin.
Step 5: Transparent Usage Analytics
Users need to understand their usage patterns. We built a simple dashboard showing monthly action usage, trends, and projected costs. Transparency builds trust and helps users self-select into appropriate plans.
Cost Awareness
Track your real unit economics including API costs, processing overhead, and usage patterns by user segment
Value-First Tiers
Structure pricing around usage intensity rather than traditional company size or feature access models
Hybrid Pricing
Combine base subscription fees with transparent usage-based overages to balance predictability with fair cost allocation
Buffer Planning
Build 40-50% margins into AI-related costs to account for model changes and API price fluctuations
The results were immediate and dramatic. Within 60 days of implementing the new pricing structure:
Financial Health Improved: Monthly recurring revenue increased 85% while gross margins went from negative 23% to positive 31%. The hybrid model meant power users paid for their actual usage while lighter users got predictable costs.
Customer Behavior Shifted: Users became more thoughtful about AI usage. Instead of throwing random queries at the system, they focused on high-value interactions. Paradoxically, user satisfaction increased even though costs were more transparent.
Unexpected Insights: The usage analytics revealed that many "power users" were actually using the AI inefficiently. We built optimization suggestions that helped customers get better results with fewer actions - improving their outcomes while reducing our costs.
Most importantly, the pricing structure attracted the right customers. Companies that saw real value from AI interactions were willing to pay for them. Price-sensitive users who weren't getting proportional value naturally filtered out.
Six months later, they had achieved their first profitable month and were able to raise their Series A based on proven unit economics rather than growth-at-all-costs metrics.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from restructuring AI MVP pricing multiple times:
AI usage follows power law distributions. Most users will use little, but your heaviest users will use exponentially more. Plan for this variance.
Free tiers with AI are dangerous. Unlike static features, every AI interaction costs real money. Be extremely conservative with free allowances.
Transparency reduces friction. Users who understand their usage patterns make better purchasing decisions and complain less about bills.
Usage caps are your friend. They prevent bill shock while creating natural upgrade moments.
Monitor unit economics constantly. AI costs change frequently. What's profitable today might not be next month.
Segment by behavior, not demographics. A small company using AI heavily is more valuable than a large company using it lightly.
Build pricing into the product experience. Usage dashboards and cost projections should be core features, not afterthoughts.
The biggest mistake I see founders make is treating AI like traditional software. It's not. Every interaction has a marginal cost, performance varies, and value delivery can be inconsistent. Your pricing needs to account for this reality.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups building AI MVPs:
Start with usage-based pricing from day one - don't retrofit it later
Build detailed usage analytics into your core product dashboard
Set conservative free tier limits that reflect real AI costs
Create upgrade paths based on usage patterns, not just feature access
For your Ecommerce store
For ecommerce businesses implementing AI features:
Consider AI as a premium add-on service rather than a core feature
Price AI recommendations or chat features separately from your main platform
Focus on ROI metrics like conversion lift to justify AI premium pricing
Use AI strategically for high-value interactions rather than every customer touchpoint