Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Last year, a potential client approached me with an exciting opportunity: build a two-sided marketplace platform with AI features. The budget was substantial, the technical challenge was interesting, and it would have been one of my biggest projects to date.
I said no.
Here's why — and what this taught me about the real cost of building AI MVPs in 2025. While everyone's talking about no-code platforms like Bubble making AI development "accessible," nobody's being honest about what it actually costs to build something meaningful.
The client came to me excited about the no-code revolution and new AI tools. They'd heard these tools could build anything quickly and cheaply. They weren't wrong — technically, you can build a complex platform with these tools. But their core statement revealed the problem: "We want to see if our idea is worth pursuing."
In this playbook, you'll discover:
The hidden costs most founders miss when budgeting AI MVPs
Why "cheap" no-code solutions often become expensive mistakes
A real budget breakdown for different types of AI MVPs
When to build vs. when to validate first
My framework for right-sizing your AI MVP investment
Whether you're considering building on Bubble or exploring other AI development approaches, this breakdown will save you from expensive mistakes I've seen too many founders make.
Industry Reality
What every startup founder has been told about AI MVP costs
Walk into any startup accelerator or browse through tech Twitter, and you'll hear the same gospel: "AI MVPs are now cheaper than ever!" The narrative is compelling and sounds something like this:
The Standard Pitch:
No-code platforms like Bubble eliminate development costs
AI APIs make complex features "plug and play"
You can build and test ideas for under $5,000
Speed to market beats feature completeness
Launch first, optimize later
This advice exists for good reasons. The barrier to entry for AI development has genuinely dropped. Tools like Bubble, combined with APIs from OpenAI, Anthropic, and others, do make it possible to prototype quickly. The no-code movement has democratized access to technology that previously required teams of developers.
Venture capitalists love this narrative because it means more bets with smaller initial investments. Startup gurus promote it because it sounds actionable and optimistic. Tool vendors push it because, well, they're selling tools.
Where This Falls Short:
The problem isn't that this advice is wrong — it's that it's incomplete. Yes, you can build something quickly and cheaply. But "building something" and "building something that solves a real problem for real users" are entirely different challenges with completely different cost structures.
Most cost discussions focus on the initial build while ignoring validation, iteration, maintenance, and scaling. They treat MVPs like one-time expenses rather than the beginning of an ongoing investment cycle.
This creates a dangerous gap between expectation and reality that I've watched drain founder bank accounts and enthusiasm.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client who approached me had fallen into this exact trap. They had:
No existing audience
No validated customer base
No proof of demand
Just an idea and enthusiasm
When I dug deeper into their "market research," it consisted mainly of talking to friends and assumptions about what users "should" want. They'd seen other marketplace platforms succeed and figured AI would be their differentiator.
The Budget Reality Check:
They came in expecting to spend around $15,000-20,000 for a "complete MVP." But when I broke down what "complete" actually meant for a two-sided marketplace with AI features, the real numbers looked very different:
Technical development was just the tip of the iceberg. A marketplace needs both supply and demand to function. AI features need training data, ongoing API costs, and constant refinement. User acquisition requires marketing spend. Customer support becomes essential once real users start experiencing inevitable issues.
I realized I'd been in similar situations before as a freelance consultant. Clients would come with exciting AI ideas and realistic budgets for building, but completely unrealistic expectations about what "success" would cost beyond the initial build.
The conversation shifted when I asked them a simple question: "If you're truly testing market demand, shouldn't your MVP take one day to build — not three months?"
That's when I realized most founders are building MVPs to feel productive rather than to actually validate demand. They want to build because building feels like progress, even when it's the most expensive way to test an assumption.
Here's my playbook
What I ended up doing and the results.
The Hard Truth I Shared With Them:
If you're testing whether people want your solution, your first MVP shouldn't be a product at all. Here's the framework I've developed after working with multiple AI-focused startups:
Phase 1: Validation MVP (Budget: $500-2,000)
Day 1: Create a simple landing page or Notion doc explaining the value proposition
Week 1: Start manual outreach to potential users on both sides of the marketplace
Week 2-4: Manually match supply and demand via email/WhatsApp
Month 2: Only after proving demand, consider building automation
This approach costs almost nothing but teaches you everything about whether demand actually exists. I've seen founders save $50,000+ by discovering their idea needed pivoting before they built anything.
Phase 2: Technical MVP (Budget: $5,000-25,000)
Once you've validated demand manually, here's where Bubble becomes genuinely useful:
Basic Bubble AI MVP (Week 1-2):
Bubble subscription: $25-100/month
OpenAI API integration: $100-500/month
Basic design and workflow setup: $2,000-5,000
Advanced Features (Month 2-3):
Custom AI training/fine-tuning: $1,000-5,000
Payment processing integration: $500-1,000
User authentication and security: $1,000-2,000
Mobile responsiveness optimization: $1,000-3,000
Phase 3: Scaling Costs (Ongoing)
This is where most cost estimates completely fall apart:
AI API costs scale with usage (can reach $1,000+/month quickly)
Customer acquisition often costs $20-100 per user
Customer support becomes essential ($2,000-5,000/month)
Platform limitations require custom development ($10,000-50,000)
My framework prioritizes learning over building. Distribution and validation come before development, not after.
Quick Validation
Test demand manually before building anything. A Google Form can validate ideas faster than months of development.
Hidden Costs
AI API costs, customer acquisition, and ongoing support often exceed initial development budgets by 300-500%.
Scaling Reality
Bubble works for prototypes but expect $20K-100K+ in custom development when you need advanced features or scale.
Timing Wisdom
Build your MVP only after you've manually validated demand. Your first MVP should be your marketing process, not your product.
The outcome validated my approach completely. Rather than spending months building a complex platform, we implemented the validation framework:
Week 1-2 Results:
Created a landing page explaining the marketplace concept
Manually reached out to 50 potential supply-side users
Discovered 80% had no interest in the proposed value prop
Month 1 Pivot:
Based on actual user conversations, we discovered the real problem was completely different from what they'd assumed. Instead of building a marketplace, users needed a simple workflow automation tool.
This pivot would have been impossible to discover through building a complex AI marketplace. It required direct conversation with real users facing real problems.
Final Implementation:
We ended up building a much simpler Bubble-based tool focused on the actual user need. Total development cost: $3,500. Time to first paying customer: 6 weeks. Time saved by not building the original idea: 4 months and $35,000+.
The lesson reinforced a principle I now share with every client: In the age of AI and no-code, the constraint isn't building — it's knowing what to build and for whom.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
This experience reinforced several critical lessons about AI MVP development:
1. Budget for Learning, Not Building
Most founders allocate 90% of their budget to development and 10% to validation. It should be reversed. Spend heavily on understanding your market before you spend anything on code.
2. AI Amplifies Bad Ideas Faster
AI tools make it easier to build the wrong thing quickly. Without proper validation, you'll just create impressive solutions to problems nobody has.
3. No-Code Has Hidden Scaling Costs
Bubble is fantastic for prototyping but expect significant platform limitations as you grow. Budget for eventual custom development.
4. API Costs Are Variable and Unpredictable
AI API pricing can fluctuate dramatically based on usage patterns. Budget for 3-5x your initial estimates.
5. Distribution Costs More Than Development
Getting users to try your AI MVP often costs 10x more than building it. Most founders drastically underestimate acquisition costs.
6. Maintenance Never Ends
AI models need constant tuning, APIs change, and user expectations evolve. Factor ongoing costs into your total investment.
7. Manual Processes Scale Better Than You Think
You can manually serve 50-100 users while learning what automation actually needs to do. Don't automate until you understand the process perfectly.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Start with manual onboarding and support to understand user needs
Use AI to enhance existing workflows, not replace human insight
Budget $10K-30K for serious validation, not just building
Plan for 6-12 months of iteration before finding product-market fit
For your Ecommerce store
For E-commerce businesses:
Focus AI features on recommendation engines and customer support
Start with existing platforms before building custom solutions
Validate demand through landing pages and pre-orders
Budget heavily for customer acquisition and retention