Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, a potential client came to me with an exciting AI MVP project. Big budget, interesting technical challenge, latest no-code tools like Bubble.io. The kind of project that makes you think you've hit the freelance jackpot.
I said no.
Not because the project wasn't good - it was. Not because the budget wasn't real - it was substantial. I turned it down because they were asking the wrong question. Instead of "How much to build this AI MVP?" they should have been asking "How much to validate if this is worth building at all?"
Here's the uncomfortable truth I shared with them: If you're truly testing market demand, your MVP should cost $100, not $10,000. And definitely not tens of thousands.
After working with dozens of startups on SaaS validation and seeing the no-code revolution evolve, I've developed a completely different approach to AI MVP cost estimation. One that most agencies won't tell you because it doesn't maximize their revenue.
You'll learn: How to estimate real AI MVP costs (spoiler: it's not what you think) • Why most AI MVP budgets are 10x too high • The $100 validation framework that beats expensive prototypes • When to actually invest in building vs. validating • My contrarian take on AI development that saves months of work
Industry Reality
What every startup founder hears about AI MVP costs
Walk into any startup accelerator or browse ProductHunt, and you'll hear the same AI MVP wisdom repeated like gospel. Here's what the industry typically tells you:
"AI MVPs require substantial upfront investment." Agencies quote $15K-50K because "AI is complex." They'll show you technical architecture diagrams and talk about machine learning pipelines like you're building the next Google.
"No-code tools make it affordable." Bubble.io, Webflow, and other platforms promise you can build anything quickly and cheaply. Which is technically true - but misses the point entirely.
"You need a functional prototype to test market demand." The lean startup methodology got twisted into "build a working product first, then see if people want it." Even Eric Ries would cringe.
"AI features require real data to validate." So you end up building data pipelines, training models, and creating complex workflows before you know if anyone cares about your solution.
"Modern tools eliminate technical risk." The focus becomes building capabilities rather than proving demand exists.
This conventional wisdom exists because it's profitable for agencies and feels productive for founders. Building something tangible feels like progress. But here's where it falls short: you're optimizing for the wrong constraint.
In 2025, the constraint isn't building - it's knowing what to build and for whom. Every hour spent on development before validation is waste disguised as work.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The project that changed my perspective was a two-sided marketplace platform with AI matching capabilities. Classic startup stuff - think "Uber for X" but with intelligent algorithms.
The founders were smart, well-funded, and had done their homework. They'd researched the latest tools, understood that Bubble.io could handle complex logic, and even had preliminary user interviews suggesting demand. Their budget was real - enough to build a proper MVP with all the bells and whistles.
But during our discovery call, one phrase made me pause: "We want to see if our idea is worth pursuing."
They had no existing audience. No validated customer base. No proof people would actually use their solution beyond "yeah, that sounds useful" in interviews. Just an idea and enthusiasm.
This is where most agencies would have started drawing wireframes and discussing technical architecture. Instead, I asked an uncomfortable question: "What's the simplest way to test if people will actually pay for this matching service?"
They looked confused. "Well... we build the platform and see if people use it?"
That's when I realized they were falling into the same trap I'd seen with dozens of other startups. They were conflating building with validating. The AI features, the sophisticated matching algorithms, the beautiful interface - all of it was secondary to one fundamental question: Will people pay for this solution?
I've seen this pattern repeatedly in SaaS projects. Founders get excited about the technology and forget that technology without demand equals expensive hobbies.
Here's my playbook
What I ended up doing and the results.
Instead of accepting the project, I gave them what became my standard "AI MVP cost reality check." Here's the framework I've developed after watching too many startups burn money on beautiful solutions to problems nobody wanted to pay for:
Phase 0: The $100 Validation (Week 1)
Before writing a single line of code, create a simple landing page explaining your value proposition. Not the technical features - the outcome people get. Use Carrd, a simple Webflow template, or even a Notion page. Cost: $0-100.
Start manual outreach immediately. Find potential users on both sides of your marketplace. Don't ask if they like your idea - ask if they'll pay a deposit to be first in line when you launch. Real money, not "yeah I'd probably use that."
Phase 1: Manual Validation (Weeks 2-4)
Here's the part that makes founders uncomfortable: manually do what your AI would eventually automate. For the marketplace project, this meant personally matching supply and demand via email and WhatsApp.
Sounds primitive? Good. If you can't make the matching process work manually, no algorithm will save you. If people won't pay for the manual version, they won't pay for the automated one either.
I've applied this approach across different growth experiments. The companies that succeed are those willing to do unscalable things first.
Phase 2: Proof-of-Concept Build (Month 2-3)
Only after proving manual demand do you start building automation. But here's where my approach differs from typical MVP development:
Start with workflows, not interfaces. Use tools like Zapier, Airtable, and simple forms to automate your manual process. This typically costs $500-2000 in tools and setup time. No custom development yet.
Phase 3: Technical MVP (Month 4-6)
Finally, when you've proven people will pay and you understand the core workflows, consider platforms like Bubble.io for custom development. But now your budget estimate is based on validated demand, not speculation.
Real cost at this stage: $5K-15K for a validated concept vs. $20K-50K for unvalidated speculation.
Validation First
Never start with technology. Start with demand validation using manual processes before investing in any development.
Manual Matching
Test your core value proposition by manually doing what the AI would automate. If it doesn't work manually it won't work automatically.
Progressive Investment
Invest in complexity only after proving simpler versions work. Each phase gates the next level of investment.
Reality Check
Most ""AI MVP"" features are nice-to-haves disguised as necessities. Focus on core value creation first.
The marketplace founders took my advice and started with manual matching via a simple Google Form and email sequences. Within 3 weeks, they had 12 paying customers on the supply side and 30+ validated demand matches.
Their "AI MVP cost" went from $35,000 to $200 - the cost of setting up simple automation workflows. More importantly, they learned their initial matching criteria were completely wrong. The manual process revealed insights no amount of planning could have predicted.
Six months later, they raised funding based on proven demand and clear unit economics. Their actual platform development became an execution challenge, not a validation experiment.
The time savings were even more valuable than the cost savings. Instead of spending 3-4 months building and then discovering fundamental flaws, they spent 3 weeks learning and iterating.
This approach has worked across different projects I've consulted on - from AI automation tools to complex ecommerce solutions. The pattern is consistent: manual validation always reveals assumptions that would have been expensive mistakes.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
1. Technology is cheap, validation is expensive. In 2025, building capability is the easy part. Proving people want that capability is the hard part. Budget accordingly.
2. AI features are amplifiers, not solutions. If your manual process doesn't create value, adding AI won't magically fix it. It'll just automate something useless.
3. No-code tools enable lazy thinking. Because you can build complex features quickly, founders skip the hard work of figuring out what features actually matter.
4. Cost estimation should be staged. Don't estimate the final product cost - estimate the validation cost, then the proof-of-concept cost, then the MVP cost. Each stage should be 10x smaller than the next.
5. Manual-first reveals hidden complexity. Every "simple AI matching algorithm" becomes incredibly complex when you try to do it manually. Better to discover this before building.
6. Budget for learning, not building. The most expensive MVPs are those built on wrong assumptions. Cheap validation prevents expensive pivots.
7. Demand validation beats technical validation. Proving people will pay is infinitely more valuable than proving your technology works.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups:
Start with manual customer success instead of automated onboarding
Use email sequences before building in-app flows
Test pricing with simple payment forms before complex billing systems
For your Ecommerce store
For Ecommerce stores:
Test product-market fit with simple landing pages before complex catalogs
Use manual customer service before AI chatbots
Validate demand with pre-orders before inventory investment