Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Short-term (< 3 months)
Six months ago, I had a potential client approach me with an exciting opportunity: build a two-sided marketplace platform powered by AI. 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 choosing the right platform for AI product development in 2025. While everyone rushes to build AI products from scratch or debates whether to use traditional development, I've discovered something counterintuitive: Bubble has become the secret weapon for validating AI ideas fast.
Most founders get trapped in the "perfect tech stack" mindset when building AI products. They spend months debating Python vs JavaScript, cloud architectures, and ML frameworks while their competitors ship working prototypes using no-code tools.
In this playbook, you'll learn:
Why Bubble's AI integration capabilities beat traditional development for early-stage validation
The hidden costs of custom AI development that kill most startups
My 3-step framework for choosing between Bubble and custom code for AI products
When NOT to use Bubble for AI (the limitations nobody talks about)
Real examples of successful AI products built on Bubble that raised millions
Industry wisdom
What the ""experts"" say about AI development
Walk into any startup accelerator or developer conference, and you'll hear the same advice about building AI products:
"You need a technical co-founder who can handle machine learning." The conventional wisdom says you can't build serious AI without deep technical expertise in Python, TensorFlow, and cloud infrastructure.
"Start with a custom backend for scalability." Most advisors will tell you to architect your AI product like you're planning to serve millions of users from day one.
"Use proper ML frameworks for production-ready AI." The industry pushes founders toward complex tech stacks that require dedicated DevOps teams.
"No-code platforms can't handle real AI workloads." There's this persistent myth that visual development tools are toys that can't integrate with serious AI services.
"You need months of development before you can test with users." Traditional development approaches treat user validation as something that happens after you've built the entire system.
Here's the problem with this conventional wisdom: it assumes you already know your AI product will work. Most AI startups fail not because of technical limitations, but because they built something nobody wanted. They spent 6-12 months perfecting their ML pipeline before discovering their core assumption was wrong.
This advice made sense when AI APIs were limited and no-code platforms couldn't integrate with external services. But in 2025, the landscape has completely changed. Modern no-code platforms can connect to the same AI services that power billion-dollar companies.
The real question isn't "what's the most technically sophisticated approach?" It's "what's the fastest path to validated learning?"
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The client I mentioned earlier came to me excited about the no-code revolution and new AI tools. They'd heard that platforms like Bubble could build anything quickly and cheaply. They weren't wrong — technically, you can build a complex AI-powered platform with these tools.
But their core statement revealed the fundamental problem: "We want to see if our idea works."
They had no existing audience, no validated customer base, no proof of demand. Just an idea and enthusiasm for AI. This is exactly the situation where most founders make expensive mistakes.
Here's what I told them that initially shocked them: "If you're truly testing market demand, your MVP should take one day to build — not three months."
Yes, even with AI and no-code tools, building a functional two-sided marketplace with intelligent matching takes significant time. But here's what most founders miss: your first MVP shouldn't be a product at all.
I recommended they start with a simple landing page explaining their AI-powered value proposition, manually match supply and demand via email, and only build automation after proving demand existed. The lesson? Your MVP should be your marketing and sales process, not your product.
But this experience got me thinking about when Bubble actually makes sense for AI products. Over the next few months, I started experimenting with different AI integrations on the platform. What I discovered changed how I approach early-stage AI development entirely.
The breakthrough came when I realized that Bubble excels not at replacing complex AI development, but at making AI accessible for validation and iteration. It's the perfect tool for answering the question "will people actually use this?" before you invest in custom development.
Here's my playbook
What I ended up doing and the results.
After testing Bubble with multiple AI use cases, I developed a systematic approach for determining when it's the right choice. Here's my framework:
Step 1: Validate Before You Build
Before touching any development platform, I create what I call a "fake AI backend." Using Bubble's database and workflow system, I manually process what the AI would eventually handle automatically. This lets me test user behavior patterns without any AI integration at all.
For example, when testing an AI content recommendation system, I used Bubble to create the user interface and manually curated the recommendations behind the scenes. Users thought it was powered by AI, but I was learning what they actually engaged with. This approach saved weeks of development and revealed insights that would have been impossible to discover with a black-box AI system.
Step 2: Integrate Real AI APIs
Once I validated core user behaviors, I integrated actual AI services through Bubble's API Connector. The platform can connect to OpenAI, Claude, Google AI, and virtually any AI service with a REST API. This is where Bubble's strength becomes obvious — you can prototype with production-quality AI without writing a single line of code.
I built a content generation workflow that connected Bubble to multiple AI APIs: OpenAI for text generation, DALL-E for images, and a custom sentiment analysis service. The entire integration took hours, not weeks. Traditional development would have required setting up servers, handling authentication, managing rate limits, and building error handling systems.
Step 3: Scale or Rebuild
Here's where my approach differs from typical no-code advice: I plan for migration from day one. Bubble is perfect for validation and early scaling, but it has limitations for complex AI workloads. I document every workflow, API integration, and database structure so the transition to custom development is smooth when needed.
The key insight? Most AI products never reach the scale where Bubble's limitations matter. And for those that do, you'll have validated user needs, proven business model, and clear technical requirements that make custom development much more focused and cost-effective.
This framework has worked across different AI applications: chatbots, recommendation engines, content generators, and data analysis tools. The pattern remains consistent — use Bubble to prove the concept works, then decide whether to scale within the platform or migrate to custom development.
Speed to Market
Launch AI prototypes in days, not months, giving you crucial competitive advantage in fast-moving markets.
Cost Validation
Test AI product concepts for under $100/month versus $50K+ in custom development before knowing if users want it.
User Learning
Quickly iterate on AI interactions and workflows based on real user feedback without complex code deployments.
Migration Ready
Document everything in Bubble to enable smooth transition to custom development when you've validated product-market fit.
Using this framework across different AI projects, I've seen consistent patterns emerge. The speed advantage is undeniable — what takes traditional AI development teams 3-6 months, I can prototype and test in 1-2 weeks using Bubble.
More importantly, the learning velocity is 10x faster. When your AI workflow is built visually in Bubble, you can modify the logic, test new approaches, and iterate based on user feedback in real-time. With custom development, each change requires code updates, testing, and deployment cycles.
The cost difference is dramatic. I can validate an AI product concept for the price of a Bubble subscription plus API costs — typically under $200/month. Custom AI development starts at $50,000+ before you've learned anything about user behavior.
But the most significant result has been the mindset shift. When development is fast and cheap, you focus on user problems instead of technical architecture. I've seen founders spend months debating ML frameworks when they should have been talking to customers.
Three AI products I helped validate using Bubble have successfully transitioned to custom development after proving product-market fit. One raised a Series A, another was acquired, and the third is scaling rapidly on custom infrastructure. In each case, the Bubble prototype provided the validated learning needed to make smart technical decisions.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
After working with Bubble across multiple AI projects, here are the key insights that changed how I approach AI product development:
1. Speed beats sophistication for early-stage validation
Users don't care about your tech stack — they care about whether your AI solves their problem. Bubble lets you focus on user experience instead of infrastructure.
2. Visual development reveals hidden complexity
Building AI workflows visually in Bubble forces you to think through edge cases and user flows that are easy to miss in traditional code. This leads to better-designed AI interactions.
3. API-first AI development is the future
The most successful AI companies are becoming API-first. Bubble's strength in API integration positions you to leverage the best AI services without being locked into specific frameworks.
4. Know when NOT to use Bubble
Bubble isn't suitable for real-time AI applications, complex ML pipelines, or products requiring custom AI model training. If your core innovation is in the AI algorithm itself, go custom from the start.
5. Plan your exit strategy
The best Bubble AI projects are designed with migration in mind. Document your workflows, API integrations, and user insights to enable smooth transitions to custom development.
6. User feedback is more valuable than technical perfection
Every week spent optimizing your AI algorithm is a week not spent learning about user behavior. Bubble forces you to prioritize learning over building.
7. Integration complexity is where Bubble shines
Connecting multiple AI services, handling different API responses, and managing complex workflows — this is where Bubble's visual development approach really pays off.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS AI Products:
Use Bubble for AI feature prototypes within existing products
Test AI-powered onboarding and user engagement flows
Validate AI analytics and reporting features before custom development
For your Ecommerce store
For E-commerce AI Applications:
Prototype AI recommendation engines and personalization features
Test AI-powered customer support and chatbot interactions
Validate AI content generation for product descriptions and marketing