A founder's honest comparison of AI platforms. Learn when to use Vertex AI, SageMaker, Lovable, Bolt, Replit, or Cursor—and when Hal9 is the right choice.
I've seen too many founders waste 3 months picking the "perfect" AI platform.
They research Vertex AI vs SageMaker. They debate Lovable vs Replit. They read comparison posts that sound like they were written by enterprise consultants, not people who've actually shipped an AI product from scratch.
Here's what those posts won't tell you: the "best" platform isn't the one with the most features. It's the one that matches where you are right now.
The platform question matters. But not in the way most guides frame it.
This comparison breaks down what actually works for founders moving from idea to first paying customer—not enterprises scaling to millions of users.
Before we dive into tools, let's get honest about what matters when you're validating an AI idea.
Most platforms are built for data scientists at established companies. They assume you've got:
But if you're a founder trying to validate whether anyone wants this thing, your constraints look different:
The right platform gets you to "does anyone actually want this?" faster. Everything else is stuff you optimize later.
Vertex AI and SageMaker dominate every "best AI platforms" list. And yeah, they're powerful. But here's the thing—they're built for companies with ML teams, not founders trying to validate an idea.
Great if:
But the reality:
Similar advantages:
But also:
I've seen founders spend 6 weeks just getting their dev environment set up on these platforms. That's 6 weeks they could've spent talking to customers.
When they make sense: You're already on GCP or AWS, you have an ML engineer on the team, or you're past $100K MRR and need to scale.
When they don't: You're pre-revenue, trying to validate fast, or you're a solo technical founder.
Azure ML Studio and IBM Watson follow the same pattern—powerful for organizations already in those ecosystems, but not really built for scrappy founders.
Azure ML is solid if you're in Microsoft's world (Office, Dynamics, etc.) and need compliance features. IBM Watson is all about pre-built AI services rather than custom models.
The pattern here: these optimize for enterprise procurement, not speed to validation.
If you're a three-person startup trying to figure out if anyone wants your AI feature, these aren't your constraint.
Here's where it gets interesting. Tools like Lovable and Bolt let you vibe-code an entire interface without writing code.
These are genuinely no-code—you describe what you want, and they generate a working UI. No syntax, no debugging, just prompting.
What works:
Where they fall short:
I see this constantly. A founder spends 3 months in Lovable, thinks they're almost done because the UI works, then realizes they need to rebuild everything to actually ship.
The UI is maybe 30% of a real product. The other 70% is the stuff users don't see—data pipelines, API integrations, the actual AI that makes it work.
When no-code makes sense: You're testing whether users even want the workflow. You need visuals for fundraising. You're doing customer discovery.
When it doesn't: You're ready to launch and need something that can handle real users.
Tools like Replit and Cursor are different—they're still "vibe coding," but you're actually writing code with AI assistance.
Replit is a cloud IDE where you can spin up projects instantly. Great for prototyping, collaboration, and learning. You're writing JavaScript, Python, whatever—but the environment is all handled for you.
Cursor is a VS Code fork with AI deeply integrated. You write code, but Cursor can generate entire functions, refactor components, or explain complex logic.
What's better about code-first:
The tradeoffs:
The pattern I see working: founders use Lovable or Bolt to test the concept visually, then move to Replit or Cursor to actually build the working version.
But here's the thing both miss: even if you build a working frontend and backend, you still need the AI logic that makes it actually valuable.
That's where most founders get stuck. The UI is done. The backend is wired up. But the AI model that's supposed to generate insights, recommendations, predictions—that's the hard part. And neither no-code nor code-first tools solve that for you.
This is where Hal9's positioning becomes obvious.
We're not trying to replace Vertex AI for teams running massive ML workloads. We're solving a different problem: getting technical founders from idea to first paying customer without the infrastructure headache.
Most founders don't need distributed training or enterprise MLOps. They need to prove someone will pay for their AI feature before they commit to building all that complexity.
Hal9 helps you:
The comparison isn't "Hal9 vs SageMaker" like they solve the same problem. It's more like:
"I need to prove this idea works fast" (Hal9) vs "I need to scale proven ML models for millions of users" (SageMaker/Vertex AI)
Why founders choose Hal9:
Think of it like this: Hal9 gets you to your first 100 users. Vertex AI is what you graduate to when you're scaling to your first million.
And honestly? A lot of founders never need to graduate. If your AI product works at 1,000 users on Hal9, you're making money. You can decide later if you want to replatform for marginal cost savings.
Stop asking "which platform is best?" Instead, ask these questions:
Idea validation (0-100 users): You need speed. Use no-code tools (Lovable, Bolt) to test the concept visually. Or jump straight to Hal9 if you're ready to build something users can actually use.
Early traction (100-10K users): You need something that can scale but isn't overkill. Hal9 works well here. Or consider managed platforms with transparent pricing.
Scaling up (10K+ users): Now enterprise platforms like Vertex AI or SageMaker start making sense. You have the revenue to justify the engineering resources.
Strong ML engineering: Vertex AI or SageMaker give you full control. You can optimize everything.
General software dev: Code-first tools like Replit or Cursor let you build real products. Or platforms like Hal9 that abstract infrastructure but still give you control.
Non-technical founder: You probably need a technical co-founder or fractional CTO before you pick platforms. No-code tools can help you validate, but you'll need engineering to ship.
Pre-revenue: Predictable costs matter more than features. You can't afford surprise bills. Look for transparent pricing or free tiers that actually work.
Post-revenue: Now you can optimize for performance and reliability. Enterprise platforms offer better SLAs, but you're paying for it.
"Will anyone use this workflow?" → No-code (Lovable, Bolt) to test the UX
"Will anyone pay for this feature?" → Hal9 or code-first tools to build something real
"Can this scale to 100K users?" → Enterprise platforms once you've proven demand
Here's what I've learned after helping dozens of founders pick platforms: the "best" tool isn't the one with the most features. It's the one that matches your current constraint.
Early-stage constraint: Can I validate this idea fast enough to know if it's worth building?
Later-stage constraint: Can I serve 50,000 requests per day reliably without breaking the bank?
These require different tools. Vertex AI and SageMaker are exceptional at the second problem. But using them for the first problem adds complexity before you've earned the right to that complexity.
If you're comparing platforms right now, first figure out what you're actually trying to prove.
Need to show investors a concept? No-code tools like Lovable or Bolt can get you something visual fast.
Validating whether users will pay? You need something between prototype and production. That's where Hal9 is designed to operate—stable enough for real users, flexible enough to pivot fast.
Scaling a proven product? Now you're ready for the infrastructure complexity of Vertex AI or SageMaker. You have the revenue and team to justify it.
The expensive mistake is picking the scaling-phase platform during the validation phase. You burn 3 months learning GCP instead of talking to customers.
And here's the thing nobody talks about: you can always migrate later. But you can't get those first 3 months back.
About Hal9: We help technical founders build AI products from idea to first customer in under 30 days. If you're validating an AI idea and need something between a prototype and enterprise infrastructure, let's talk about what you're building .