Cloud Platforms for AI Applications
AWS, GCP, Azure, Vercel, Supabase — each platform brings different strengths to AI infrastructure. Choosing the right combination saves you months of pain and thousands of dollars.
What you'll learn
- What each major cloud platform offers for AI workloads
- When to use hyperscalers vs. modern platforms like Vercel and Supabase
- How to build a multi-platform stack without drowning in complexity
- Real cost comparisons for common AI architectures
AWS, GCP, and Azure
The big three cloud providers offer everything — compute, storage, networking, managed AI services, GPU instances, and hundreds of other services. They're powerful but complex. You can build anything on them, but the learning curve is steep and the billing can surprise you.
AWS has the largest ecosystem. SageMaker for ML pipelines, Bedrock for managed LLM access, Lambda for serverless functions. If you need GPU instances at scale, AWS has the most availability.
GCP has the deepest AI integration — Vertex AI, TPU access, and tight integration with Google's own models. If you're building on Gemini or need custom model training, GCP is the natural home.
Azure owns the OpenAI partnership. Azure OpenAI Service gives you GPT models with enterprise compliance, data residency guarantees, and SLAs that OpenAI's direct API doesn't offer.
Vercel and Supabase
You don't need a hyperscaler for most AI applications. Modern platforms like Vercel and Supabase handle 90% of what indie developers and small teams need — with dramatically less complexity.
Vercel excels at frontend deployment and edge functions. Your AI-powered Next.js app deploys with a git push. Edge functions can handle API orchestration, streaming responses, and lightweight processing — all without managing servers.
Supabase gives you PostgreSQL with superpowers: built-in auth, realtime subscriptions, edge functions, and — critically for AI — pgvector for vector similarity search. One platform handles your relational data, your vector embeddings, your auth, and your serverless compute.
This combination (Vercel + Supabase) is what Like One runs on. It's real, it's production-grade, and it costs a fraction of a hyperscaler setup.
Choosing Your Platform
Solo developer or small team? Start with Vercel + Supabase. You'll be in production in hours, not weeks.
Need custom model training? Add GCP or AWS for GPU compute. Keep your app layer on Vercel.
Enterprise compliance requirements? Azure OpenAI + whatever your org already uses. Don't fight the existing stack.
Running open-source models? GPU instances on any hyperscaler, or specialized providers like Replicate, Modal, or RunPod for cheaper GPU access.
The smartest approach: use modern platforms for your app layer and only reach for hyperscalers when you hit a specific capability gap. Don't start complex.
Platform Comparison at a Glance
Vercel: Frontend, edge functions, streaming — $20/mo pro
Supabase: Database, vectors, auth, edge functions — $25/mo pro
AWS/GCP/Azure: Everything, including GPU — $50-$5000+/mo depending on usage
Specialized GPU (Replicate, Modal): Pay-per-second GPU — $0 idle, scales with usage
Try it yourself
Create free-tier accounts on Vercel and Supabase. Deploy a basic Next.js app to Vercel and connect it to a Supabase database. This is the foundation you'll build on for the rest of the course.Cloud Platforms — Match Each to Its Strength
Tap one on the left, then its match on the right
Start Simple, Scale Intentionally
The biggest infrastructure mistake in AI is starting too complex. You don't need Kubernetes on day one. You need a deployed app that works. Pick the simplest platform that meets your requirements, build something real, and add complexity only when the simple thing breaks.