Deploy a Production AI Platform on AWS for $100/month

From seven broken Lambda functions to a production AI platform in 8 articles.

That's the journey we've taken together. Functions that couldn't communicate, hit timeout walls, and left users staring at loading spinners. Now you get a complete platform that orchestrates complex workflows, streams real-time updates, and won't bankrupt your startup.

This isn't a toy example. The architecture I'm about to show you serves 1,500+ requests daily, has survived 8 months in production, and handles everything from document analysis to multi-step research tasks.

Time to deploy it.

The Complete Architecture

Before we dive into deployment, here's what we're building:

What We Built Together

Eight articles. One complete AI platform.

We started with seven broken Lambda functions. We built:

• 
  Agent orchestration that handles complex multi-step workflows without timeouts


• 
  TypeScript SDK with perfect IntelliSense, streaming support, and smart error handling
  


• 
  Cost control that prevents $2,847 surprises with budgets and rate limits


• 
  Production security with authentication, encryption, and monitoring


• 
  One-command deployment that gets you running in under an hour

The platform serves 1,500+ requests daily. It's survived 8 months in production. It's processing everything from document analysis to research workflows. And it's completely open source.

The Hard-Won Lessons

Building production AI infrastructure taught me things tutorials never mention:

Technical truths:

• Cost control is life support, not a nice-to-have feature
  


• Lambda excels at tools, fails at orchestration


• Streaming looks simple, implementation is brutal


• Type safety prevents expensive mistakes at 3AM

Business realities:

• Developers pay for great experience, abandon bad APIs


• Open source builds trust better than marketing


• Production numbers matter more than perfect demos


• Failure stories teach more than success posts

Personal discoveries:

• Building in public creates accountability


• Documentation is your product's face


• Shipping beats perfecting every time


• Sharing mistakes helps everyone improve

Your Turn

You have everything you need. Real code, real examples, real production lessons. The platform is MIT licensed - use it, improve it, make money with it.

Next steps:

1. 
   Star the repos -
   


2. 
   Deploy example 07 - full platform in under an hour


3. 
   Build something cool - then tell me about it


4. 
   Share your experience - help others learn from your journey

Get stuck? Email me at .

The AI revolution needs better infrastructure. You can build it.

Go.

End of series: "Building an AI Platform on AWS from Scratch". Complete platform and examples at github.com/tysoncung/ai-platform-aws.