Last month, I got our AWS bill. Over $5,000 across dozens of Lambda functions handling AI inference.
I manage the AI workloads at one of my startups, a digital asset management platform. Document classification, image analysis, content summarization, smart tagging. Each one seemed small and manageable on its own. Together, they were bleeding money and driving me insane.
The breaking point came during OpenAI's pricing restructure last year. We had built everything on GPT-4, and suddenly our costs doubled overnight. I spent three sleepless nights migrating everything to Claude, then Bedrock, then back to OpenAI when Bedrock couldn't handle our edge cases. Each migration meant rewriting integration code, testing different prompt formats, and praying nothing broke in production.
I realized we had a fundamental problem. We weren't just using AI. We were at the mercy of it.
The Pain Was Real
Here's what managing dozens of AI Lambdas actually looked like:
Vendor Lock-in Nightmare: Each Lambda was tightly coupled to its AI provider. Our summarizer used OpenAI's SDK. Our classifier used Bedrock's boto3 client. Our image analyzer used Anthropic's API directly. When pricing changed or APIs went down, we had to scramble.
Cost Blindness: Lambda billing is opaque for AI workloads. You get charged per millisecond, but AI inference can take 5-30 seconds. I couldn't tell which functions were expensive until the month-end bill arrived. By then, damage was done.
SDK Sprawl: Every provider has different patterns. OpenAI wants streaming handled one way. Bedrock wants it another. Anthropic has its own quirks. We had three different error handling patterns, three different retry logics, three different ways to handle rate limits. Maintaining this was hell.
No Streaming from Lambda: This was the killer. Our chat features needed real-time responses, but Lambda + API Gateway can't stream. Everything buffered until complete, making our UX feel sluggish compared to ChatGPT.
State Management Chaos: Multi-turn conversations were impossible. Lambda functions are stateless, so conversation context had to be stored externally and passed around. This led to complex orchestration for simple agent workflows.
I spent more time fighting infrastructure than building features. There had to be a better way.
The final straw came when I tried to implement a simple document analysis workflow. What should have been a 30-minute feature took three days of infrastructure wrestling:
// What I wanted to write
const result = await aiPlatform.analyzeDocument({
document: uploadedFile,
tasks: ['summarize', 'classify', 'extractEntities'],
});
// What I actually had to write
const summary = await summarizerLambda.invoke({
body: JSON.stringify({ document: uploadedFile })
});
const classification = await classifierLambda.invoke({
body: JSON.stringify({ document: uploadedFile })
});
const entities = await entityExtractorLambda.invoke({
body: JSON.stringify({ document: uploadedFile })
});
// Then handle errors, retries, cost tracking, and result merging
Each Lambda had different error handling patterns. Different retry logic. Different ways to track costs. I was building the same infrastructure over and over again.
What Was Already Out There
Before building my own solution, I evaluated everything available:
LangChain: The 800-pound gorilla. Everyone uses it. But it's Python-first, heavyweight, and abstracts away too much control. I need TypeScript for our frontend team, and I need to understand exactly what's happening under the hood when something breaks at 2 AM.
LiteLLM: Clever proxy approach. Great for simple use cases. But it's just a proxy. No agents, no RAG, no complex workflows. It normalizes API calls but doesn't solve the fundamental architecture problems.
Bedrock Alone: AWS's managed AI service. Solid, but limits you to Amazon's model selection. When GPT-4 Turbo launched with better reasoning, we couldn't use it. When Claude 3.5 Sonnet became available elsewhere first, we had to wait.
Custom Solutions: Most companies I talked to had rolled their own. But everyone's solution was different, and none were open source. I was looking at months of development with no proven patterns to follow.
None of these addressed my core needs: TypeScript-first, AWS-native, vendor-agnostic, production-ready, with proper streaming and state management.
The real problem wasn't any single tool. It was the architecture. Every solution assumed you wanted to build on top of their abstractions. But when you're running AI in production at scale, you need control. You need visibility. You need the ability to optimize for your specific use case.
At my startup, we process thousands of documents daily. Legal contracts, marketing materials, user-generated content. Each document type needs different handling. Legal contracts require precise entity extraction. Marketing materials need creative summarization. User content needs safety classification.
No existing platform let me optimize for these specific needs. They were all built for the general case, which meant they were suboptimal for every specific case.
The Vision
I wanted something that didn't exist: a unified AI platform that could handle everything from simple API calls to complex agent workflows, all while being vendor-agnostic and AWS-native.
Here's what I envisioned:
Gateway-First Architecture: One API endpoint to rule them all. Send the same request format whether you're using OpenAI, Claude, or Bedrock. The gateway handles provider-specific formatting, retries, and fallbacks.
TypeScript Everything: First-class TypeScript support across the stack. Strong typing for requests, responses, and configurations. No Python dependencies or runtime switching.
AWS-Native: Built for AWS from the ground up. Uses Lambda, API Gateway, DynamoDB, and S3. No Docker containers or Kubernetes complexity. Just AWS services that auto-scale.
Streaming by Default: Proper server-sent events through API Gateway. Real-time responses without buffering. Chat interfaces that feel snappy.
Agent-Ready: Built to handle multi-turn conversations, tool usage, and complex workflows. State management included, not bolted on.
RAG Integrated: Vector search and document retrieval as first-class features. No separate vector database to manage - leverage MongoDB Atlas if you're already using it.
Cost Transparent: Track costs per request, per model, per application. Know what you're spending in real-time, not at month-end.
I called it ai-platform-aws, and I built it as five separate packages that work together:
- Gateway: The unified API layer
- SDK: TypeScript client for all platforms
- RAG: Document processing and vector search
- Agents: Multi-turn conversation and tool usage
- OpenAPI: Auto-generated documentation and client SDKs
Why Open Source
I could have kept this internal. But after talking to other engineering teams, I realized everyone was solving the same problems. There's no reason every company should rebuild AI infrastructure from scratch.
The AI landscape changes fast. Models improve, new providers emerge, pricing shifts. By open sourcing this, we all benefit from shared improvements. When someone adds support for a new model, everyone gets it. When someone optimizes cost tracking, everyone saves money.
Plus, transparency builds trust. You can see exactly how your AI requests are handled. No black boxes, no vendor lock-in. If you don't like something, you can change it.
What's Coming
This series will walk you through building a production-ready AI platform step by step. We'll start with the problems (Lambda limitations), then build solutions (the gateway pattern), and eventually cover advanced topics like RAG, agents, and cost optimization.
I'll show you exactly how we reduced our AI costs by 40% while improving reliability and adding new features. More importantly, I'll show you how to avoid the architectural mistakes that cost us months of development time.
Here's what's coming:
- Why I Built My Own AI Platform (this article)
- We Started with Lambdas. Here's What Broke - The technical war stories
- The Gateway Pattern - One API, any model
- Building a RAG Pipeline That Actually Works - Beyond toy examples
- Agents That Don't Suck - Multi-turn conversations done right
- Cost Tracking That Actually Helps - Know what you're spending
- Deployment and Scaling - CDK, monitoring, and production concerns
- Lessons Learned and What's Next - Six months of production insights
Each article includes working TypeScript code examples that you can run. The complete examples live at https://github.com/tysoncung/ai-platform-aws-examples, so you can follow along or jump ahead.
The Bottom Line
If you're running AI in production, you're probably hitting the same walls we did. Vendor lock-in, cost surprises, integration complexity, and infrastructure that fights you instead of helping.
You don't have to solve this alone. The platform we built cut our AI costs by 40% while improving reliability and developer experience. More importantly, it let us focus on building features instead of fighting infrastructure.
Here's what changed after we built ai-platform-aws:
Development velocity increased 3x: Adding a new AI feature went from 2-3 days to 2-3 hours. No more provider-specific integration code.
Cost transparency: We know exactly what each feature costs in real-time. No more surprise bills.
Reliability improved: Automatic fallbacks mean our AI features stay online even when individual providers have issues.
Streaming everywhere: All our chat interfaces now feel responsive. Users love the real-time experience.
The most surprising benefit? Our AI features became more accurate. When you can easily A/B test different models and providers, you naturally optimize for quality, not just availability.
In the next article, I'll show you exactly how our Lambda approach fell apart, with real error logs and cost breakdowns. You'll see why serverless AI is harder than it looks, and why we needed a different approach.
Trust me. If you're running AI in production, you'll recognize these problems immediately. The question isn't whether you'll hit them. The question is how long you'll suffer before you fix them.
Tyson Cung is a software engineer who has spent the last year scaling AI infrastructure for a digital asset management platform. The ai-platform-aws project is open source at https://github.com/tysoncung/ai-platform-aws.
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