🚀 Exposing a Serverless RAG API: AWS Lambda + ALB in Practice

At this point, the RAG pipeline is:

• refactored
• containerized
• stored in ECR

Now it’s time to do the most important thing:

make it accessible from the outside world.

This post shows how to expose a RAG system as a real serverless API using AWS Lambda + Application Load Balancer (ALB).

1️⃣ Lambda Running from a Docker Image

Instead of ZIP files, the Lambda uses a Docker image pulled from ECR.

Why this matters:

• full control over dependencies
• consistent runtime
• perfect for AI workloads

Once attached, Lambda simply executes the container entry point.

2️⃣ Timeouts: RAG Is Not Instant

RAG involves:

• vector search
• LLM calls
• network latency

Default Lambda timeout (3s) is often not enough.

Recommended:

• 15–30 seconds (or more, depending on your use case)

If you forget this, your function will fail even if the code is correct.

3️⃣ Environment Variables: API Keys Done Right

Never hardcode secrets.

In Lambda configuration:

OPENAI_API_KEY=sk-xxxx
        

Inside the container:

import os

api_key = os.environ["OPENAI_API_KEY"]
        

This works cleanly with:

• Docker
• Lambda
• CI/CD pipelines

4️⃣ Why Use ALB Instead of API Gateway?

Both work, but ALB + Lambda is a great fit when:

• you already use load balancers
• you want simpler HTTP routing
• you prefer fewer abstractions

ALB sends HTTP requests directly to Lambda.

5️⃣ Wiring ALB → Lambda

The flow looks like this:

1. Application Load Balancer
2. Target Group (type: Lambda)
3. Lambda Function
4. Docker-based RAG app

Once connected, HTTP requests trigger the Lambda automatically.

6️⃣ Security Groups: Don’t Forget This

The ALB must allow inbound traffic:

• typically port 80 or 443

If the Security Group blocks traffic:

• Lambda works
• Docker works
• but nothing is reachable

This is a very common mistake.

7️⃣ Testing the API with curl

Once everything is connected:

curl -X POST https://your-alb-url.amazonaws.com \
  -H "Content-Type: application/json" \
  -d '{"question": "What is RAG?"}'
        

Response:

{
  "answer": "Retrieval-Augmented Generation combines search with LLMs."
}
        

At this moment, the RAG system is live.

✅ Final Result

You now have:

• a Dockerized RAG pipeline
• running on AWS Lambda
• exposed via ALB
• secured with env vars
• accessible via HTTP

This is not a demo anymore. This is production architecture on Amazon Web Services.

🎯 Weekly Wrap-up

This week covered the full journey:

• Notebook → Script
• Script → Lambda
• Lambda → Docker
• Docker → ECR
• ECR → Public API

That’s how experimental AI becomes a real system.

💬 Have you exposed AI workloads with Lambda + ALB before? What trade-offs did you face?