Serverless AI: Computing without Servers

Not every AI application needs a dedicated GPU cluster or a complex Kubernetes setup. For many tasks—like summarizing a single email or classifying a support ticket—you can use Serverless Functions.

Serverless allows you to run code only when it's needed. You don't pay for idle time, and you don't manage any "Boxes."

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1. What is Serverless AI?

Serverless AI comes in two flavors:

A. The Serverless App (AWS Lambda / Cloudflare Workers)

Your Python logic (the "Agent Shell") runs in a serverless function. It calls an external API (like AWS Bedrock) to do the heavy lifting.

• Cost: Extremely low.
• Scaling: Infinite.

B. The Serverless Model (Beam / Modal / Replicate)

The model itself runs in a serverless container with a GPU that "Wakes Up" when a request arrives and "Sleeps" when it's done.

• Cost: Medium.
• Scaling: Excellent, but handles "Cold Starts" (a delay when waking up the GPU).

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2. Dealing with the "Cold Start"

A serverless function is "Frozen" when not in use. When a user clicks "Summarize," the cloud provider has to unfreeze the code and the model weights.

• Lambda (CPU): 0.1 - 1.0 second delay.
• Serverless GPU: 10 - 60 seconds delay!

LLM Engineer Strategy: Use Warmed Instances for mission-critical tasks. This keeps one "Small" version of the function alive at all times to avoid the wake-up delay.

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3. Use Cases for Serverless AI

Serverless is NOT for real-time chatbots (the delay is too high). It is PERFECT for:

• Background Jobs: Processing 500 PDFs at 3 AM.
• Data Pipelines: Every time a user uploads a file, a serverless function triggers to embed and store it in a Vector DB.
• Scheduled Tasks: Generating a weekly summary of Slack messages.

graph LR
    A[S3: New PDF Upload] --> B[AWS EventBridge]
    B --> C[AWS Lambda: Python]
    C --> D[AWS Bedrock: Summarize]
    D --> E[DynamoDB: Save Result]
    E --> F[SNS: Notify User via Email]

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4. Environment Limitations

Serverless functions have strict limits:

• Time: Most Lambda functions must finish in 15 minutes.
• Memory: Max 10GB of RAM. You cannot run a large LLM inside a standard Lambda function; you must call an external API.

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Code Concept: AWS Lambda AI Handler

import json
import boto3

def lambda_handler(event, context):
    # 1. Get user input from the trigger
    user_text = event.get('text', '')
    
    # 2. Call the AI Model (Bedrock)
    bedrock = boto3.client("bedrock-runtime")
    # ... Bedrock logic here ...
    
    return {
        'statusCode': 200,
        'body': json.dumps({'summary': 'This is the AI summary'})
    }

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Summary

• Serverless is the most cost-effective way to build background AI tasks.
• Lambda handles the logic; Bedrock handles the model.
• Cold Starts are the main trade-off for the low cost.
• Use serverless for Asynchronous tasks where the user isn't watching a spinning wheel.

In the final lesson of Module 11, we will look at Global Scaling, learning how to route these serverless and containerized functions to users around the world.

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Exercise: The Cost Optimizer

You have 10,000 users who each summarize 1 document per month.

• Option A: Dedicated GPU Server ($500/month).
• Option B: AWS Lambda + Bedrock API tokens ($0.05 per summary).

1. Calculate the cost of Option B.
2. Which option would you choose for this specific workload?

Answer Logic:

1. $500. ($10,000 \times 0.05$).
2. Option B. Even though the price is the same in this specific example, Option B requires zero maintenance. If the number of users drops to 1,000 next month, your cost for Option B drops to $50, while the GPU server still costs $500!