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Building a Serverless AI Application with AWS Lambda and Amazon Bedrock

Learn how to build a simple AI-powered application using AWS serverless services. This article explains how API Gateway, AWS Lambda, and Amazon Bedrock work together to process user requests and generate AI-powered responses without managing servers.

Priyanshu Mishra

Student at shri Shankaracharya Institute of Professional Management and Technology

Building a Serverless AI Application with AWS Lambda and Amazon Bedrock

Artificial Intelligence applications are becoming an important part of modern software development. However, building and deploying an AI application traditionally requires managing servers, APIs, infrastructure, and scalability.
AWS provides several services that make this process much easier. In this article, we will explore how to build a simple serverless AI application using Amazon API Gateway, AWS Lambda, and Amazon Bedrock.

What Are We Building?

Our application will follow this simple flow:
User → API Gateway → AWS Lambda → Amazon Bedrock → AI Response
The user sends a request to an API. API Gateway receives the request and invokes a Lambda function. Lambda processes the request and sends it to Amazon Bedrock, which generates an AI response. The response is then returned to the user.

AWS Services Used

1. Amazon API Gateway

API Gateway provides an endpoint through which users or applications can communicate with our backend.
For example:
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POST /ask-ai
The request could contain:
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{
"prompt": "Explain cloud computing in simple words."
}
API Gateway forwards this request to our Lambda function.

2. AWS Lambda

AWS Lambda allows us to run backend code without managing servers.
Our Lambda function can:
  • Receive the API request
  • Extract the user's prompt
  • Send the prompt to Amazon Bedrock
  • Process the AI response
  • Return the result to the client
This makes Lambda a great choice for lightweight, event-driven AI applications.

3. Amazon Bedrock

Amazon Bedrock provides access to foundation models through AWS APIs.
Instead of managing the underlying AI infrastructure ourselves, we can send a prompt to a supported model and receive the generated response.
For example:
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Prompt:
"Explain AWS Lambda to a beginner."

Response:
"AWS Lambda is a serverless service that lets you run code
without managing servers."

How the Architecture Works

The complete request flow looks like this:
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User
|
v
 Amazon API Gateway
|
v
 AWS Lambda
|
v
 Amazon Bedrock
|
v
 AI Response
|
v
 API Gateway
|
v
 User
This architecture is useful because each component has a specific responsibility.

Step 1: Create an API

First, we create an API using Amazon API Gateway.
The API provides an endpoint that our frontend or other applications can call.
For example:
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POST /ask-ai
The client sends the user's prompt as part of the request body.

Step 2: Create the Lambda Function

Next, we create an AWS Lambda function.
The function receives the request from API Gateway and extracts the prompt.
Conceptually, the Lambda function performs the following steps:
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Receive Request
↓
Extract Prompt
↓
Call Amazon Bedrock
↓
Receive AI Response
↓
Return Response
Because Lambda is serverless, we don't need to provision or maintain a traditional backend server.

Step 3: Connect Lambda with Amazon Bedrock

The Lambda function uses the AWS SDK to communicate with Amazon Bedrock.
The prompt is sent to the selected foundation model, and the generated response is returned to Lambda.
Lambda can then format the response and send it back through API Gateway.

Step 4: Add IAM Permissions

Security is an important part of any AWS application.
The Lambda function should have an IAM role that provides only the permissions it actually needs.
For example, instead of giving Lambda broad AWS permissions, we should follow the principle of least privilege.
This helps reduce the risk of accidental or unauthorized access.

Why Use a Serverless Architecture?

There are several advantages to this approach.

No Server Management

We don't have to manage virtual machines or traditional servers.

Automatic Scaling

AWS can scale serverless components according to incoming requests.

Pay for Usage

With serverless services, costs are generally based on usage rather than maintaining an always-running server.

Easy Integration

API Gateway, Lambda, and Bedrock can work together to create an AI backend with relatively little infrastructure management.

Real-World Applications

This architecture can be used to build many different applications, such as:
  • AI chatbots
  • AI study assistants
  • Resume analyzers
  • Customer support assistants
  • Document question-answering systems
  • AI content generation tools
  • Developer assistants
For example, a student could build an AI Study Assistant where the frontend sends a question to API Gateway, Lambda processes it, and Amazon Bedrock generates an explanation.

Security Considerations

When building an AI application on AWS, security should be considered from the beginning.
Some important practices include:
  • Use IAM roles instead of hard-coded credentials.
  • Follow the principle of least privilege.
  • Validate API requests.
  • Avoid exposing sensitive information.
  • Monitor application activity.
  • Protect APIs from unauthorized access.
  • Store secrets securely instead of putting them directly in source code.

Cost Considerations

One of the biggest advantages of serverless architecture is that we don't need to maintain an always-running server.
However, the total cost depends on factors such as:
  • Number of API requests
  • Lambda execution time
  • Amazon Bedrock model usage
  • Data transfer
  • Other AWS services used by the application
Therefore, monitoring usage and choosing appropriate configurations is important when building production applications.

What Can We Improve?

This basic architecture can be extended further.
For example, we could add:
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Frontend
↓
API Gateway
↓
Lambda
↓
Bedrock
↓
DynamoDB
DynamoDB could be used to store conversation history or application data.
We could also introduce authentication, monitoring, logging, caching, and additional AWS services as the application grows.
# serverless# aws-builders# api# agentic-ai
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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