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Building an AI-Powered Spring Boot Application: From Local Development to AWS

Building an AI-Powered Spring Boot Application: From Local Development to AWS

Artificial Intelligence applications are becoming increasingly common in modern software development. However, building an AI-powered application is not only about calling an AI API. Developers also need to think about backend architecture, API design, security, scalability, and deployment. In this article, we will look at how a simple AI-powered email reply generator can be built using Spring Boot and then prepared for deployment on AWS.

1. What Are We Building?

Our application takes an email as input and generates a professional reply using an AI model.
The basic architecture looks like this:
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User
|
v
React Frontend
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| HTTP POST
v
Spring Boot REST API
|
v
AI Model API
|
v
Generated Email Reply
|
v
User
The frontend is responsible for collecting the email content and displaying the generated response.
The Spring Boot backend handles the API request and communicates with the AI service.

2. Why Use Spring Boot?

Spring Boot makes it easy to build REST APIs using Java.
Instead of manually configuring a web server and many dependencies, Spring Boot provides a production-ready application structure.
A simple controller can look like this:
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@RestController
@RequestMapping("/api/email")
public class EmailGeneratorController {

@PostMapping("/generate")
public String generateEmail(@RequestBody EmailRequest request) {

return emailGeneratorService.generateReply(
request.getEmailContent(),
request.getTone()
);
}
}
The client can send a request such as:
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{
"emailContent": "Can we schedule a meeting tomorrow?",
"tone": "professional"
}
The backend then sends the request to the AI service.

3. Connecting the Backend to an AI Model

The Spring Boot service can use an HTTP client such as WebClient to communicate with an AI API.
The important idea is that the API key should never be hardcoded in the source code.
Instead of doing this:
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String apiKey = "MY_SECRET_KEY";
we should use environment variables or a secrets-management solution.
For example:
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gemini.api.url=${GEMINI_URL}
gemini.api.key=${GEMINI_KEY}
This allows the same application to run in different environments without changing the source code.

4. Why Environment Variables Matter

Suppose we have:
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Local Development
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v
GEMINI_KEY=development-key
and later deploy the application:
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AWS
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+-- Production environment
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+-- GEMINI_KEY=production-key
The application code remains the same.
This is an important principle:
Configuration should be separated from application code.
It also reduces the risk of accidentally committing credentials to GitHub.

5. Preparing the Application for AWS

Once the application works locally, the next step is deployment.
A typical AWS architecture could look like this:
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AWS Cloud
|
+------------+------------+
| |
v v
 Frontend Hosting Spring Boot API
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v
 AI API Provider
Depending on the requirements, the Spring Boot application could be deployed using services such as:
  • Amazon EC2
  • AWS Elastic Beanstalk
  • Amazon ECS
  • Amazon EKS
  • AWS App Runner
For beginners, managed deployment options can reduce the amount of infrastructure that needs to be configured manually.

6. Containerizing the Spring Boot Application

One popular approach is to package the application as a Docker container.
A simple Dockerfile might look like:
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FROM eclipse-temurin:17-jre

WORKDIR /app

COPY target/email-writer-sb.jar app.jar

EXPOSE 8080

ENTRYPOINT ["java", "-jar", "app.jar"]
The application can then be built as a Docker image:
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docker build -t email-writer .
After that, the image can be pushed to a container registry and deployed to an AWS container service.
This approach provides consistency between development and production environments.

7. Managing Secrets on AWS

When deploying an application to AWS, storing API keys directly inside the Docker image is a bad practice.
Instead, sensitive configuration should be managed securely.
AWS provides services such as:
AWS Secrets Manager
It can be used to securely store credentials, API keys, database passwords, and other sensitive configuration.
The application can retrieve the required secret at runtime rather than storing it inside the source code.
This provides a much safer architecture than:
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Source Code
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+-- API Key ❌
A better approach is:
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Application
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v
AWS Secrets Manager
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v
Secret

8. Handling AI API Failures

AI APIs can sometimes return errors because of:
  • Rate limits
  • Temporary service failures
  • Invalid API requests
  • Authentication problems
  • Network failures
Therefore, the backend should not simply expose a generic HTTP 500 error to users.
For example, instead of showing:
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500 Internal Server Error
the application can return a more meaningful response:
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AI service is temporarily unavailable.
Please try again later.
Production applications should also implement appropriate logging and error handling.

9. Scalability

One advantage of deploying the backend to AWS is the ability to scale the application as traffic increases.
Imagine the application receives:
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10 users
↓
1 Spring Boot instance
Later:
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10,000 users
↓
Multiple application instances
A load balancer can distribute incoming requests across multiple instances.
A simplified architecture could look like:
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Users
|
v
 Load Balancer
/ | \
/ | \
v v v
App 1 App 2 App 3
\ | /
\ | /
AI API
This allows the application to handle increased traffic more effectively.

10. Observability

Deployment is not the end of the development process.
Once an application is running in production, developers need to know:
  • Is the application healthy?
  • How many requests are being received?
  • Are requests failing?
  • How long are API calls taking?
  • Are there resource problems?
AWS provides monitoring and logging capabilities through services such as Amazon CloudWatch.
For example, application logs can help identify an issue such as:
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Request received
↓
AI API request
↓
Rate limit exceeded
↓
HTTP 429
Without proper logging, debugging production issues becomes much harder.

11. Security Considerations

An AI-powered application should follow basic security practices.
Some important practices include:

Never expose API keys

Do not put API keys in:
  • Git repositories
  • Frontend JavaScript
  • Dockerfiles
  • Public configuration files

Validate user input

The backend should validate incoming requests before sending them to an AI service.

Configure CORS carefully

During development, developers may use:
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@CrossOrigin(origins = "*")
However, allowing every origin is generally not appropriate for a production application.
Production applications should restrict requests to trusted frontend domains.

Use HTTPS

Communication between the client and backend should use encrypted HTTPS connections.

12. From Student Project to Production Application

A project can start very simply:
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React
↓
Spring Boot
↓
AI API
But as the application grows, the architecture can evolve:
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AWS
|
Load Balancer
|
+----------+----------+
| |
Spring Boot Spring Boot
Instance 1 Instance 2
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+----------+----------+
|
AI Service
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CloudWatch
|
Secrets Manager
This demonstrates an important lesson:
Production architecture is an evolution of a working application.
You do not need to start with a complex architecture. Start with a working application, understand the bottlenecks, and introduce AWS services when they solve a real problem.

Conclusion

Building an AI-powered application is a combination of software development, API integration, security, and cloud engineering.
A Spring Boot application provides a strong backend foundation, while AWS provides services for deployment, monitoring, security, and scalability.
The journey can be approached step by step:
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Build locally
↓
Create REST API
↓
Integrate AI
↓
Externalize configuration
↓
Containerize
↓
Deploy to AWS
↓
Secure secrets
↓
Monitor application
↓
Scale when necessary
For developers learning both Java/Spring Boot and AWS, an AI-powered application is a great project because it combines backend development with real-world cloud concepts.
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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