Serverless Computing use cases

Serverless computing has moved from buzzword to backbone for modern app development By eliminating infrastructure management it enables teams to focus on business logic while providers handle scaling availability and resource allocation Let’s explore practical use cases where serverless shines with real code examples and architectural patterns

APIs and Microservices
Building RESTful APIs remains one of the most straightforward serverless applications A single Lambda function can handle HTTP routes via API Gateway while frameworks like Serverless Framework simplify deployment For high-traffic services automatic scaling ensures cost efficiency without overprovisioning

service: user-api
provider: aws
functions:
  getUser:
    handler: handler.getUser
    events:
      - http: GET /users/{id}
  createUser:
    handler: handler.createUser
    events:
      - http: POST /users

Stateless microservices benefit from isolated execution environments reducing blast radius for failures Tools like AWS Step Functions orchestrate complex workflows between functions while maintaining loose coupling

Event-Driven File Processing
Object storage triggers enable serverless pipelines for media processing data validation or format conversion When a user uploads an image to S3 it can automatically resize via Lambda

import boto3
from PIL import Image

def lambda_handler(event, context):
    s3 = boto3.client('s3')
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']

    obj = s3.get_object(Bucket=bucket, Key=key)
    img = Image.open(obj['Body'])
    img.thumbnail((200, 200))

    thumb_key = f'thumbs/{key}'
    img.save(f'/tmp/{key}')
    s3.upload_file(f'/tmp/{key}', bucket, thumb_key)

This pattern works for CSV-to-JSON conversion virus scanning or metadata extraction without maintaining batch servers

Scheduled Cron Jobs
Replace traditional cron servers with serverless functions for periodic tasks like database cleanup report generation or API health checks Cloud providers offer schedule event triggers with minute-level precision

// Azure Function timer trigger
module.exports = async function (context, myTimer) {
    context.log('Cleaning expired sessions...');
    await db.collection('sessions')
             .deleteMany({ expires: { $lt: new Date() } });
};

Cost savings come from paying only for execution time rather than 24/7 VM uptime

Chatbots and Voice Assistants
Serverless backends process natural language queries at scale for platforms like Slack or Alexa A single Lambda can handle intent recognition integrate with NLP services and fetch data from databases

def lambda_handler(event, context):
    intent = event['currentIntent']['name']
    slots = event['currentIntent']['slots']

    if intent == 'BookFlight':
        departure = slots['City']
        return {
            'dialogAction': {
                'type': 'Close',
                'fulfillmentState': 'Fulfilled',
                'message': {'contentType': 'PlainText', 
                            'content': f'Booking flight from {departure}'}
            }
        }

Stateless execution aligns with the sporadic nature of conversational interfaces

Data Streaming Pipelines
Process real-time analytics from Kinesis or Kafka using serverless functions Each batch of records triggers parallel processing for aggregation filtering or enrichment

// AWS Kinesis Lambda processor
public void handleRequest(KinesisEvent event, Context context) {
    event.getRecords().forEach(record -> {
        String payload = new String(record.getKinesis().getData().array());
        AnalyticsService.process(payload);
    });
}

This avoids managing consumer groups or scaling workers manually

Webhook Handlers
Third-party services often send webhooks for payment notifications CI/CD events or form submissions Serverless endpoints provide scalable receivers that validate and route payloads

// Netlify webhook handler
func HandleRequest(ctx context.Context, event events.APIGatewayRequest) (events.APIGatewayResponse, error) {
    var payload WebhookPayload
    json.Unmarshal([]byte(event.Body), &payload)

    if payload.Event == "deploy_succeeded" {
        slack.SendAlert("Deploy completed")
    }
    return events.APIGatewayResponse{StatusCode: 200}, nil
}

Built-in auto-retry and dead-letter queues improve reliability

When to Think Twice
While serverless excels in episodic workloads long-running tasks (over 15 minutes in AWS) or high-memory processing may require traditional VMs Database connections also pose challenges due to cold starts which connection pooling patterns can mitigate

Key Takeaways
- Use serverless for sporadic bursty workloads like APIs file ops or cron jobs
- Avoid for long-running processes or apps requiring persistent TCP connections
- Combine with managed services (DBs queues auth) to minimize operational overhead
- Monitor cold start latency and optimize with provisioned concurrency where needed

Serverless isn’t a silver bullet but a strategic tool By aligning its strengths with specific use cases teams achieve faster iteration lower costs and inherent scalability The next time you design a feature ask: "Would this benefit from event-driven granular scaling?" The answer might surprise you