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How does AWS Lambda differ from traditional server-based computing?

Differences between traditional server based computing and AWS Lambda are listed below:

Traditional Server-Based Computing:
  1. 1

    Infrastructure Management: You must provision, configure, monitor, and maintain servers

  2. 2

    Scaling: Manual or auto-scaling setup required; often over- or under-provisioned

  3. 3

    Cost: Pay for server uptime, regardless of actual usage

  4. 4

    Deployment: Set up full environment (OS, app server, etc.) and deploy manually

  5. 5

    Startup Time: Persistent, always-on servers

  6. 6

    State Management: Can maintain state in memory or disk (session-based apps)

  7. 7

    Use Case Fit: Long-running processes, complex backends, stateful apps

  8. 8

    DevOps Overhead: High – patching, scaling, uptime, logging, etc.

  9. 9

    Security Patching: You’re responsible for OS and runtime patching

AWS Lambda serverless computing:
  1. 1

    Infrastructure Management: No server management – fully abstracted by AWS

  2. 2

    Scaling: Automatic, seamless scaling per request

  3. 3

    Cost: Pay per use (invocation & compute time)

  4. 4

    Deployment: Upload function code; very quick

  5. 5

    Startup Time: Cold start (some delay possible), then fast

  6. 6

    State Management: Stateless functions – externalize state (e.g., DynamoDB)

  7. 7

    Use Case Fit: Event-driven, short-lived tasks

  8. 8

    DevOps Overhead: Low – no patching, no server config

  9. 9

    Security Patching: Handled by AWS