Differences between traditional server based computing and AWS Lambda are listed below:
Infrastructure Management: You must provision, configure, monitor, and maintain servers
Scaling: Manual or auto-scaling setup required; often over- or under-provisioned
Cost: Pay for server uptime, regardless of actual usage
Deployment: Set up full environment (OS, app server, etc.) and deploy manually
Startup Time: Persistent, always-on servers
State Management: Can maintain state in memory or disk (session-based apps)
Infrastructure Management: No server management – fully abstracted by AWS
Scaling: Automatic, seamless scaling per request
Cost: Pay per use (invocation & compute time)
Deployment: Upload function code; very quick
Startup Time: Cold start (some delay possible), then fast
State Management: Stateless functions – externalize state (e.g., DynamoDB)
0-2 years experience
Use Case Fit: Long-running processes, complex backends, stateful apps
DevOps Overhead: High – patching, scaling, uptime, logging, etc.
Security Patching: You’re responsible for OS and runtime patching
Use Case Fit: Event-driven, short-lived tasks
DevOps Overhead: Low – no patching, no server config
Security Patching: Handled by AWS
2-5 years experience
5-8 years experience
8+ years experience
