Serverless Computing Explained
Despite the name, serverless computing still uses servers — you just don't manage them. Instead of provisioning machines, configuring operating systems, and handling scaling, you deploy code and let the cloud provider handle everything else. It's a fundamentally different model from traditional hosting.
What Serverless Actually Means
Serverless has four defining characteristics:
No server management. You never SSH into a machine or install software. The provider handles all infrastructure.
Automatic scaling. Your code scales from zero to thousands of concurrent executions without configuration. When traffic drops, it scales back down.
Pay per execution. You're billed for actual compute time, often in milliseconds. No traffic means no cost — unlike traditional servers that run (and charge) 24/7.
Event-driven execution. Functions run in response to events: HTTP requests, file uploads, queue messages, or schedules.
Major Serverless Platforms
The serverless landscape includes several major players:
- AWS Lambda — The original and most mature platform
- Google Cloud Functions — Google's equivalent
- Azure Functions — Microsoft's offering
- Cloudflare Workers — Runs at the edge, extremely fast cold starts
- Vercel/Netlify Functions — Developer-friendly, great for frontend projects
Each has different strengths, pricing models, and supported languages.
Serverless vs Traditional Hosting
Traditional server:
+ Predictable pricing at scale
+ Full control over environment
+ Long-running processes supported
- Always running (paying even when idle)
- You manage scaling
Serverless:
+ Scale to zero (no traffic = no cost)
+ Automatic scaling up
+ No infrastructure management
- Cold starts add latency
- Execution time limits (usually 15 min max)
- Stateless by design
When Serverless Fits
Serverless excels for variable workloads, event processing, and applications with unpredictable traffic. A side project that gets occasional visitors costs nearly nothing. An API that handles sporadic webhooks scales automatically.
It's less ideal for consistent high-traffic applications (where traditional servers may be cheaper), long-running processes, or workloads requiring persistent connections.
The key insight is that serverless isn't better or worse — it's different. Understanding when it fits helps you make the right architectural choice.