A cloud computing model where you write code without managing servers. The cloud provider handles infrastructure automatically.
Serverless computing lets you run code without managing servers. You write functions, deploy them, and the cloud provider handles everything else - servers, scaling, maintenance.
The name is misleading - servers still exist, you just do not manage them.
You write a function, deploy it to a serverless platform. When an event triggers the function (HTTP request, file upload, scheduled task), the platform runs it. You pay only for execution time.
Traditional: Rent server 24/7, pay whether used or not.
Serverless: Pay only when code runs. Idle time costs nothing.
AWS Lambda: Most popular, mature ecosystem, integrates with AWS services.
Google Cloud Functions: Simple, good for light workloads.
Azure Functions: Microsoft offering, integrates with Azure.
Vercel: Focused on frontend deployments, excellent DX.
Netlify Functions: Simple setup, great for static sites.
APIs: Build REST APIs without managing servers.
Background Jobs: Process images, send emails, generate reports.
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Webhooks: Handle incoming webhooks from third-party services.
Scheduled Tasks: Run cron jobs without maintaining servers.
Data Processing: Process S3 uploads, transform data streams.
No Server Management: Focus on code, not infrastructure.
Auto-Scaling: Handles 1 request or 1 million automatically.
Pay Per Use: Only pay for actual execution time.
Fast Deployment: Deploy functions in seconds.
High Availability: Built-in redundancy across multiple zones.
Cold Starts: First request after idle period takes longer (100-1000ms).
Execution Time Limits: Functions timeout after 5-15 minutes depending on platform.
Stateless: Cannot store data between invocations. Use databases or storage services.
Vendor Lock-in: Migrating between platforms requires code changes.
Debugging: Harder than traditional servers. Limited logging and monitoring.
Good For: Event-driven tasks, APIs with variable traffic, background processing, webhooks, scheduled jobs.
Not Good For: Long-running processes, high-frequency operations (cold starts hurt), applications needing persistent connections.
Small Scale: Serverless often cheaper (free tier covers most hobby projects).
Medium Scale: Depends on usage patterns. Calculate carefully.
Large Scale: Can become expensive. Reserved capacity or dedicated servers might be cheaper.
Netflix: Uses Lambda for video encoding and validation.
Coca-Cola: Serverless vending machines report inventory via Lambda functions.
Startups: Many start fully serverless for cost savings and simplicity.
Serverless: Zero infrastructure, auto-scaling, pay per use, limited control.
Containers: More control, predictable costs, no cold starts, require management.
Many companies use both - serverless for event-driven tasks, containers for core services.
Serverless is not a replacement for traditional servers. It is a tool for specific use cases. Use it where it fits - APIs, background jobs, webhooks. Avoid it where it does not - long-running processes, high-frequency tasks.
The serverless revolution is real, but not everything should be serverless. Choose based on your specific requirements.
